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Consortium Blockchains: Overview, Applications and Challenges

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The Blockchain technology has recently attracted increasing interests worldwide because of its potential to disrupt existing businesses and to revolutionize the way applications will be built, operated, consumed and marketed in the near future. While this technology was initially designed as an immutable and distributed ledger for preventing the double spending of cryptocurrencies, it is now foreseen as the core backbone of enterprises by enabling the interoperability and collaboration between organizations. In this context, consortium blockchains emerged as an interesting architecture concept that benefits from the transactions’ efficiency and privacy of private blockchains, while leveraging the decentralized governance of public blockchains. Although many studies have been made on the blockchain technology in general, the concept of consortium blockchains has been very little addressed in the literature. To bridge this gap, this article provides a detailed analysis of consortium blockchains, in terms of architectures, technological components and applications. In particular, the underlying consensus algorithms are analyzed in details, and a general taxonomy is discussed. Then, a practical case study that focuses on the consortium blockchain technology Ethermint is performed in order to highlight its main advantages and limitations. Finally, various research challenges and opportunities are discussed.
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Consortium Blockchains: Overview, Applications and Challenges
Omar Dib, Kei-Leo Brousmiche, Antoine Durand, Eric Thea, Elyes Ben Hamida
IRT SystemX, Paris-Saclay, France
Email: {first.lastname}@irt-systemx.fr
Abstract—The Blockchain technology has recently attracted in-
creasing interests worldwide because of its potential to disrupt
existing businesses and to revolutionize the way applications will
be built, operated, consumed and marketed in the near future.
While this technology was initially designed as an immutable
and distributed ledger for preventing the double spending of
cryptocurrencies, it is now foreseen as the core backbone of enter-
prises by enabling the interoperability and collaboration between
organizations. In this context, consortium blockchains emerged
as an interesting architecture concept that benefits from the
transactions’ efficiency and privacy of private blockchains, while
leveraging the decentralized governance of public blockchains.
Although many studies have been made on the blockchain
technology in general, the concept of consortium blockchains has
been very little addressed in the literature. To bridge this gap, this
article provides a detailed analysis of consortium blockchains,
in terms of architectures, technological components and appli-
cations. In particular, the underlying consensus algorithms are
analyzed in details, and a general taxonomy is discussed. Then,
a practical case study that focuses on the consortium blockchain
technology Ethermint is performed in order to highlight its main
advantages and limitations. Finally, various research challenges
and opportunities are discussed.
KeywordsConsortium Blockchain; Distributed Ledger Technol-
ogy; Smart Contract; Consensus Algorithm; Data Privacy and
Security; Scalability; Benchmark.
I. INTRODUCTION
The blockchain technology has attracted increased interests
worldwide in recent years, with emerging applications in
key domains, including finance, energy, insurance, logistics
and mobility. Indeed, this technology is expected to disrupt
businesses and markets on a global scale.
A blockchain is essentially a trustless, peer-to-peer and
continuously growing database (or ledger) of records, aka.
blocks, that have been verified and shared among the partici-
pating entities [1]. Each block typically contains a timestamp,
a cryptographic hash value of the previous block, and a set of
transactions data. Once a new block is validated by consen-
sus and written to the ledger, transactions cannot be altered
retroactively without the collusion of the network majority.
This technology was initially designed as a public transaction
ledger to solve the double spending problem in the Bitcoin
digital currency system [2], without the need for any third
party or trust authority.
This technology per se is not novel, but is rather a
combination of well-known building blocks, including peer-
to-peer protocols, cryptographic primitives, distributed con-
sensus algorithms and economic incentives mechanisms. A
blockchain is more a paradigm shift in the way applications
and solutions will be built, deployed, operated, consumed and
marketed in the near future, than just a technology. Blockchain
is secure by design and relies on well-known cryptographic
tools and distributed consensus mechanisms to provide key
characteristics, such as persistence, anonymity, fault-tolerance,
auditability and resilience.
More recently, smart contracts [3] have emerged as a new
usage for blockchains to digitize and automate the execu-
tion of business workflows (i.e., self-executing contracts or
agreements), and whose proper execution is enforced by the
consensus mechanism. This makes the blockchain technology
particularly suitable for the management of medical records
[4], notary services [5], users’ identities [6] and reputations
[7], data traceability [8], vehicles’ history [9], etc.
