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Blockchain-based decentralized cryptocurrencies have drawn much attention and been widely-deployed in recent years. Bitcoin, the first application of blockchain, achieves great success and promotes more development in this field. However, Bitcoin encounters performance problems of low throughput and high transaction latency. Other cryptocurrencies based on proof-of-work also inherit the flaws, leading to more concerns about the scalability of blockchain. This paper attempts to cover the existing scaling solutions for blockchain and classify them by level. In addition, we make comparisons between different methods and list some potential directions for solving the scalability problem of blockchain.
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Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier 10.1109/ACCESS.2017.DOI
Solutions to Scalability of Blockchain: A
School of Data and Computer Science, Sun Yat-Sen University, 510006, Guangzhou, China
National Engineering Research Center of Digital Life, Sun Yat-sen University, Guangzhou, China
Corresponding author: Huawei Huang, and Zibin Zheng. (e-mail: {huanghw28, zhzibin}
The work described in this paper was supported by the National Key Research and Development Program (2016YFB1000101), the
National Natural Science Foundation of China (61902445, 61722214) and the Guangdong Province Universities and Colleges Pearl River
Scholar Funded Scheme (2016).
ABSTRACT Blockchain-based decentralized cryptocurrencies have drawn much attention and been
widely-deployed in recent years. Bitcoin, the first application of blockchain, achieves great success and
promotes more development in this field. However, Bitcoin encounters performance problems of low
throughput and high transaction latency. Other cryptocurrencies based on proof-of-work also inherit the
flaws, leading to more concerns about the scalability of blockchain. This paper attempts to cover the existing
scaling solutions for blockchain and classify them by level. In addition, we make comparisons between
different methods and list some potential directions for solving the scalability problem of blockchain.
INDEX TERMS Blockchain, Scalability
Blockchain as an emerging technology to realizing the dis-
tributed ledgers has attracted extensive research attention
recently. Such a ledger intends to achieve decentralized trans-
action management, which means that any node joining the
ledger can initiate transactions equally according to rules, and
the transaction does not need to be managed by any third
party. All transactions in the system are stored in blocks,
which are then linked as a chain and organized in chronolog-
ical order. Moreover, transactions that have written in blocks
are immutable and transparent to all peers. With all these
attractive characteristics, blockchain is drastically different
from the traditional centralized trust entities and becomes
a significant enabler to future financial systems. In recent
years, the blockchain has developed rapidly, from Bitcoin
[1], the first decentralized cryptocurrency, to Ethereum [2]
with smart contracts, followed by the emerging permissioned
blockchain (e.g. Hyperledger fabric [3]). Because of the wide
adoption of Blockchain, blockchain based applications have
been getting involved in our daily lives.
When the number of users of blockchain systems increases
extensively, the scalability issues of major public-chain [4]
platforms (e.g. Bitcoin and Ethereum) have arisen and greatly
affected the development of blockchain.
Transaction throughput and transaction confirmation la-
tency are two most talked-about performance metrics of
blockchain and both of them have not reached a satisfactory
level in recent popular blockchain systems [5], which leads
to the bad user’s quality of experience. However, compared
with the centralized payment system like banks, these two
metrics cannot be improved easily in blockchain, a self-
regulating system, that needs more considerations in order
to maintain decentralization. After numerous studies on the
particularities of blockchain, some researchers raise the view
of Blockchain Trilemma [6]. Similar to the CAP theory [7] in
the traditional field of the distributed system, the Blockchain
Trilemma points out that three important properties of a
blockchain system, involving decentralization, security, and
scalability, cannot perfectly co-exist. For instance, consid-
ering a simplified circumstance, adding a centralized coor-
dinator into the system can reduce the consumption (e.g.
computational resources consumed by proof-of-work [8])
for all users in the system to reach consensus on a set of
transactions. Another example, shortening the block interval
of Bitcoin can increase the transaction throughput but also
affects the security of the whole system because of the
increasing probability of fork. Therefore, balancing or even
achieve these three aspects of blockchain system well is
essential for the future development of blockchain that is
suitable for more complex and larger-scale scenes in our daily
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FIGURE 1. Energy Consumption by Country and Bitcoin: the number above
the bar indicates the energy consumption and Bitcoin consumes 73.12 TWh
per year, which ranks the 40th among all countries.) Source:
In order to improve the scalability of the blockchain,
many companies and research teams have proposed a large
number of different solutions. We classify them according
to the hierarchical structure of blockchain. In detail, the
hierarchical structure mainly includes two layers, which are
described briefly as follows. Layer1 concentrates on the on-
chain design of blockchain including the structure of blocks,
consensus algorithm and also the specific structure of the
main-chain. On the other hand, Layer2 focuses on off-chain
methods, which intends to reduce the burden of the main-
chain, such as executing some transactions off-chain and
moving some complex computational tasks to an off-chain
platform. Layer1 (on-chain) solutions such as Bitcoin-Cash
[9] increasing the block size, Compact block relay [10]
compressing the blocks, Sharding techniques [11], [12],
[13], [14], and various improved consensus algorithms [15],
[16], [17], [18], [19], in which the transaction throughput is
increased and transaction latency is decreased, respectively.
Layer2 solutions like payment channel (Bitcoin’s Lightning
network [20] and side chain (Plasma [21] of Ethereum)
are still under developing. The cross-chain solutions that
emerged in the last few years also play an important role
in Layer2 scaling solutions. One of the most representative
solutions is Cosmos [22], which aims to connect multiple in-
dependent blockchains to establish an integrated blockchain
network and achieve scalability. Although the existing solu-
tions somewhat improve the scalability, it should be noticed
that most of these solutions sacrifice the most fundamental
property of blockchain, i.e., decentralization, and also bring
new security issues. In summary, with both advantages and
limitations, those solutions are striving to achieve decentral-
ization, security, and scalability simultaneously.
The rapid development of blockchain technology has
drawn growing attention. However, its performance still
needs much improvement compared with the mainstream
payment processors such as Visa. Therefore, to accelerate
the wide adoption of blockchain technology, the scalability
issue requires many other more sophisticated solutions in the
FIGURE 2. Ethereum Pending Transactions Queue: The number of Ethereum
pending transactions in a certain period. Source:
In this paper, we intend to classify various existing scala-
bility solutions towards blockchain.
The rest of this paper is organized as follows. Section II
provides some concrete facts to briefly explain the perfor-
mance of several typical blockchains. Section III presents the
solutions to the scalability issue of blockchain proposed in
recent years. Then, section IV discusses some open issues
and future directions to scale blockchain. Finally, section V
summarizes this paper.
With the domination of Bitcoin in cryptocurrency, the scal-
ability issues of blockchain have been exposed, too. Kyle
Croman et al. [23] analyzed several key metrics to measure
the scalability of Bitcoin, including maximum throughput,
latency,bootstrap time and cost per confirmed transaction
(CPCT). The maximum throughput and latency are the two
most important performance metrics that have a significant
impact on the user’s quality of experience (QoE).
Among all metrics listed above, transaction throughput
receives the most attention. It has been reported that Bitcoin’s
highest transaction throughput is 7 TPS (transaction-per-
second) [24] while Visa can achieve more than 4000 TPS
[25] Obviously, low throughput of Bitcoin cannot satisfy the
large-scale trading scenarios.
In theory, transaction throughput is restrained by the block
interval and the block size. A larger block can store more
transactions, directly raising throughput, but it also causes
an increase in block propagation time. To ensure the current
block to be propagated to most peers in the whole net-
work before the next block is generated, which is critical
to reducing the probability of fork, the block size and the
average block interval between two successive blocks should
be well configured. In Bitcoin, the block interval is about
10 minutes, and the block size is around 1 MB [1], which
limits the number of transactions that can be stored in each
block. Thus, to maintain the block propagation time while
increasing the block size, the average bandwidth of the whole
system that determines the block propagation time becomes
a performance bottleneck of the blockchain system.
Another metric, transaction confirmation latency that is
the time for a transaction to be confirmed, also has a strong
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Layer Categories Solutions
Payment channel Lightning Network [20], DMC [26]
Non On-Chain
Raiden Network [27], Sprites [28]
Side chain Pegged Sidechain [29], Plasma [21] [30]
Cross-chain Cosmos [22], Polkadot [31]
Off-chain computation Truebit [32], Arbitrum [33]
Block data
SegWit [34], Bitcoin-Cash [9]
Compact block relay [10], Txilm [35]
CUB [36], Jidar [37]
Consensus Bitcoin-NG [15], Algorand [16]
Snow white [17],Ouroboros [18] [19]
Sharding Elastico [11], OmniLedger [12]
RapidChain [13],Monoxide [14]
Inclusive [38], SPECTRE [39]
PHANTOM [40], Conflux [41]
Dagcoin [42], IOTA [43]
Byteball [44], Nano [45]
Layer0 Data propagation Erlay [46], Kadcast [47]
Velocity [48],bloXroute [49]
TABLE 1. Taxonomy of the scalability solutions in different layers
relation with user experience.
Due to the huge volume of Bitcoin transactions nowadays,
the limited size of blocks is far from enough to deliver
all transactions submitted by nodes. Under such a situa-
tion, miners tend to select transactions that are with high
transaction fees. As a result, the transactions that are with
a low bid have to wait until packaged, which leads to the
longer transaction latency [50]. Ethereum, another popular
PoW-featured blockchain (in its pre-2.0 version) makes this
problem even severe since some popular decentralized ap-
plications (DApps) [51] in Ethereum have induced extensive
congestion in the entire network. As we can see in Figure
2, the total number of Ethereum transactions waiting to be
confirmed in a certain period maintains a high level.