In this context, the blockchain technology is foreseen
as the core backbone of future smart cities and enterprises
by enabling the interoperability and collaboration between
organizations, while enhancing their security, data and process
management. Although many studies have been made on
the blockchain technology in general [10], the application
of this technology to business environments, beyond digital
currencies, has been very little addressed in the literature.
Indeed, several challenges will need to be addressed to unlock
the tremendous potential of blockchains especially before this
paradigm shift becomes technically, economically and legally
viable in enterprises environments.
These challenges concern the technical aspects of
blockchains, including its architecture, deployment, gover-
nance, scalability and data privacy. Private and consortium
blockchains emerged from this perspective as appropriate
architecture concepts for business environments, where re-
strictions are applied on who is allowed to participate to the
network. While the former approach assumes that the network
is operated by a single entity, a consortium blockchain operates
under the leadership of a group of entities, thus enabling
collaborative business transformation among organizations and
innovative business models. Last but not least, the legal aspects
of blockchains represent a challenge, especially in Europe,
where this technology should be analyzed in the light of
upcoming new regulations, such as the General Data Pro-
tection Regulation (GDPR) (Regulation (EU) 2016/679 [11]),
and whose objective is to strengthen users’ data privacy and
protection within the European Union.
This article aims at bridging the gap between blockchain
technologies and their potential benefits to business envi-
ronments, by providing a detailed analysis of consortium
blockchains, in terms of architectures, technologies and ap-
plications. In particular, the underlying consensus algorithms
are analyzed in details, and a general taxonomy is discussed.
Then, a practical and experimental use case is discussed in
order to assess the performance of the consortium blockchain
technology Ethermint. The performance indicators analyzed
are the time required to validate a transaction, the number of
transactions validated per second, as well as the average inter
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blocks delay and the size of the blockchain data folder. Param-
eters that have been varied are the number of validators, the
number of transactions submitted per second and the topology
of the network. The results highlighted some limitations in
terms of transactions validation time and storage requirements
that may hinder the usage of Ethermint to deal with some real
world use cases.
The remainder of this article is organized as follows. Sec-
tion II discusses the technical aspects of blockchains in terms
of taxonomy, system architecture, and building blocks. Section
III provides a detailed analysis of consensus algorithms that are
used today in private and consortium blockchains. Section IV
discusses a general classification of blockchains applications
and highlights typical use cases in the finance, energy, mobility
and logistics sectors. Section V presents a practical case study
based on the Ethermint consortium blockchain technology
in order to highlight its main advantages and limitations.
Section VI draws and discusses various research challenges
and opportunities. Finally, Section VII concludes the article.
II. BLOCKCHAIN TECHNOLOGY OV ERVIEW
While public blockchains enable parties to make transac-
tions in a secured manner, in trust-less environments, they
show certain limitations when applied to industrial use cases.
Indeed, we believe that aspects, such as controlled data re-
versibility (i), data privacy (ii), transactions volume scala-
bility (iii), system responsiveness (iv) and ease of protocol
updatability (v) that are crucial for the majority of corporate
applications are not covered by public blockchain implementa-
tions. These shortcomings led industrials to develop alternative
blockchain technologies tackling the aforementioned aspects
and intended for restricted audience. These technologies can
generally be classified into two categories: private and consor-
tium blockchains [12]. The distinction between them comes
Figure 1. Blockchain High Level Architecture
down to the governance scheme along with the infrastructure
(see Table I). In private blockchains, one entity rules the whole
system whereas members of consortium blockchains share the
authority among them. Accordingly, the infrastructure is cen-
tralized in case of private blockchains but, similarly to common
distributed databases, data is replicated on multiple nodes that
belong to the single owner. In contrast, consortium blockchains
are deployed in a decentralized manner on multiple hardwares
managed by different owners (or companies). Moreover, data
is not necessary homogeneous among consortium nodes since
some blockchains allow private transactions leading to knowl-
edge fragmentation (i.e., private transactions are shared by
subsets of participants).
Figure 1 shows the typical high level architecture of
blockchain and its main layers. However, it should be noted
that blockchain architecture is not yet standardized, and other
representations exist in the literature, such as [13].
In the following, the term consortium blockchains will
encompass both private and consortium blockchains. In the
next sections, we overview the architecture of both public and
consortium blockchain and highlight their inherent differences.