Besides the performance bottleneck of blockchain, we
should also consider the capacity problem of blockchain
seriously. As the scale of a blockchain growing rapidly, the
storage required by all blocks grows accordingly. Therefore,
the full nodes, which store all block data of the network,
are required large storage capacity for each. Similarly, the
Bootstrap time will increases linearly as the blockchain his-
tory grows, slowing down the process of new nodes joining
into the system. All these restrictions degrade the availability
and decentralization of a blockchain, and thus should be
examined closely when developing a large-scale blockchain.
Nowadays, more block compression methods have been pro-
posed to reduce redundant data of blocks, which is beneficial
for easing the capacity problem. At the same time, sharding
techniques, partitioning the whole blockchain network into
different shards, have been researched more detailed to solve
the capacity problem of blockchain.
Meanwhile, many concerns have been raised about the
energy consumption of Proof-of-work based blockchain sys-
tems, such as Bitcoin and Ethereum [52]. Miners in a PoW-
featured blockchain are always competing with each other
through calculating, which results in a large dissipation of
electricity. Figure 1 shows the energy consumption of Bitcoin
comparing with that of some countries/states, where we can
find that the entire Bitcoin network consumes even more
energy than many countries, such as Austria and Colombia,
and barely ranks the 40th. Although PoW works securely,
it’s far from green enough to be a sustainable consensus
mechanism for future blockchain.
Striving to improve the scalability of blockchains while
maintaining security and decentralization, many existing ap-
proaches have been proposed by literature. We will review
some mainstream solutions in the next section.
By Table 1, we classify the existing popular solutions of solv-
ing the scalability of blockchains into three layers: Layer1
Layer2, and Layer0.
Layer1 focuses on consensus, network and data structure
of blockchain, all of which are executed on-chain. In con-
trast, Layer2 seeks the opportunity to scale out blockchain
by off-chain methods such as off-chain channel [20], [27],
side-chain [21], [30] and cross-chain protocols [22], [31].
Besides, we also present a table 2 which shows the data of
Transaction Per Second (TPS) and confirmation time of some
representative scaling solutions.
In the subsequent parts of this section, we elaborate on
these existing state-of-the-art solutions dedicated to improv-
ing the scalability of blockchains.
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Project Technology TPS (tx/sec) Confirmation time
Ouroboros [18] PoS 257.6 2 min
ByzCoin [25] PBFT 1000 15-20 sec
Algorand [16] Byzantine agreement 875+ 22 sec
RapidChain [13] Sharding 7,380 8.7 sec
Monoxide [14] Sharding 11,694 13-21 sec
Conflux [41] DAG 6,400 4.5-7.4 min
TABLE 2. Comparsion of Transaction per second (TPS) and Confirmation time among different solutions
1) Solutions related to Block Data
As discussed in Section2, the scalability problem has a cer-
tain relevance with block size. Obviously, increasing block
size enables a block to include more transactions. Block
compression can achieve the same effect and also reduce
storage overhead. And, some other solutions explore methods
to achieve data reduction are also proposed. In this section,
we will introduce some approaches focused on these ideas.
Segregated Witness: The Segregated Witness (SegWit)
[34] defined in BIP141 [53] is designed to prevent non-
intentional Bitcoin transaction malleability and to alleviate
blockchain size-limitation that reduces the transaction speed
of Bitcoin. It achieves the goals by splitting the transaction
into two segments, removing the unlocking signatures from
the original transaction hashes, and the new Witness structure
will contain both the scripts and signatures.
SegWit also defines a new way to calculate the maximum
block-size by assigning a weight for each block. The new
calculation is shown as follows.
BW = 3 ×BS +T S,
where BW is new defined Block weight and BS is Base
size including the size of the original transaction serializa-
tion without any witness-related data. TS stands for Total
size, which is the size of transaction serialization described
in BIP144 [53] Block weight is limited under 4 MB, and
theoretically allowing more transactions can be accommo-
dated in one block, which slightly increases the scalability
performance of blockchain.
An additional design of SegWit is to provide convenience
for deploying Lightning Networks [20], which will be intro-
duced in the Part(B) of this section.
Bitcoin-Cash: In 2017, because of the scalability problem,
Bitcoin experienced a hard fork [54] and was split into two
blockchain branches, i.e., Bitcoin and Bitcoin-Cash. Bitcoin-
Cash has increased its block size to 8MB, which is much
larger than the size of its previous version (only 1MB in size).
After that, Bitcoin-Cash was upgraded further, to expand the
block size up to 32MB. The average block interval of Bitcoin-
Cash is still maintained at the original 10 minutes. In theory,
the transaction throughput can be greatly increased. This has
been verified in the stress test conducted in September 2018.
From the theoretical and practical points of view, im-
proving the block size can scale-out the blockchain capacity
directly. However, the infinite expansion enlarges the size of
each block, which cannot be transferred easily due to the
limitation of intra-blockchain bandwidth. Thus, only increas-
ing the block size is not a sustainable solution. Some other
studies [55], [56] also claim that larger blocks may lead to
the problem of centralization since individual users in the
network are not able to propagate blocks efficiently and also
have difficulty in verifying a large number of transactions
within a given interval. This will result in that only a cen-
tralized organization can act as a full node.
Block compression: To improve the throughput of
blockchains, various solutions related to block compression
have been proposed (e.g. Compact block relay [10]and Txilm
[35]). All these methods share a similar idea that is to reduce
some redundant data of a block that has been already stored
in the Mempool of receivers.
Compact block relay was proposed in BIP152 [10] and
altered the data structure of origin blocks in Bitcoin. A com-
pact block contains the header of the block and some short
transaction IDs (TXIDs) which will be used for matching
transactions that are already available to the receivers.
Figure 3 shows the workflow of this protocol. BIP152
provides two modes for block relay. The essential part of
the protocol is sending cmpctblock messages and receivers
dealing with the messages. Node Asend a compact block to
Node B. The moment Node Breceives the block, Node B
should calculate TXIDs of the transactions in their Mempool
and match each of them with TXIDs stored in the compact
block. Then, if all unconfirmed transactions are available
to Node B, the full block can be reconstructed. Otherwise,
Node Bshould send a getblocktxn message to require the
information of transactions they do not have and reconstruct
the block after they receive all the data they need. The main
difference between the provided two modes is that, in Low
Bandwidth Relaying, the compact blocks are sent only if the
receivers make requests.
Txilm is a protocol based on BIP152 [10] that compresses
transactions in each block to save the bandwidth of the
network. Txilm utilizes a short hash of TXID to repre-
sent a transaction, which achieves a greater result on block
compression. However, hash collisions are more likely to
occur when a short hash is used. Therefore, Txilm optimizes
the protocol using sorted transactions based on TXIDs to
reduce the probability of hash collision and prevent the sys-
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Node A Node B
Node A Node B
headers or inv
High Bandwidth Relaying Low Bandwidth Relaying
FIGURE 3. Procedures of two modes of Compact block relay
tem from Collision attack by adding "SALT" (e.g. CRC32-
Merkle root) when computing the hash of TXIDs. Based
on the protocol, 80 times of data reduction is realized in
their simulations and thus increases the throughput of the
Some other approaches [57] [58], concerned with data of
the block is also proposed in recent years. All existing solu-
tions make some contributions to increasing the transaction
throughput of blockchain but demand more optimization to
scale the blockchain system.
Storage Scheme Optimization: Apart from block com-
pression, there are some other solutions to reduce the storage
pressure of each user.
CUB [36] proposes a scheme that assigns different nodes
into a Consensus Unit. In each unit, each node stores part of
the block data. The blocks of the whole chain are assigned
to nodes in the unit to minimize the total query cost. They
name this process as block assignment problem and propose
algorithms to solve it, which reduces the storage overhead of
each node while ensuring the throughput and latency.
Jidar [37] is a data reduction approach for Bitcoin system.
The main idea of Jidar is to allow users only store relevant
data they are interested in and thus releases the storage pres-
sure of each node. When a new block is created, each node
stores only a small part of the total data of a block, including
relevant transactions and Merkle branch. Jidar adopts bloom
filter to validate if the input of a transaction has been spent.
Besides, if some users want to get all block data of the
system, they can ask other nodes for data and cohere all frag-
ments into complete blocks. However, incentive mechanisms
are required to support this function.
2) Different Consensus Strategies
We then review different consensus strategies of blockchain
and some optimizations proposed to improve the scalability
of blockchain.
PoW (Proof of Work): Bitcoin, proposed in 2008,
adopted the PoW to achieve consensus in a decentralized
network [1]. Under PoW, participants, also called miners,
need to solve a computational task in order to generate a
new block. When the answer is found, the miner broadcasts a
relevant message to the network for other miners to verify the
new block. If the block is validated, it can be added into the
chain and the miner who generates it will be rewarded with
tokens such as bitcoins. PoW is a novel consensus and has
been exploited by a large number of blockchains. However,
since Bitcoin has the risk to suffer from forks, transaction
confirmation time is set to around one hour (after 6 blocks
being mined). Even worse, the calculation in PoW has led to
too much resource dissipation.