A. Data structure (Data Ledger Layer)
The data structure of a blockchain, whether public or
consortium, corresponds to a linked list of blocks containing
transactions also referred to as the “ledger”. Each element of
the list, has a pointer to the previous block and embodies its
hash value as illustrated in Figure 2.
This hash value is the key element of the blockchain
integrity, and is computed thanks to a one-way hash function
that maps data of arbitrary size to a non-invertible hash value
of fixed size. Indeed, if an adversary tries to modify the
content of a block, anyone can detect it by computing its
hash and comparing it to the one stored in the next block
to see the inconsistency. In order to avoid this detection,
the adversary could try to change all the hashes from the
tampered block to the latest block. However, this is not feasible
without the consent of a significant proportion of validator
nodes, depending on the consensus algorithm (see Section III).
Hence, the “immutability” characteristic of blockchain: data
are tamper-proof.
On the other hand, consortium chains members can come
to an agreement and alter previous blocks (i). In order to prove
that data were not tampered and preserve the auditability of
the ledger, it is common to periodically publish the hash of
a block onto a public blockchain. By doing so, one can be
assured that blocks in the interval of two published hashes
have not been modified.
Figure 2. List of linked blocks with hash pointers
B. Network and privacy (Network/P2P Exchange Layer)
Along with its data structure, a blockchain is based on a
peer-to-peer network that links its members. Members partici-
pate to the network through their blockchain client node. Each
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TABLE I. Blockchain Classification
Blockchain Governance
Property Public Consortium Private
Governance Type Consensus is public Consensus is managed by a set of participants Consensus is managed by a single owner
Transactions Validation Anynode (or miner) A list of authorized nodes (or validators)
Consensus Algorithm Without permission
(PoW, PoS, PoET, etc.)
With permission
(PBFT, Tendermint, PoA, etc.)
Transactions Reading Any node Any node (without permission) or
A list of predefined nodes (with permission)
Data Immutability Yes, blockchain rollback is almost impossible Yes, but blockchain rollback is possible
Transactions Throughput Low (a few dozen of transactions validated per second) High (a few hundred/thousand transactions validated per second)
Network scalability High Low to medium (a few dozen/hundred of nodes)
Infrastructure Highly-Decentralized Decentralized Distributed
Features
Censorship resistance
Unregulated and cross-borders
Support of native assets
Anonymous identities
Scalable network architecture
Applicable to highly regulated business (known identities, legal standards, etc.)
Efficient transactions throughput
Transactions without fees
Infrastructure rules are easier to manage
Better protection against external disturbances
Examples of technologies Bitcoin, Ethereum, Ripple, etc. MultiChain, Quorum, HyperLedger, Ethermint, Tendermint, etc.
node has a local copy of the whole linked list (or the most
recent part of it in case of light nodes [14]). When retrieving
the list for the first time, a node verifies the integrity of the
blocks by computing all the hashes and keeps verifying each
new block.
Depending on the implementation of the blockchain, the
network can be either public (i.e., anyone can access it) or per-
missioned (i.e., only accounts that are allowed can participate).
This restricted access to the network in consortium blockchains
ensures data privacy (ii). Moreover, some blockchains allows
to control data visibility at a more finer grain by enabling data
encryption at transaction level (e.g., [15]).
The identity of a participant is defined by his cryptographic
asymmetrical key pair. The public key is derived to obtain
his unique address, which serves as his public identity. The
private key is used to sign transactions and guarantee their
authenticity (i.e., other participants can verify the signature
using the associated public key).
In order to add data to the blockchain, a node sends a
transaction request to the network. The prime data fields of
a transaction in most technologies are the addresses of both
sender and receiver, data values that are being communicated
and the signature of the sender. These transactions’ requests are
then picked by some special nodes called miners also referred
as to block generators or validators on consortium blockchains.
C. Security vs scalability (Consensus Layer)
On public blockchains, miners are nodes that are willing
to share their computational power to add blocks to the
blockchain in return of some reward. The way they are
rewarded depends on the implementation of the blockchain
protocol, however, it often involves a fee (i.e., the node who
was asking to add data pays the miner to do it) and/or creation
of value (e.g., in case of Bitcoin, the blockchain mints value
and gives it to the miner who has added a block). Thus, miners
are in competition: they all want to add the next block but only
one or few of them will achieve it in a random way for each
new block.