Therefore, some other studies [15], [39], [59] were ded-
icated to improving the original PoW mechanism. For ex-
ample, Bitcoin-NG [15] is a blockchain protocol based on
Nakamoto consensus [1]. It divides time into epochs, and
each epoch has a single leader responsible for transaction
serialization. In order to support this mechanism, Bitcoin-NG
introduces two types of blocks: key block and microblock.
The key block, generated by the miners through the PoW
mechanism, does not contain transaction data and is only
used for the election of the leader. And, the leader is allowed
to generate the microblock which contains the packaged
transaction data. Thus, transactions can be processed contin-
ually until the next leader is elected that significantly reduces
transaction confirmation time and improves the scalability.
GHOST [59] also builds upon PoW and re-organizes the
data structure of Bitcoin to eliminate the security concern of
double-spending [8] attacks, spending the same asset more
than once, caused by network delay. SPECTRE [39] is a
PoW-based protocol that utilizes the structure called direct
acyclic graph (DAG) to improve the transaction throughput
and reduce the confirmation time of Bitcoin.
PoS (Proof of Stake): PoS is an alternative mechanism
that avoids the computational overhead of PoW. Instead
of consuming computational resources to get involved in
generating blocks, participants in PoS vote leaders by their
investment in a blockchain system and thus reduce the con-
firmation time of transactions. The basic idea of PoS is that
nodes with more currencies in the system are less likely to
do harm to the system. However, because of the elimination
of computational verification, to ensure the security of a PoS
protocol is a challenging task. Many secure PoS protocols
have been proposed. For example, Ouroboros [18] uses a
coin-flipping protocol to elect leaders for the current epoch
and seed for the next epoch. Participants in Ouroboros Praos
[19] utilize a verifiable random function to generate a random
number, which will be used to determine whether a partic-
ipant can be elected as a leader. Snow-white [17] exploits
a random oracle to elect a leader. Furthermore, Ethereum
Casper [60] is planned to release in 2020, which is equipped
with a PoS protocol and is expected to improve the scalability
of Ethereum.
DPoS (Delegated Proof of Stake): DPoS [61] is a new
consensus protocol for blockchain and its principle is dif-
ferent from PoS. In DPoS, stakeholders elect a small group
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of delegates to be responsible for producing as well as
validating blocks. DPoS has been adopted as the consen-
sus algorithm for Bitshare [62] and EOS [63] to solve the
problem of scalability. This algorithm is divided into two
stages. The first stage is the staked voting, in which the nodes
holding tokens can vote for the potential block producers, and
finally, 21 producers with most votes are selected to create
the next block. The idea is to let the token holders in the
network vote for producers who can provide great computing
power and indirectly vote the malicious nodes. A block is
broadcast to other producers to be verified and if more than
15 block producers verify and sign, the block is confirmed.
Such voting is continually performed throughout the system
to select the producers, but if a selected block producer does
not produce a block within 24 hours, it will be replaced by
a spare producer. At the same time, the probability of this
producer to be selected in the future will be reduced as well
because of its previous failures.
In EOS, a block is generated by one producer every three
seconds on average, and the average confirmation time for
each transaction is about 1.5 seconds. Compared with other
mainstream blockchain platforms, EOS can reach an over-
whelming million-level TPS. However, its decentralization
has been questioned. It is believed that more than 50% of
the coins in EOS are occupied by only ten addresses, and
less than 1% of EOS addresses hold more than 86% tokens
of EOS [64]. The DPoS applied by EOS actually chooses
the super node that holds the most resources, resulting in the
rights are in the hands of a small number of nodes, which is
essentially viewed as a centralized mode.
PBFT (Practical Byzantine Fault Tolerance): PBFT
[65] is a replication algorithm that is able to tolerate the
Byzantine faults [66], consistency problems caused by un-
reliable components or nodes in the system, in asynchronous
systems and performs more efficiently than early approaches
[67]–[69] . In every view of PBFT, a primary server is
selected to be responsible to order messages. When primary
receives a client request, a three-phase protocol begins work-
ing, including pre-prepare,prepare,commit phases. In the
pre-prepare phase, primary broadcasts the pre-prepare mes-
sages in an ordered sequence to other replicas. In the prepare
phase, each server makes a choice to accept the pre-prepare
message or not. If accepted, the server broadcasts a prepare
message to all other replicas. When it successfully collect
2f+1 feedback messages (findicates the number of faulty
nodes), it starts the commit phase. Similar to the prepare
phase, each server broadcasts commit messages to others and
waits for 2f+1 feedback messages from other replicas which
indicates that a majority of servers agree to accept the client’s
request and send a reply to the client.
In contrast to PoW, PBFT works without computational
tasks. It thus reduces the complexity of consensus to the
polynomial level but requires more communication over-
head. Some follow-up works build their consensus protocols
based on PBFT and make some modifications. For example,
Tendermint [70] uses validators with voting power to vote
for each round and reach consensus finally. Elastico [11]
is a sharding protocol that chooses PBFT as the consensus
for each committee of Elastico to agree on a single set of
Hybrid Consensus: Hybrid Consensus is a protocol that
combines some classical consensus protocols. ByzCoin [25]
proposes a two-phase protocol based on the idea of Bitcoin-
NG [15]. However, it is able to ensure strong consistency
by combining PoW and PBFT. In addition, ByzCoin uses
a collective signing protocol called Cosi [71] to reduce the
cost of the prepare and commit phases of PBFT and scale it
to large consensus groups. Later works such as Hybrid con-
sensus [72], Solidus [73] also propose to combine different
protocols with PoW aiming to improve on the throughput and
Algorand [16] is a cryptocurrency based on a Byzantine
Agreement (BA) protocol. By combining with Verifiable
Random Functions [74], users are chosen to become a com-
mittee member to participate in the BA and reach consensus
on the next set of transactions. To mitigate targeted attacks,
the participant will be replaced after sending a message in
BA. With all these approaches, Algorand scales to 500,000
users in experiments and achieves high throughput.
Other Consensus: Some other new consensus algorithms
have been proposed in recent years, including PoA (proof-of-
authority) [75], PoC (proof of capacity) [76] and PoP (proof-
of-Participation) [77], which make some modifications of the
previous consensus to improve the scalability of blockchain.
PoP (Proof of Participation) is a new protocol that im-
plements PoS through the mining mechanism of PoW. PoP
selects a list of stakeholders to work out a computational
task, which is simpler than that in PoW, to generate a new
block. Other stakeholders who did not participate in the
mining validate the block and propagate it. Unlike PoS,
transaction fees in PoP are only distributed to stakehold-
ers participating in validation and propagation, which thus
encourages stakeholders to maintain an online node and
sustain the system. PoP includes two layers of security, proof-
of-work, and proof-of-stake, that protect the system from
security problems (e.g. double-spending) and also consume
less energy than the traditional PoW mechanism.
PoC (Proof of Capacity) is a consensus algorithm that uti-
lizes the storage resource (disk space) to mine. Miners in PoC
based system stores a list of possible answers before mining.
Larger space indicates a higher possibility of generating the
next block and getting the reward. PoC is similar to PoW
but reduces energy consumption by complex computational
PoA (Proof of Authority) is a modified form of PoS where
a block validator’s identity plays the role of stake and relies
on a set of selected validators to reach consensus. Since a new
block is validated by authorized nodes, a small part of nodes
in the network, the speed of validating processes is highly
increased. PoA is suitable for permissioned blockchain where
nodes’ identities are authorized and increases the perfor-
mance in terms of the TPS.
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Shard 1
(b) Transaction sharding
Shard 2 Shard 3
(a) State sharding
Peer node:
FIGURE 4. Illustration of Sharding. The initial network contains eight nodes
(blue circle). After (a), nodes are assigned to different shards. (b) Transactions
are distributed to different shards and be processed in parallel.
3) Sharding
Sharding [78] is a traditional technology first proposed in the
database field mainly for the optimization of large commer-
cial databases. This method is to divide the data of a large
database into a number of fragments, and then store them in
separate servers to reduce the pressure of a centralized server,
thereby improving the search performance and enlarging the
storage capacity) of the entire database system.
The basic idea of sharding technology is divide-
and-conquer. Therefore, applying sharding technology to
blockchain is to divide a blockchain network into several
smaller networks, each contains a part of nodes, which is
called a “shard". Transactions in the network will be pro-
cessed in different shards, so that each node only needs to
process a small part of arriving transactions. Different shards
can process transactions in parallel, which can boost the
concurrency of transaction processing and verification, thus
increasing the throughput of the entire network. While parti-
tioning the whole system into different shards, it is critical
to protect the decentralization and security of the system.
Several aspects required to particularly take into account:
(a) How to reach a consensus in each shard and prevent
each shard from suffering some common risks such as 51%
vulnerability and Double-spending. (b) How to handle cross-
shard transactions quickly while ensuring the consistency of
these transactions.
Figure 4 shows an example of sharding architecture, where
the blockchain network is divided into 3 shards, including
three procedures:
At first, peers in the network are assigned to different
shards. In order to reduce the storage overhead of each
node, State sharding enables nodes in each shard only
need to store the state of their own shard.
Transaction sharding distributes transactions to differ-
ent shards and allow transactions to be processed in par-
allel. Apart from transactions executed within a single
shard, cross-shard transactions are very common in a
large system. Therefore, the system should be equipped
with some protocols to deal with cross-shard transac-
Main chain
Shard 1
Shard 3
State commit
Shard 2
FIGURE 5. Architecture of the Sharding protocol with a Main chain
tions carefully and efficiently.
As cross-shard transactions require more communication
costs and also increase the confirmation latency, transactions
in a sharding-based system should be placed into shards
more carefully based on some partitioning rules. This pro-
cess should consider different factors including the balance
among different shards, the possible number of cross-shard
transactions and the total amount of data that would be real-
located when rescheduling shards [79]. Some classical graph
partitioning algorithms can be adopted, such as Kernighan-
Lin algorithm [80] and METIS [81]. Hashing is another
straightforward approach that uses the hash result of the
unique id of each account as the id of the selected shard.
Some other solutions proposed a new structure consisting
of a main chain and multiple shard chains. Each shard main-
tains a shard chain and commits its state to the main chain
periodically. From the architecture shown in Figure 5, we
can see that each shard has a dedicated chain. Under this
kind of architecture, cross-shard transactions are processed
through the main chain by admitting the receipts of cross-
shard transactions committed by different shards, which can
be validated by all shards to ensure the correctness of cross-
shard transactions. However, when the scale of cross-shard
transactions increases in a blockchain system, the main chain
will become the bottleneck of the holistic system since the
large volume of transactions brings great pressure of both
storage and communications.
We also find that several existing works [11]–[14] have
exploited various methods to optimize their systems based on
the sharding technology. Each of those representative works
is reviewed as follows.
Elastico: Elastico [11] is the first sharding protocol for
the permission-less blockchain. In each consensus epoch of
Elatico, participants need to solve a PoW puzzle, which
will be used to determine the consensus committee. Every
committee works as a shard and runs PBFT [65] to reach
the consensus and the result will be committed to a leader
committee, which is responsible for generating the final
decisions on the consensus results of other shards. Finally, the
final value will be sent back to update other shards. However,
there are several drawbacks of Elastico:
Elastico generates identities and committees in each
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epoch. Such frequent operation potentially degrades the
efficiency of transaction execution.
Although each node only needs to verify transactions
within its own shard, each node is still required to store
all data of the entire network.
Elastico requires a small size to limit the overhead of
running PBFT in each committee, leading to a high
failure probability while only tolerating up to a 1/4
fraction faulty nodes.
Elastico fails to ensure the cross-shard transaction atom-
OmniLedger: OmniLedger [12], a more recent distributed
ledger based on Sharding technique, builds closely on Elas-
tico [11] and tries to solve the problems of Elastico. It uses
a bias-resistant public-randomness protocol for shard assign-
ment, which combines RandHound [82] with Algorand [16].
To guarantee the atomicity of cross-shard transactions, Om-
niLedger introduces a two-phase client-driven “lock/unlock"
protocol called Atomix. OmniLedger also adopts the data
structure blockDAG [38] to make block commitment par-
allelly and increase transaction throughput via Trust-but-
Verify Validation. However, the following issues still remain
unsolved in OmniLedger:
Similar to Elastico, OmniLedger is also resilient to
Byzantine faults only up to a 1/4 fraction.
Users in OmniLedger are required to participate actively
in cross-shard transactions, which is very difficult to
satisfy light-weight users [83]
RapidChain: RapidChain [13] is a sharding-based public
blockchain protocol that is resilient to Byzantine faults up
to a 1/3 fraction of the participants, which is better than the
1/4 fraction of OmniLedger [12]. RapidChain reveals that the
communication overhead per transaction is a major bottle-
neck to the transaction throughput and latency in previous
sharding-based protocols [11], [12]. Therefore, Rapidchain
reduces the amount of data exchange per transaction and does
not need to gossip transactions to the entire network because
of the usage of a fast cross-shard verification technique.
Additionally, RapidChain utilizes block pipelining to reach
a further improvement of throughput and ensures robustness
via a reconfiguration mechanism.
Monoxide: Monoxide [14] is a scale-out blockchain that
proposes Asynchronous Consensus Zones and scales the
blockchain linearly while considerably maintaining decen-
tralization and security of the system.
The entire network of Monoxide is divided into different
parallel zones, each of which only needs to be responsible for
itself since blocks and transactions are zone-specific and are
only stored in their own zone. Handling transactions across
shards (i.e., zones) is an essential issue in sharding-based
blockchain systems. In Monoxide, eventual atomicity is pro-
posed to ensure the correctness of cross-zone transactions.
At the same time, Monoxide proposed an innovative Chu-ko-
nu Mining that magnifies the mining power, enabling miners
to create blocks in different zones via solving one PoW
Block or
FIGURE 6. An overview of DAG: Each rectangle in the graph represents a
block (or a transaction). Multiple blocks (or transactions) can be generated
concurrently by linking to previous blocks (or transactions) in DAG (i.e. three
orange arrows pointing to Aand two blue arrows pointing to D).
puzzle. Therefore, the difficulty of attacking a single zone is
as difficult as attacking the entire network. This characteristic
ensures the security of a single zone.
Some other public blockchain projects, including Zilliqa
[84] and Harmony [85], also employed sharding technology
to solve the scalability of their systems. Zilliqa is the first
sharding-based public blockchain with PoW as the consensus
algorithm. Zilliqa improves the TPS via processing transac-
tions in different shards, but each node in Zilliqa still needs
to store the data of the whole network, which will hinder
the system to scale. Later, Harmony also adopts sharding
to build a scalable and provably secure public blockchain.
Harmony applies a structure with multiple Shard Chains,
which processes transactions and store data within the shard,
and a Beacon Chain that includes the block header from each
Shard Chain and generates random numbers needed in the
consensus. Besides, different from Zilliqa, Harmony divides
the storage of blockchain data into different shards and a node
in a shard only needs to store the data of its own shard.
At present, there are very few efficient sharding protocols
that highly guarantee decentralization, scalability, and secu-
rity. Thus, there remains a large research space for sharding
4) DAG (Directed Acyclic Graph)
The traditional blockchain stores transactions in blocks that
are organized in a single chain structure. With this kind of
structure, blocks cannot be generated concurrently and thus
limits the transaction throughput. In order to solve this prob-
lem, an idea dedicated to revising the structure of blockchain
called DAG [86] is proposed.
DAG is a finite directed graph with no directed cycles
commonly used in the computer science field. An obvious
way to transform blockchain into DAG is to let a block act as
a vertex in DAG and connect to some previous vertices. How-
ever, different from blockchain, DAG allows several vertices
to connect to a previous vertex which means concurrent block
generation and thus enables more transactions to be included
in the system.
Some representative proposals are briefly reviewed as fol-
lows. Y. Lewenberg et al. [38] utilize Directed Acyclic Graph
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Project structure consensus block total order
Inclusive [38] block DAG PoW No
SPECTRE [39] block DAG PoW No
PHANTOM [40] block DAG PoW Yes
Conflux [41] block DAG (with pivot chain) PoW (GHOST [11]) on a pivot chain Yes
IOTA [43] Tx DAG Cumulative weight of transactions No
Byteball [44] Tx DAG Relying on a reputable group called Witnesses Yes
Nano [38] Block-lattice Balance-weighted votes on conflicting transactions Yes
TABLE 3. Comparsion between different DAG-based solutions
of blocks (blockDAG) in their protocol. Different from the
traditional structure of blockchain, in this protocol, a new
block references multiple former blocks. An inclusive rule
is proposed to select a main chain of the formed DAG. More-
over, the contents of off-chain blocks that do not conflict with
previous blocks can also be included in the ledger. With the
proposed protocol, the system achieves higher throughput.
Later, another DAG-based blockchain called SPECTRE
[39] applies the DAG structure to represent an abstract vote to
specify the partial order between each pair of blocks, which
cannot be extended to a total order over all transactions.
PHANTOM [40] also applies blockDAG to achieve faster
block generation and higher transaction throughput. More-
over, PHANTOM proposes a greedy algorithm to order trans-
actions embedded in blockDAG and is able to support smart
Conflux [41] is a fast and scalable blockchain system
based on DAG. In Conflux, they proposed two different kinds
of edges between blocks (i.e. parent edges and reference
edges). A pivot chain formed by parent edges is selected
via a selection algorithm. Therefore, the consensus problem
of conflux is transformed to reach the consensus of a single
chain, which they adopt GHOST [59] to solve.
In industry, there are also several DAG-based projects. A
DAG-based cryptocurrency called Dagcoin [42] treats each
transaction as a block and focuses on faster security confir-
mations and greater throughput. Similar to Dagcoin, another
branch of studies aim to build DAG-based distributed ledgers,
such as IOTA [43], Byteball [44] and Nano [45].
Fantom [87] proposed the OPERA chain, a DAG con-
structed by event blocks, and a Main-chain to determine
the ordering between every block. Lachesis Consensus is
also provided to reach faster consensus via more efficient
In table 3, we make a comparison of selected proper-
ties (specific structure, consensus, whether ensuring total
block order) among some DAG-based protocols. As the table
shows, some of them aim at scaling the proof-of-work based
system using DAG. And, the specific structure of them also
has some differences between each other. Tx DAG stands
for a DAG structure that is formed by many independent
transactions that are not required to be packed into blocks.
Total block order is an essential property that determines the
order between every two blocks in the network and thus acts
as an important role for protecting the system from several
attacks (e.g. double-spending).
Tangle [88] is a DAG network under the basic idea of
IOTA. As Figure 6 shows, Tangle is extended by adding di-
rected edges between two transactions. Each edge represents
that a new transaction has approved a previous transaction. In
IOTA, there is no block, miner and transaction fee involved.