The process for selecting the actual node that will add the
next block among all the miners is referred as a consensus
protocol. For instance, Bitcoin and Ethereum use the Proof
of Work (PoW) consensus [2], where miners have solve
a computationally demanding cryptographic puzzle to prove
their commitment. This protocol enables the random selection
of the next miner and prevent adversary nodes from adding
fraudulent blocks since their probability to be retained is too
low compared to the procured energy and time to solve the
puzzle. This leads to the 51% attack: if more than half of the
power of the network is allied with the adversary, then his
version of the ledger will always end up being the main one.
In a trust-less public configuration where miners are anony-
mous, this consensus is crucial for the integrity and security
of the data. On the other hand, in consortium chains, val-
idators are preliminary known according to the governance
scheme and are trusted to some degree. Indeed, in majority
of consortium protocols, validators are defined at the genesis
of the blockchain. Some technologies enable the dynamic
addition and retrieval of validators but these actions are always
under the control of the current validators. Therefore, the
security and required computational power can be lowered by
using less demanding consensus algorithms. This reduction
of complexity in the consensus protocol leads directly to
an increased scalability in terms of transactions throughput
(iii). Indeed, consensus such as PoW delay data transmission,
e.g., 10 minutes for each 1Mb block for Bitcoin, whereas
protocols for consortium blockchains can make optimal use of
the network and reach throughput on the order of thousands
transactions per second. An overview of the major consensus
algorithm for consortium blockchains is proposed in Section
III.
Figure 3. Fork
D. Forks and responsiveness
Once a miner’s block has been published, it is added
to the blockchain and the information is broadcast. Due to
network effects, there are cases where multiple miners blocks
are published at the same time. It is also possible that some
miners propose other block versions when they do not agree
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with the content of the most recent one. These cases are called
afork: the blockchain splits into multiple branches. However,
the nodes goal is to converge towards acknowledging a unique
and same version of the blockchain. In practice, the Proof of
Work consensus achieves this result by requiring miners to
work on the longest branch, with the intuition that eventually
the longest branch will be the same for all nodes.
Thus, even if a transaction has been validated, we cannot
be sure that it will remain on the main chain. In Bitcoin, users
usually wait six blocks of confirmation before considering a
transaction (i.e., its block) as valid, that being 6×10min = 1h.
In that sense, transactions are never definitely accepted, there
is only the probability of reversal that decrease exponentially
as the chain grows.
Hence, there is a correlation between the probability of fork
occurrence and the responsiveness of the blockchain (i.e., time
to wait for a transaction to be validated and adopted by the
rest of the network). On consortium blockchains, the use of
adapted consensus algorithm allows for “block finality”: once
a block has been validated, it remains on the main chain and
forks are not allowed. This increases the system responsiveness
by shrinking waiting time for confirmations (iv).
E. Forks and updates
Miners software is sometimes updated to fix bugs or
add functionalities. This also can create forks, as different
nodes might handle transactions differently depending on their
software versions. We usually distinguish:
- “soft forks” where the transactions considered valid by the
new version are also valid for the old version.
- “hard forks” where the transactions considered invalid by the
old version might be valid for the new version.
While it is complicated to synchronize the software of public
blockchains due to the huge amount of anonymous participants
and potential disagreements among them, it is easily feasible
on consortium chains where members know each other and
can quickly come to a mutual agreement (v).
F. Smart Contracts
The concept of smart contract has been introduced by
Ethereum and consists in computer programs, which execu-
tions results are verified by miners. Their deployments and
executions are triggered by users through transactions. Each
node participating to the blockchain has a local virtual machine
(e.g., EVM in Ethereum [16]) in addition to the linked list
and executes smart contracts, according to the transactions
that have been validated and maintain a globally shared
state. By inheriting the security level offered by transactions,
blockchain’s smart contracts protocol benefit from the prop-
erties of the blockchain: security, integrity, no intermediary,
transparency and availability. Some consortium blockchains
offer the possibility to restrict the visibility of these contracts
to a subset of members in the same way as private transactions
(e.g., [17]).