Every node can create transactions freely after solving a
specific computational task and choose two previous trans-
actions to validate and approve them if valid. Later analysis
[89], [90] also proves all these properties of Tangle. Besides,
algorithms have been proposed to mitigate a kind of double-
spending attack in Tangle called parasite chain attacks [91].
With such impressive merits, some other DAG cryptocur-
rency techniques have been proposed, like new randomized
gossip protocol for consensus of Hashgraph [92] and the
addition of DAG in Avalanche [93] to extend their consensus
protocols, continuously improving the development of DAG.
Compared with blockchain, DAG-based platforms adopt
a different ledger-structure and different transaction-
confirming methods. However, some questions about IOTA
are raised [94], focusing on the claimed great characteristics
that IOTA do not need transaction fees and maintains high
scalability. Meanwhile, treating each transaction as a block
requires more metadata (e.g. reference to other vertices in
DAG) and thus cannot be applied as an efficient method for
constructing a scalable system.
And, because of the consensus protocol utilized in some of
the current DAG-based ledgers, security issues (e.g. double-
spending [95]) and decentralization of these systems are con-
troversial, which will probably limit the further development
of DAG.
We then classify the Layer-2 approaches into the following
categories: Payment Channel,Sidechain,off-chain computa-
tion, and the cross-chain.
1) Payment Channel
The payment channel is a temporary off-chain trading chan-
nel, transferring some transactions to this channel to achieve
the effect of reducing the transaction volume of the main
chain while improving the transaction throughput of the
entire system. The representative payment channel solutions
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include Lightning network [20] adopted by Bitcoin, as well
as the Ethereum-based Raiden network [27].
Lightning Network [20]: In recent years, the number of
Bitcoin transactions has increased drastically, and its short-
comings have exposed, including high transaction delays and
expensive transaction fees. To alleviate those drawbacks of
the Bitcoin network, developers have proposed a new method
-lightning network.
To explain briefly, the basic idea of Lightning Network
is that two nodes in Bitcoin establish an off-chain trading
channel, in which they can carry out multiple low-latency
transactions. As shown in Figure 7, this solution includes
three phases, establishing the channel, trading, and closing
the channel. Before launching transactions, the two parties
first have saved a certain amount of tokens in the channel
as a deposit (greater than the total amount involved in the
subsequent transaction), which is the first transaction to open
the channel and is recorded on the Bitcoin main chain.
Both parties can then trade with each other in the channel
and if one of them cheats, all funds in this channel will be
sent to a counterparty as penalty. When closing the channel,
the amount of tokens on both sides is submitted to the
block of the main chain. Therefore, multiple transactions are
completed off-chain and the whole process produces only
two transaction records submitted to the main chain. This
approach greatly increases the number of transactions when
the block size is a constant.
Furthermore, it is not necessary to establish a payment
channel between every two parties who intend to exchange
tokens. A Payment Channel Network (PCN) is introduced
to conduct off-chain transactions between two parties that
have no direct payment channel established between them.
One participant to route to another via the path between them
and make indirect transactions. Figure 8 shows the routing
schematic diagram of the Lightning network. Node 0and
Node 9establish a payment channel and carry out transac-
tions directly. Node 1is able to send transactions to Node 3
via the two channels (i.e. Node 1to Node 2and Node 2to
Node 3). Similarly, Node 4and Node 8can trade with each
other indirectly. Since transactions can only be sent through
a route connected by different payment channels, a proper
routing mechanism is needed to ensure the availability of
Lightning Network, which has not been developed perfectly.
Companies like Lightning Labs [96] implement protocols to
build Lightning Network and help users make transactions
Lightning Network provides instant and low-cost payment.
However, the flaws of the lightning network are also very
obvious. First, the off-chain channel requires both parties
to be online at the same time. Second, it has been reported
that the lightning network’s large transaction success rate
is low [97], indicating that current Lightning Network is
not suitable for handling high-value transactions. These two
disadvantages listed above greatly limit the wide-adoption of
lightning networks.
Raiden Network: Raiden Network is a payment-channel
Establish channel
two parties
Close channel
Deposit Deposit
balance Retrieve
FIGURE 7. Procedures of Lightning network
for Ethereum. Its implementation is very similar to the
Lightning Network. The main difference is that the Raiden
Network supports all ERC20 [98] tokens, while the Lightning
Network is limited to Bitcoin transactions.
Payment channels have been widely researched in recent
years, releasing several implementations of the Lightning
Network [99]–[101]. Besides, there are many other solu-
tions of off-chain payment channel from academia, including
Bitcoin Duplex Micropayment Channels [26], Sprites [28],
AMHLs [102]. Sprites develops constant locktimes to im-
prove transaction throughput in Payment channel networks
and support incremental deposits and withdrawals without in-
terrupting the payment channel. AMHLS utilizes anonymous
multi-hop locks to preserve privacy in the Payment channel
and also reduce the communication overhead. There remains
a large space for research to provide a more effective and
secure payment channel.
2) Sidechain
Pegged Sidechain [29] is the first sidechain that enables
assets in blockchains like Bitcoin to be transferred between
different blockchains while preventing the assets from mali-
cious attackers and also ensuring the atomicity of the trans-
Figure 9 shows an example of transferring assets from
parent chain to side chain by the Two-way peg protocol
proposed in Pegged Sidechains [29]. First, the parent chain
sends coins to a special output that cannot be unlocked
without a Simplified Payment Verification (SPV) [103] proof
on the pegged sidechain. After sending coins is a waiting
period called confirmation period, which intends to protect
the transferring from a denial of service attack and trades
latency for security. Unlocking action is followed by the
contest period, in which the newly-transferred assets cannot
be spent on the sidechain, aims to prevent double-spending
of the previously-locked assets.
Transferring assets from the Pegged sidechain back to the
Parent chain is the same procedure as above, so the protocol
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Open channel
FIGURE 8. Lightning network topology: A circle represents a user in the
lightning network and a left-right arrow indicates a trading channel established
between both sides of the arrow.
is also called Symmetric Two-Way Peg.
Plasma: Plasma [21] is a framework of sidechain attached
to the Ethereum main chain. Its root is a smart contract
running on the main chain, which records the rules and the
state hash of the sidechain. Multiple child chains can be
generated from the root, which is continuously expanding
and finally become a tree structure. Users can create a ledger
on the Plasma chain and achieve asset-transfer between the
Plasma chain and the Ethereum main chain via the root. Users
can also withdraw their funds from the chain any time.
Transactions can be carried out between different users
on the child chains, similar to the situation under Bitcoin’s
Lightning Network. However, Plasma allows multiple par-
ticipants to interact without requiring all participants to be
online at the same time to update the transaction status.
Furthermore, Plasma can reduce the pressure of the
Ethereum main chain by minimizing transaction status so that
a simple hash can represent the update of multiple statuses. In
this way, Plasma is capable to extend the transactions volume
of the side chain.
While improving scalability, Plasma also provides some
measures to ensure security avoid security hazards (e.g.
double-spending) in sidechains. The Plasma chain submits
the hash of the header of its block to the Ethereum main chain
periodically. Thus, the main chain can verify the validity of
transactions included in Plasma chains. If fraud is found in
an invalid block, it will be rolled back with a slashed penalty.
Based on the framework aforementioned, many versions of
Plasma have been designed. Minimal Viable Plasma (Plasma
MVP) [104] is a simplified version based on the Unspent
Transaction Outputs (UTXO) model and shows the funda-
mental properties of Plasma. Plasma Cash [105], a later
improved version of Minimal Viable Plasma, proposes a
mechanism in which each deposit operation corresponds to a
unique coin ID and uses a data structure called Sparse Merkle
Tree [106] to store the transaction history. Plasma Debit [107]
is another implementation of Plasma framework and also an
extension of Plasma Cash. Plasma is still under development
and will be a potential solution to substantially scale out the
blockchain systems.
Send assets to SPV-locked output
Confirmation Period Contest Period
Unlock(with SPV proof )
Parent chain Pegged sidechain
FIGURE 9. Two-way peg protocol of Pegged Sidechain [29]: Two red dotted
lines indicate the procedure of transferring assets from the Parent chain to the
Pegged sidechain. The blue dotted lines show the reverse procedure.
Liquidity Network (Nocust): The previous state-channel
solutions [20] require at least one transaction on the parent-
chain when a channel is established, and also have the
drawback that the transaction funds need to be saved in the
trading channel as a deposit and the transaction channel relies
on complex routing topologies.
The Liquidity.Network [30] team proposed the securely
scalable commit-chain named Nocust [108], which has the
following excellent properties:
A new kind of data structure called Merkleized Interval
Tree is a multi-layered tree. Individual user account
balances are stored in exclusive non-crossing interval
space, but the structure ensures that the balances of
different users can be summed very quickly to verify
whether the amount is the same as that recorded in the
smart contract on the parent-chain.
Nocust is non-custodial, that is, there is no need to limit
the funds of the users on the chain, unlike the lightning
network which requires participants to deposit in prior
for the channel.
Users do not need to interact with the parent-chain to
join the commit-chain. They are free to trade with each
other, including transferring funds and receiving funds.
Nocust can guarantee real-time transactions and reduce
transaction delays without additional fees and mort-
The experimental results in the paper [108] show that
Nocust can maintain a very low transaction fee and achieve a
high transaction throughput when scaling to one billion users.
These merits imply the practicality of its scalability solution.
3) Off-Chain Computation
Miners in Ethereum need to emulate the execution of all
contracts to verify their states. The process is costly and
limits the scalability of Ethereum. Thus, some solutions have
been proposed to build scalable smart contracts.
Truebit: Truebit [32] is a system for verifiable compu-
tation that outsources complex computing tasks to an off-
chain market. Such the off-chain market executes the tasks
and verifies the results and finally submits them back to
the main chain. It was originally designed to break the gas
restrictions of the Smart Contracts in Ethereum platform. For
instance, a DApp needs to perform a very complicated and
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expensive calculation task which is costly and inefficient in
Ethereum. Then, the Truebit protocol is a good option for this
DApp. Overall, Truebit is divided into three layers including
the Incentives Layer, the Dispute Resolution Layer, and the
Computational Layer. Each layer is elaborated as follows.
Computational Layer: In this layer, users submit the
computing task code and incentives to publish a task.
There is an off-chain computing market, in which the
miners listen to tasks and run the code after paying
deposits. Each participant who solves a task is called
a Solver, and each Verifier is responsible for verifying
that a task is completed correctly.
Dispute Resolution Layer: As the name suggests, Dis-
pute Resolution layer is responsible for resolving dis-
putes. When a computation is completed, the verifiers
verify the result. If one of the verifiers finds that the re-
sult is incorrect, it can call into question about the result,
and then both parties will be involved in a verification
game. They can use interactive verification to find the
specific steps they have in conflict.
In the verification game, the party who is wrong will be
punished, to prevent from deliberately cheating for both
Incentives Layer: Solvers get rewards by solving tasks
and verifiers get rewards by detecting errors from the
results computed by solvers. However, verifiers can’t get
a reward if no error found for a long time. If incentives
for verifiers are not enough, the number of verifiers in
the market will keep losing, resulting in the imbalance
of the whole system. To solve this problem, Truebit adds
aforced error mechanism that enforces the solvers to
provide erroneous calculations periodically and add tags
in the hash. In this way, when a verifier finds an error,
both the solver and verifier can be rewarded, making
verifiers profitable.
Arbitrum: Arbitrum [33] introduces a new protocol that
improves the scalability of smart contracts by moving the
computation of verifying smart contracts off-chain. In Ar-
bitrum, Verifier is a global role that validates transactions,
e.g., Miners in Bitcoin. Arbitrum utilizes a Virtual Machine
to implement a contract that owns a fund, which cannot be
overspent by any execution of the contract. And, every party
can create a VM and select a set of VM managers to force
the VM to work correctly according to the VM’s code. If
all managers of a VM agree with the new state of VM,
they sign a Unanimous assertion. On the other hand, VM
managers sign a Disputable assertion to challenge the VM’s
state change and be engaged in the bisection protocol. The bi-
section protocol performs similarly with Dispute Resolution
in Truebit, intending to determine if the VM’s state change is
correct. In this way, only hashes of contract states need to be
verified by the Verifier. This releases the pressure of verifiers
and also allows contracts to execute privately.
With the support of verifiable computation, large scale
computation tasks can be solved off-chain, which provides
FIGURE 10. Architecture for Relay [22], [31]
great improvement in the scalability of blockchain systems.
4) Cross-Chain Techniques
Nowadays, cross-chain projects are also fashionable and
viewed as potential solutions to scale out blockchain systems.
Relay technique [22], [31] is another obvious idea of
connecting different blockchains together, expecting to build
a big network of blockchains and ensuring interoperability
between different blockchains. Figure 10 shows a model of
current inter chain architecture called Relay, the components
of which includes independent blockchains built atop sim-
ilar consensus and relay chain connecting all independent
blockchains. In addition, the Pegged chain (e.g. Peg Zone in
Cosmos and Parachain bridge in Polkadot) is also provided
to bridge existing blockchains with the cross-chain system.
Relay chain in Figure 10 serves the role as a router,
enabling new independent blockchains to join in the cross-
chain system and adopting cross-chain protocols to process
cross-chain transactions more efficiently and also to ensure
the consistency.
We then review several representative cross-chain projects
as follows.
Cosmos: Cosmos [22] is an ecosystem of connected
blockchains. The network is comprised of many indepen-
dent blockchains, each of which is called a zone. Powered
by consensus algorithms like Tendermint consensus, those
zones can communicate with each other via their Inter-
Blockchain Communication (IBC) protocol, allowing het-
erogeneous chains to exchange values (i.e. tokens) or data
with each other. Hub (a framework like Relay-chain shown
in Figure 10) is the first zone on Cosmos, and any other
zones can connect to it. Therefore, Cosmos achieves inter-
operability where zones can send to or receive from other
zones securely and quickly via Hub, instead of creating
connections between every two zones.
Cosmos also provides Tendermint core and Cosmos SDK
(Software Development Kit) [109]) for developers to build
Blockchains based on Tendermint consensus conveniently
such that more blockchains can join the system and gradually
extend the scalability of a network. With multiple parallel
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chains running in the network, Cosmos can achieve a hori-
zontal scalability.
Unfortunately, the popular PoW-featured blockchain such
as Bitcoin and Ethereum, cannot connect to Cosmos Hub
directly. An alternative solution is to create a customized Peg-
zone (like Pegged chain shown in Figure 10) as a bridge to
exchange data.
Polkadot: Polkadot [31] also outlined a multi-chain pro-
tocol that provides a relay-chain to connect heterogeneous
blockchains. As mentioned already, relay-chain enables
an independent blockchain, an example which is called
parachain in Polkadot, to exchange information and trust-
free inter-chain transactability. In addition, parachain bridge
can link to already running blockchains such as Ethereum.
All these proposals employed are able to achieve interop-
erability and scalability.
Section III introduces many solutions proposed in recent
years dedicated to solving the scalability of blockchain.
However, there is still no method that can be applied to exist-
ing well-known blockchain systems and solve this problem
perfectly. To this end, we should continue to explore and
improve existing solutions to achieve a better effect. Here are
a few possible directions.
Layer-1 solutions have been studied widely, but it still re-
quires more explorations for scalability solutions. We envi-
sion open issues in the directions of block data and sharding
1) Block data
Despite other methods concerning scalability, the individual
nodes’ limited capability of storage and bandwidth will be
the performance bottleneck of blockchain systems. Firstly,
increasing TPS indicates that much more block data need to
be propagated within the system, which may aggravate the
congestion problems. Besides, as the blockchain grows, more
blockchain data should be stored by individual nodes. It will
increase the pressure of storage and promote the tendency to
centralization. Many discussions about chain pruning [110]
[111] [112] have been proposed. Blockchain pruning ap-
proaches aim to remove some historical data that is not crit-
ical from the blockchain while preserving the security. The
reduction of data releases the storage pressure of full nodes
in the blockchain. Therefore, to keep developing blockchain,
solutions related to block compression and blockchain data
pruning require more optimization and should be applied to
real blockchain systems.
2) Sharding techniques
The sharding technique is a popular and effective solution.
A sharding-based blockchain is divided into different shards
with proper mechanisms to manage each shard as well
as transactions and scales horizontally with the number of
nodes. However, the following two issues are still open for
further investigations:
(1) How to place transactions into different shards. 95%
transactions in OmniLedger [12] are cross-shard trans-
actions, leading to much bandwidth pressure because of
the communication cost of cross-shard transactions and
thus decrease the performance of the whole sharding-
based system. Besides the communication cost, recon-
figuring shards also cause the exchange of a great
amount of data. Therefore, better algorithms should be
provided to solve the problem.
(2) How to improve the efficiency of cross-shard trans-
actions. The existing solutions have achieved several
good results by their cross-shard submission protocols.
However, since cross-shard transactions involve multi-
ple shards and lead to more bandwidth consumption and
longer confirmation time, a more efficient protocol is
needed to reduce the confirmation latency. This direc-
tion still has a large room to explore.
Regarding the Layer-2 solutions, some of them are still in
their work-in-progress stages. In particular, Lightning Net-
work is under the spotlight. Many teams have developed the
Lightning Network clients and have achieved a high user-of-
experience through a series of improvements in the routing
mechanism. When the Ethereum’s Plasma framework was
proposed, many follow-up teams implemented it to varying
degrees, proving the high recognition of sidechain technol-
ogy. According to the prototypes outlined in this paper, the
subsequent studies should focus on the relationship between
the sidechain and the main chain, and how to scale out the
blockchain and achieve substantial improvement on over-
all performance while ensuring its fundamental properties.
Cross-chain solutions, like Cosmos and Polkadot, have de-
vised their dedicated protocols in order to build a network of
heterogeneous blockchain.
We particularly review some new solutions proposed recently
and classify them into the category of Layer-0. This type of
solutions concern the optimizations of the dissemination pro-
tocol for information (transaction or block) in the blockchain
network. Nodes in the blockchain network broadcast blocks
and transactions to the network, but the broadcast is not ef-
ficient enough, leading to latency and high bandwidth usage.
Some solutions related to block compression discussed above
like Compact Blocks [10], also focus on the optimization
of block propagation, and thus can be viewed as a Layer-
0 solution. As mentioned before, faster block propagation
leads to larger blocks and shorter block intervals, thereby
increasing transaction throughput. Thus, the protocols aiming
at optimizing the data propagation in blockchains are desired
in future scalable blockchain systems.