This overview of blockchain architecture and components
highlighted the main differences between public and permis-
sioned implementations. In the next section, we present major
consensus algorithms used in consortium blockchains that
enables high transaction throughput compared to the regular
algorithm such as the Proof of Work or the Proof of Stake.
III. CON SE NS US ALGORITHMS
As we saw in the previous section, the process that adds
blocks to the linked list is a major factor to security and
scalability. This is done with a State Machine Replication
(SMR) algorithm, which makes the network agrees on a
unique, constantly growing, ordered set of transactions. The
consensus algorithm, is the part that makes the nodes agree on
a single piece of data [26], i.e., the block that is going to be
added. However, since they are both very closely related, the
terms are often used interchangeably.
To solve consensus, an algorithm has to provide two
properties: Safety and Liveness. Informally, Safety means that
once nodes confirmed a new entry to the transaction log, it
will not be changed later. Liveness means that if a honest user
has sufficient connectivity and tries to submit a transaction,
then it will eventually get accepted.
The reason why blockchains are deemed to be trustless
is because those algorithms are Byzantine Fault Tolerant
(BFT), that is, other nodes may behave arbitrarily, includ-
ing in malicious ways. Therefore, blockchains users do not
need to trust others. However, it has been proved that no
consensus algorithm can tolerate more than a third of the
nodes being malicious [27]. Note that blockchains may tolerate
more byzantine faults, but will not satisfy the traditional
definition of consensus. This is most often the case with public
blockchains that do not have transaction finality (or block
finality, as mentioned earlier), i.e., transactions are guaranteed
to be irreversible only with a sufficiently high probability.
A. Algorithms
In this section, we describe prominent consensus algo-
rithms that are considered today in consortium blockchains
where miners are designated nodes (i.e., validators). In Table
II, we synthesize their characteristics: Fault tolerance indicates
whether the algorithm tolerates arbitrary (e.g., malicious) be-
havior, or only crash failure. The Threshold further quantifies
the amount of faults tolerated. Confirmation time, also known
as latency, is the time elapsed between the submission of a
transaction, and its definitive acceptance. Throughput is the
number of transactions per second that the system can sustain
as a whole, and Scalability is the number of nodes that can
participate in the consensus. We warn the reader that the
figures for the last three columns are not based on rigorous
experimental studies, but they were collected from online blogs
and websites. Indeed, these performances are not expected to
be reachable simultaneously for the three metrics, and depends
largely on the algorithm configuration and network settings.
a) Practical Byzantine Fault Tolerance (PBFT): PBFT
is the first high performance consensus algorithm with an
optimal byzantine fault tolerance [28]. As such, it has been
widely implemented and tested, and served as a basis for a
wide range of newer algorithms.
This method works with a leader that does the ordering
of transactions. Then consensus is reached in three phases:
First, the leader broadcasts the new request, e.g., transactions
or block (pre-prepare). Then, each validator signs and broad-
casts a prepare message for the request. If enough prepare
messages have been received, then validators broadcast commit
messages, and when enough commits have been received, the
request is finally accepted.
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TABLE II. BENCHMARKING OF CONSENSUS ALGORITHMS
Consensus algorithm Fault Tolerance Threshold Confirmation time Throughput scalability
Tendermint Core Byzantine 33% 5s [18] 10k tx/s [18] 100 nodes [18]
PBFT Byzantine 33% 1s [19] 50k tx/s [19] 30 nodes [19]
Hashgraph Byzantine 33% n/a (claimed 1s) [20] n/a (claimed 100k tx/s) [20] n/a
SCP Byzantine partitioning up to 15s [21] 1-10k tx/s [22] -
PoET Byzantine TEE failure n/a n/a very high [23]
DiversityMining Crash tunable, 50% n/a 1k tx/s [24] n/a
Raft Crash 50% 1s [25] up to 30k tx/s [25] n/a
If at some point the leader does not behave as expected
(e.g., if there was no answer after some timeout), the next
leader in the round robin order takes over through a complex
procedure named view change. Informally, view change can
be seen as a weaker variant of consensus, because nodes need
to reach agreement on the fact that the leader changed.