Several approaches intending to improve the propagation
protocol have been proposed. For instance, Erlay [46] opti-
VOLUME 4, 2016 13
Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS
mizes Bitcoin’s transaction relay protocol to reduce the over-
all bandwidth consumption while increasing the propagation
latency. Velocity [48] also brings some improvement in block
propagation by utilizing Fountain code, a kind of erasure
code, to reduce the amount of data be propagated. Kadcast
[47] proposes an efficient block propagation approach based
on overlay structure of Kademlia [113]. bloXroute [49] is a
Blockchain Distribution Network (BDN) that helps individ-
ual nodes to propagate transactions and blocks more quickly.
Besides these solutions, there remains a lot of room for
optimizations of propagation protocols of current blockchain
systems, such as better routing mechanisms, that will con-
tribute to the improvement of the scalability of blockchain.
Blockchain technologies have grown rapidly in the past few
years and will be applied to more applications in different
fields in the foreseeable near future. With the increasing
adoption of blockchain technology, the number of users has
steadily increased. However, the network congestion prob-
lem that has occurred many times and enforced people to
carefully think about how to solve the scalability issue of
blockchains. To this end, a number of new solutions have
been proposed. In this paper, we describe the blockchain per-
formance problem mainly paying attention to scalability, and
then classify the existing mainstream solutions into several
representative layers. Besides, we elaborate some popular
solutions such as Sharding, Sidechain, and cross-chain, in-
tending to give a comprehensive explanation. Furthermore,
we also summarize several potential research directions and
open issues based on the drawback found, such as the huge
amount of blockchain data that need to be compressed or
pruned, the inefficient cross-shard transaction and unfinished
protocols to bridge the existing blockchain to cross-chain
platforms, aiming at addressing the scalability of blockchain
By this comprehensive survey, we expect our classification
and the analysis over the current solutions can inspire further
booming studies dedicated to improving the scalability of
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QIHENG ZHOU is currently an undergraduate
student at the School of Data and Computer Sci-
ence, Sun Yat-Sen University, China. His current
research interest is blockchain.
HUAWEI HUANG (M’16) is currently an As-
sociate Professor with Sun Yat-Sen University,
China. He earned his Ph.D. degree in Computer
Science and Engineering from the University of
Aizu (Japan) in 2016. His research interests in-
clude blockchain and intelligent computing. He
has served as a Research Fellow of JSPS (2016-
2018); a visiting scholar with Hong Kong Poly-
technic University (2017-2018); an Assistant Pro-
fessor with Kyoto University, Japan (2018-2019).
He received the best paper award from TrustCom2016. He is a member of
ZIBIN ZHENG received the Ph.D. degree from
the Chinese University of Hong Kong, in 2011.
He is currently a Professor at School of Data and
Computer Science with Sun Yat-sen University,
China. He serves as Chairman of the Software
Engineering Department. He published over 120
international journal and conference papers, in-
cluding 3 ESI highly cited papers. According to
Google Scholar, his papers have more than 7000
citations, with an H-index of 42. His research
interests include blockchain, services computing, software engineering, and
financial big data. He was a recipient of several awards, including the Top 50
Influential Papers in Blockchain of 2018, the ACM SIGSOFT Distinguished
Paper Award at ICSE2010, the Best Student Paper Award at ICWS2010. He
served as BlockSys’19 and CollaborateCom’16 General Co-Chair, SC2’19,
ICIOT’18 and IoV’14 PC Co-Chair.
JING BIAN received the B. Sc. degree in Automa-
tion in 1988, the M. Sc. degree in Computational
Mathematics in 2001, and the Ph. D. degree in
Physics in 2006. from Sun Yat-sen University,
Guangzhou, P. R. China. She is currently a vice-
professor with the School of Data and Computer
Science, Sun Yat-sen University, Guangzhou, P.
R. China. Her current research interests include
design and analysis of algorithms, blockchain,
Electronic Commerce and social networks.
16 VOLUME 4, 2016
... Throughput is limited in BC due to its consensus mechanisms and cryptographic security protocol. A large amount of bandwidth for new blocks in the BC is required to improve throughput [42,43]. Thus, increasing throughput in BC is challenging to meet the need for frequent transactions in IoT systems. ...
Full-text available
With the growth of computing and communication technologies, the information processing paradigm of the healthcare environment is evolving. The patient information is stored electronically , making it convenient to store and retrieve patient information remotely when needed. However, evolving the healthcare systems into smart healthcare environments comes with challenges and additional pressures. Internet of Things (IoT) connects things, such as computing devices , through wired or wireless mediums to form a network. There are numerous security vulner-abilities and risks in the existing IoT-based systems due to the lack of intrinsic security technologies. For example, patient medical data, data privacy, data sharing, and convenience are considered imperative for collecting and storing electronic health records (EHR). However, the traditional IoT-based EHR systems cannot deal with these paradigms because of inconsistent security policies and data access structures. Blockchain (BC) technology is a decentralized and distributed ledger that comes in handy in storing patient data and encountering data integrity and confidentiality challenges. Therefore, it is a viable solution for addressing existing IoT data security and privacy challenges. BC paves a tremendous path to revolutionize traditional IoT systems by enhancing data security, privacy, and transparency. The scientific community has shown a variety of healthcare applications based on artificial intelligence (AI) that improve health diagnosis and monitoring practices. Moreover, technology companies and startups are revolutionizing healthcare with AI and related technologies. This study illustrates the implication of integrated technologies based on BC, IoT, and AI to meet growing healthcare challenges. This research study examines the integration of BC technology with IoT and analyzes the advancements of these innovative paradigms in the healthcare sector. In addition, our research study presents a detailed survey on enabling technologies for the futuristic, intelligent, and secure internet of health things (IoHT). Furthermore, this study comprehensively studies the peculiarities of the IoHT environment and the security, performance , and progression of the enabling technologies. First, the research gaps are identified by mapping security and performance benefits inferred by the BC technologies. Secondly, practical issues related to the integration process of BC and IoT devices are discussed. Third, the healthcare applications integrating IoT, BC, and ML in healthcare environments are discussed. Finally, the research gaps, future directions, and limitations of the enabling technologies are discussed.
... Therefore, most existing solutions to this scalability problem use lighter and faster consensus algorithms to achieve high transactional throughput [13][14][15][16][17]. Other existing solutions approach blockchain scalability from a storage perspective and propose storage optimization schemes to reduce the storage requirement of peers [18][19][20][21][22][23][24][25][26][27][28]. ...
Full-text available
The increase of the Internet of Things (IoT) calls for secure solutions for industrial applications. The security of IoT can be potentially improved by blockchain. However, blockchain technology suffers scalability issues which hinders integration with IoT. Solutions to blockchain’s scalability issues, such as minimizing the computational complexity of consensus algorithms or blockchain storage requirements, have received attention. However, to realize the full potential of blockchain in IoT, the inefficiencies of its inter-peer communication must also be addressed. For example, blockchain uses a flooding technique to share blocks, resulting in duplicates and inefficient bandwidth usage. Moreover, blockchain peers use a random neighbor selection (RNS) technique to decide on other peers with whom to exchange blockchain data. As a result, the peer-to-peer (P2P) topology formation limits the effective achievable throughput. This paper provides a survey on the state-of-the-art network structures and communication mechanisms used in blockchain and establishes the need for network-based optimization. Additionally, it discusses the blockchain architecture and its layers categorizes existing literature into the layers and provides a survey on the state-of-the-art optimization frameworks, analyzing their effectiveness and ability to scale. Finally, this paper presents recommendations for future work.
The healthcare business can benefit from the Internet of Things (IoT) in terms of data management and transfer. Patient health data can be acquired locally from devices that can be used to make real-time decisions based on the data. For many years, several hospitals have deployed IoT in their patient rooms and their body parts. IoT devices could be vulnerable to hacking due to a lack of authentication and encryption policies, posing serious hazards to consumers. As a result, blockchain is useful for securing and proving transactions in the IoT. As a result, it is proposed that the problem be solved using Fog Computing (FGC), Edge Computing, and blockchain. Based on FGC technology, models of analysis, and a signature-encryption algorithm (SE), a three-tiered architecture is employed for the identification, verification, and identification of healthcare IoT devices. The generated findings are used to assess the suggested innovative SE algorithm. We discovered that FGC-based blockchain performed better for detecting malicious nodes in terms of packet error rate, reliability, and throughput when compared to cloud settings and other environments.
Enormous amounts of capital will need to be mobilized in the coming decades to drive the transition of our global economy toward climate neutrality. In recent years, the financial system started to move in a more environmentally sustainable direction, inter alia offering more green investment opportunities. Speeding up this process is inevitable if humanity wants to meet the Sustainable Development Goals (SDGs) and achieve the targets of the Paris Climate Agreement. Distributed Ledger Technology (DLT) may enable climate finance to effectively accelerate this transition toward sustainability. Inherent characteristics like security, immutability, transparency, and auditability make DLT a highly promising tool for climate-related use cases. However, the mainstream adoption of these digital innovations is still constrained by various obstacles, including technological and regulatory barriers, especially in the highly regulated finance sector. Existing digital divides between developed and developing nations further exacerbate these challenges. Based on these initial deliberations, this chapter shows how DLT can be used effectively for innovative climate finance. We first introduce the technology and discuss its sustainability as well as different possible DLT governance structures. Based on this introduction, we provide an overview of sustainability-related initiatives and networks linked to DLT. We review various DLT applications in Decentralized Finance (DeFi), asset management, Measurement, Reporting and Verification (MRV), tokenization, and other relevant fields. We conclude with an outlook on the role of DLT in climate finance and present recommendations for overcoming some of the remaining risks and obstacles.