This algorithm is able to tolerate a maximum of a third
of the nodes being malicious, which is the maximum possible
in this setting. It also makes the assumption that the network
is partially synchronized since nodes need to be able to skip
faulty leaders, i.e., after a timeout. A PBFT implementation is
currently in development for Hyperledger Fabric. Moreover,
IstanbulBFT has been developed as a blockchain-friendly
variant of this algorithm, and is implemented in Quorum [29].
b) Tendermint: Tendermint is a consensus algorithm
that has been designed with applications for consortium
blockchain in mind [30]. It is somewhat similar to PBFT, but
additionally provide safety even when more that one third of
the nodes get corrupted [31]. For each new block, a leader
node is selected in a round-robin manner until one is able to
commit the block. For a block to be committed, it has to gather
more than two third of votes of validators within a given time
period, through a procedure similar to the three-phases commit
from PBFT.
Tendermint-core is provided as a stand-alone consensus en-
gine, and has been implemented to function with the Ethereum
Virtual Machine (EVM) in Hyperledger Burrow and Ethermint.
Other blockchain frameworks based on Tendermint can be
found on [32].
c) Proof of Elapsed Time: Proof of elapsed time is an
initiative that aims at removing the computational cost induced
by the usual proof of work approach, by leveraging Trusted
Execution Environment (TEE) compatible hardware, such as
Software Guard Extensions (Intel SGX).
For each block, miners wait for a given random time. The
first miner for which the waiting time has elapsed is selected to
validate a block before repeating this process. In other words,
the miner with the shortest waiting time is elected as the
leader. To prove that the node actually waited the required
time, the waiting procedure is executed within the TEE, which
produce a proof of its execution [33]. In essence, this is the
same mechanism as in PoW-based consensus, except that the
cryptographic puzzle is replaced by a hardware-enforced wait
procedure. Note that the trusted hardware can also enforce that
all nodes follow the protocol in its entirety (i.e., are honest),
thus making any crash-tolerant protocol withstand malicious
faults as well. However, the precise construct of PoET allows
to keep the advantages of PoW-based consensus, namely, the
great scalability in the number of nodes. This does come with
the same performance cost than public blockchains, since the
waiting delay must be large enough to have a low probability
of a (network-induced) fork.
The downside is that the security is guaranteed only if the
TEE platform vendor is trusted. Moreover, if one is willing
to assume that nodes may still be compromised (e.g., due to
implementation bugs), then only a few number of them would
be sufficient to compromise the whole system [23]. PoET is
primarily used within the Hyperledger Sawtooth platform [34].
d) Stellar Consensus Protocol (SCP): The Stellar Con-
sensus Protocol [35] is the algorithm that was developed to
power the Stellar network. It breaks the usual prerequisite of a
unanimously accepted membership list by letting participants
independently choose the nodes they trust. Each participant
knows some nodes that are considered as trustful, i.e., its
neighborhood, and waits that the majority of them agree on
a new transaction before considering it as valid. Consensus
within a neighborhood is reached by first iteratively nominating
candidate values, then by committing one. Those procedure are
based around a primitive that allows nodes to ratify statements,
which is done with two round of voting.
This approach allows each node to only be aware of a lim-
ited number of neighbors, while enabling an efficient network-
wide consensus. On the other hand, security relies on each
user good configuration, and requires that each neighborhood
sufficiently intersects with each other. Additionally SCP may
be less suited to consortium blockchains, due to its federated
architecture, where one neighbor alone will not be trusted.
Therefore, to actually benefit from SCP, a given blockchain
platform should either anchors to the Stellar network, or try
to spawn a new independent network.
e) Hashgraph: Hashgraph is an asynchronous protocol,
which means that it makes no assumptions on the network
delays [36]. This allows the nodes to be connected through
an unreliable network such as the Internet. Its core idea is
to piggyback the underlying broadcast protocol (i.e., gossip)
to reach consensus. By explicitly recording the network-layer
execution of the broadcast into a graph similar to a blockchain,
it is able to track events that has been sufficiently spread in
the network to reach consensus. The Hashgraph authors show
that if nodes are constantly synchronizing, then a given event
will end up sufficiently deep in the graph so that one can be
assured that a majority of nodes are able to tell that this event
is valid.
Swirlds [37], the company that developed this algorithm,
made available an implementation through a closed source Java
SDK in alpha version. However, at the time of writing there
was no independent benchmark of Hashgraph, which makes
assessing its performances difficult.
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TABLE III. BENCHMARKING OF ENTERPRISE BLOCKCHAIN PLATFORMS AND TECHNOLOGIES.
Company Platform Consensus Algorithm Smart
Contracts
Private
Transactions Popularity «Activity
(Github) «
Coin Sciences Ltd MultiChain DiversityMining No No Low Medium
Quorum Quorum pluggable: IstanbulBFT ¬, Raft EVM Yes High Medium
IBM Hyperledger Fabric pluggable : Kafka Chaincode Yes ®High High
R3 Corda pluggable: Raft, BFT-SMaRt JVM Yes Medium High
SWIRLDS Hashgraph Hashgraph No ¯No Low N/A
Stellar Stellar SCP No No High Medium
ParityTech Parity pluggable: Ethereum, Aura °, Tendermint EVM No High High
Intel Hyperledger Sawtooth PoET EVM No Low High
Monax, Intel Hyperledger Burrow Tendermint core EVM No Medium Medium
All In Bits Inc. Ethermint Tendermint core EVM No Medium Medium
«Rough estimation based on GitHub metrics and online presence (e.g., download count, community hubs activity, etc.)
¬IstanbulBFT is an adaption of PBFT for blockchains
Other consensus to be added, or unofficial: PBFT, BFT-SMaRT, HoneyBadgerBFT
®Stand alone private transactions are not possible, but participants can set up private channels
¯Hashgraph let the transactions semantics be implemented by the application, but they will not be checked during consensus
°Aura is a simple consensus engine developed by ParityTech, but it is not well specified and there is no assessment of its soundness
f) Diversity Mining consensus: DiversityMining is a
consensus algorithm developed by MultiChain [38]. It is based
on leader election, but where PBFT uses timeouts to handle
leader failure, DiversityMining allows a fraction (subject to
a parameter named diversity) of them to claim leadership
independently, directly by broadcasting a block. Thus, the
amount of tolerable faults is directly set by this parameter.
Forks resulting from this process are handled the same way
as in Bitcoin, with the longest-chain rule. As a result this
algorithm does not provide transaction finality and does not fit
the usual definition of consensus in the permissioned model.
Moreover, this algorithm only tolerates crash faults (i.e.,
not malicious nodes) [39] and thus should be compared with
other crash tolerant systems, like Raft [40] or Apache Kafka.
At the time of writing, there was no available assessment of
DiversityMining performances with a high number of nodes.
However, since it exhibits a communication pattern similar
to public blockchains, it can be expected to have viable
performance with large sized network, at least under optimal
conditions.
g) Raft: Raft [40] is a consensus algorithm that has
been designed to be understandable and modular. It tolerates up
to 50% of crashed nodes, which is the maximal threshold for
this kind of faults. It is based on a leader that does the ordering
of transactions. Leader election is triggered by a timeout, but
contrary to PBFT, the timeout is randomized for each server.
As a result, the elected leader (if successful) will be the first
to timeout. Once there is an available leader, it can simply
broadcast transactions to impose its version of the transaction
log. Raft has been widely adopted and has a great number of
implementations, so its robustness and practical performances
are well known [41].
B. Benchmarking Existing Technologies
Blockchain is currently under extensive research and de-
velopment, leading to a high market fragmentation, with more
than 20 different technologies and frameworks, which have
been released by companies, open-source communities and
universities. Table III compares the key characteristics of some
popular blockchain technologies, especially for the context of
enterprise and consortium based case studies. The column
consensus algorithm lists the consensus engine that have a
compatible implementation. In column Smart Contract we give
the mechanism that execute the smart contract (e.g., virtual
machine, specification), if this feature is available. In Private
Transactions, we specify whether the platform allows client
to send transaction whose contents are only available to the
recipients, possibly including their identities. Then we give a
rough estimation of the platform popularity and activity based
on github metrics and online presence (e.g., download count,
community hubs activity, etc.)
Two points should be noted regarding private transactions:
First, if the blockchain allows arbitrary data in transactions, it
is always possible to add encrypted data using the recipients
public key, but then the validators cannot verify the semantics
of the transaction (e.g., double spending), although some
solution exists to this issue [42][43]. Second, in the particular
consortium setting, it is always possible to instantiate new
blockchains for a specific subset of users, which is conceptu-
ally what Hyperledger Fabric does with its channels. However,
this is limited in the sense that anything that happens within
a channel cannot have an influence on something external to
it, e.g., a monetary transaction cannot be redeemed outside the
channel where it has been made, only the members can vouch
for it. In the corresponding column, we state ”Yes” when the
platform has a working implementation of a feature that allows
for some level of privacy, including the mentioned techniques,
”No” otherwise.