The energy sector is undergoing a transition of decarbonization, decentralization, and digitalization. This paper explores the application of blockchain in this transition. Through qualitative content analysis, expert interviews and discussion rooted in recent literature, key insights on the practical challenges and opportunities of blockchain energy applications are investigated. This is done in technological, economic, social, environmental, and institutional dimensions in order to propose holistic insights. Key dentified issues include scalability, data privacy, costs of change, market dynamics, user experience, skills, multi-stakeholder governance, and regulatory change. Key identified opportunities also range across these dimensions, such as blockchain advancements, power market development, user research, and regulatory sandboxes. While most research on blockchain energy applications has been from the technological lens, this paper contributes with a multi-dimensional approach. The interrelatedness among these dimensions is one of the key messages of this paper. Both corporate strategy and public policy development need to integrate user behavior research, business model innovation and regulatory experimentation for a holistic approach in understanding and implementing blockchain, and other emerging technologies. It is suggested that the multi-dimensional challenges and opportunities identified in this study, and functioning of their interrelatedness, are of interest for further exploration both in academia and among practitioners.
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Title: Let's learn use blockchain! Abstract: Since the appearance of Bitcoin in 2008, the word “blockchain ” has been used in various contexts. On the other hand, it is sometimes said that the definition of blockchain is not clearly defined. So, may people might have an impression that it is a difficult technology to understand. However, the essence of blockchain is not difficult at all, and we don't need to have any knowledge of difficult mathematics and physics (except for details of cryptography). Therefore, the essence can be sufficiently understood even for junior and senior high school students. In this explanatory article, the comprehensible explanation as much as possible is tried on the core idea of blockchain. It is also clarified that although the origin of the word "blockchain" is from Bitcoin, the innovative technology proposed by Bitcoin is not blockchain but a technology for internet scale open participation consensus. One of the applications of blockchain technology is a persistent proof of electronic data. The method of issuing and verifying the persistent proof using Bloxberg blockchain is also explained. Supplementary materials:
Fair selection of community members’ transactions in blockchain-based technologies, especially in finance, is essential and vital. Therefore, in this paper, a novel consensus model for fair selection of community members’ transactions through reputation models is presented as fair reputation-based consensus model (FRCM), so that all transactions of community members have the same weight and transactions are selected regardless of the transaction fee or the assets or computing power but based on the reputation of the trustees who are responsible for placing the transactions in the block. For this purpose, a FRCM prototype has been designed and implemented based on the proposed algorithms. Experimental and theoretical evaluations and simulation results show that the FRCM can select the transactions of community members fairly while reducing the average response time effectively. In addition, comparative analysis with other consensus mechanisms proved that FRCM model has lower consensus latency than other mechanisms, while improving throughput and scalability.
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Blockchain's evolution during the past decade is astonishing: from bitcoin to over 2.000 altcoins, and from decentralised electronic payments to transactions programmable by smart contracts and complex tokens governed by decentralised organisations. While the new generation of blockchain applications is still evolving, blockchain's technical characteristics are also advancing. Yet, immutability, a hitherto indisputable and highly advertised property according to which blockchain data cannot be edited nor deleted, remains the cornerstone of blockchain's security. Nevertheless, blockchain's immutability is being called into question lately in the light of the new erasing requirements imposed by the GDPR's "Right to be Forgotten (RtbF)" provision. As the RtbF obliges blockchain data to be editable in order restricted content redactions, modifications or deletions to be applied when requested, blockchains compliance with the regulation is indeed challenging, if not impracticable. Towards resolving this contradiction, various methods and techniques for mutable blockchains have been proposed to satisfy regulatory erasing requirements while preserving blockchains' security. To this end, this work aims to provide a comprehensive review on the state-of-the-art research approaches, technical workarounds and advanced cryptographic techniques that have been put forward to resolve this conflict and to discuss their potentials, constraints and limitations when applied in the wild to either permissioned or permissionless blockchains.
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Blockchain has numerous benefits such as decentralization, persistency, anonymity and auditability. There is a wide spectrum of blockchain applications ranging from cryptocurrency, financial services, risk management, Internet of Things to public and social services. Although a number of studies focus on using the blockchain technology in various application aspects, there is no comprehensive survey on the blockchain technology in both technological and application perspectives. To fill this gap, we conduct a comprehensive survey on the blockchain technology. In particular, this paper gives the blockchain taxonomy, introduces typical blockchain consensus algorithms, reviews blockchain applications and discusses technical challenges as well as recent advances in tackling the challenges. Moreover, this paper also points out the future directions in the blockchain technology.
Conference Paper
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Designing a secure permissionless distributed ledger (blockchain) that performs on par with centralized payment processors, such as Visa, is a challenging task. Most existing distributed ledgers are unable to scale-out, i.e., to grow their total processing capacity with the number of validators; and those that do, compromise security or decentralization. We present OmniLedger, a novel scale-out distributed ledger that preserves longterm security under permissionless operation. It ensures security and correctness by using a bias-resistant public-randomness protocol for choosing large, statistically representative shards that process transactions, and by introducing an efficient cross-shard commit protocol that atomically handles transactions affecting multiple shards. OmniLedger also optimizes performance via parallel intra-shard transaction processing, ledger pruning via collectively-signed state blocks, and low-latency "trust-but-verify" validation for low-value transactions. An evaluation of our experimental prototype shows that OmniLedger's throughput scales linearly in the number of active validators, supporting Visa-level workloads and beyond, while confirming typical transactions in under two seconds.
Conference Paper
In order to propagate transactions and blocks, today's blockchain systems rely on unstructured peer-to-peer overlay networks. In such networks, broadcast is known to be an inefficient operation in terms of message complexity and overhead. In addition to the impact on the system performance, inefficient or delayed block propagation may have severe consequences regarding security and fairness of the consensus layer. Therefore, we introduce Kadcast, a novel peer-to-peer protocol for block propagation in blockchain networks. Kadcast utilizes the well-known structured overlay topology of Kademlia to realize an efficient broadcast operation with tunable overhead. As our protocol is based on UDP, we incorporate forward error correction (FEC) to increase reliability while still maintaining its lightweight protocol architecture. To this end, we build a probabilistic model to analyze Kadcast's resilience to packet losses and node failures. Moreover, we evaluate Kadcast's block delivery performance, broadcast reliability, efficiency, and security based on advanced network simulations, which confirm the merits of the Kadcast protocol.
Conference Paper
Recently, Blockchain becomes a hot research topic due to the success of Blockchain in many applications, such as cryptocurrency, smart contract, digital assets, distributed cloud storage and so on. The power of Blockchain is that it can achieve the consensus of an ordered set of transactions among nodes which do not trust each other, even with the existence of malicious nodes. However, compared to traditional databases, the current Blockchain technology still cannot handle a massive number of transactions, which is caused by many factors, such as the consensus protocol, structure of the blocks and storage challenge. Among them, the high storage requirement is a key factor that prevents the wide usage of Blockchain on various devices such as mobile phones or low-end PCs. In this paper, to address the storage challenge, we introduce a novel concept called Consensus Unit (CU), which organizes different nodes into one unit and lets them to store at least one copy of Blockchain data in the system together. Based on this idea, we further define the Blocks Assignment Optimization (BAO) problem which determines the optimal assignment of blocks such that the storage space is fully used and the query cost is minimized. We prove that the BAO problem is NP-hard. Thus, we propose three efficient heuristic algorithms to solve the static assignment problem. Furthermore, we present solutions to address the dynamic scenarios when new blocks arrive and nodes join or depart from the CU. To verify the effectiveness of CU, we have conducted extensive experiments on synthetic data and BLOCKBENCH [1]. The results have confirmed the superiority of CU in saving the storage and maintaining the system throughput.
Conference Paper
A major approach to overcoming the performance and scalability limitations of current blockchain protocols is to use sharding which is to split the overheads of processing transactions among multiple, smaller groups of nodes. These groups work in parallel to maximize performance while requiring significantly smaller communication, computation, and storage per node, allowing the system to scale to large networks. However, existing sharding-based blockchain protocols still require a linear amount of communication (in the number of participants) per transaction, and hence, attain only partially the potential benefits of sharding. We show that this introduces a major bottleneck to the throughput and latency of these protocols. Aside from the limited scalability, these protocols achieve weak security guarantees due to either a small fault resiliency (e.g., 1/8 and 1/4) or high failure probability, or they rely on strong assumptions (e.g., trusted setup) that limit their applicability to mainstream payment systems. We propose RapidChain, the first sharding-based public blockchain protocol that is resilient to Byzantine faults from up to a 1/3 fraction of its participants, and achieves complete sharding of the communication, computation, and storage overhead of processing transactions without assuming any trusted setup. RapidChain employs an optimal intra-committee consensus algorithm that can achieve very high throughputs via block pipelining, a novel gossiping protocol for large blocks, and a provably-secure reconfiguration mechanism to ensure robustness. Using an efficient cross-shard transaction verification technique, our protocol avoids gossiping transactions to the entire network. Our empirical evaluations suggest that RapidChain can process (and confirm) more than 7,300 tx/sec with an expected confirmation latency of roughly 8.7 seconds in a network of 4,000 nodes with an overwhelming time-to-failure of more than 4,500 years.