As it can be seen from Table II, consensus algorithms in
the classical setting, i.e., that tolerate 33% byzantine nodes,
have similar characteristics: they show good performance but
do not scale well when the number of nodes increase. There are
proposals to address this issue such as SCP or PoET, but they
imply alternative models that are subject to caveats, especially
regarding trust assumptions.
Therefore, an application that requires cooperation from a
small set of distrusting entities will be able to use a more
classical consensus algorithm such as Tendermint or PBFT,
and consequently will have a wide range of choices for a
blockchain framework. Then, the determining factor is related
to the additional features that are specific to each blockchain
platform, such as the ones listed in Table III.
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On the other hand, applications that require a larger net-
work, e.g., in a scenario where each registered user of the
decentralized application needs to participate to the consensus,
the choice will have to involve some performance trade-off. For
instance, PoET may meet the scalability requirements, but not
provide a low enough confirmation time. As a result, it may
be difficult to find a fitting blockchain platform that also have
a consensus algorithm meeting those requirements. Finally,
applications that does not actually need a trustless platform
can rely on crash tolerant systems where scalability is less
an issue. Therefore, the choice for a blockchain framework
will be driven by the specific characteristics of each platform,
similarly to the first case.
IV. APP LI CATIONS AN D CAS E STU DI ES
The advent of the blockchain technology has enabled a
wide range of new applications. In this section, we introduce
a general classification of these applications, followed by
examples of case studies in key domains.
A. Classification
In a centralized world, an ecosystem is organized around
one predominant actor. This architecture has advantages: it is
easy to manage, stable and the responsibilities are clear. But it
also has drawbacks: a monopolistic approach creates barriers to
entry and hinders innovation, it can also result in an unbalanced
value distribution among participants.
With the blockchain, we move towards a distributed ap-
proach with 4 categories of benefits: data traceability for
a new trust paradigm, systems interoperability for process
automation, flexibility for services enhancement, token-based
ecosystems for a shared governance.
1) Data traceability for a new trust paradigm: The
blockchain can be seen as an immutable distributed ledger
where transactions are timestamped by block. The derived
properties (transparency, integrity, immutability) create the
trust conditions that enable a new framework for asset trans-
actions. Indeed, from a vision perspective, the blockchain is to
value exchange what the Internet is to information exchange:
an interconnected network developed, maintained and updated
by the participants for their benefits, a common ground that
is safe, neutral, disintermediated and universal. Based on this
framework, it is now possible to better trace, manage and
monetize data. This offers new perspectives from machine to
machine transactions, to identity attributes sharing, to user-
centric data marketplaces.
2) Systems interoperability for process automation: The
blockchain can also help bringing down domains silos. As
an example, the transport infrastructure will more easily
interoperate with the energy infrastructure. How so? As a
shared ledger, the blockchain creates the conditions for pro-
cess standardization, via shared on-chain data models, smart
contracts and rules, between any actors that would like to
work together. These can be actors within a domain, say a
supplier and a customer, or actors from different domains, as
in the electro-mobility example. Once standardized, processes
can then easily be automated by using smart contracts (also
known as chaincodes in the Hyperledger vocabulary).
3) Flexibility for services enhancement: Based on these
interoperable systems, it is now possible to develop new
features or services. Having access to more shared data and
more standardized processes will definitely help. But the new
operational organization will help too. Because there is no
going through a third party, everyone on the blockchain is
free to implement new features or services directly, at its
own pace (and its own risk). More freedom also means
more responsibilities. But this flexibility is good for services
evolution and improvement. For example, it becomes easy to
create ad-hoc temporary offers. It is also becomes easy to
provide personalized services, by leveraging the shared data
mentioned above.
4) Tokens-based ecosystems for a shared governance:
Beyond the traceability, interoperability and flexibility benefits,
the blockchain can help fundamentally transform ecosystems
by leveraging digital assets, also known as tokens. Indeed,
tokens can be used to track and monetize transactions, but
they can also be considered as a great tool to materialize the
governance rules and maintain an equilibrium among actors.
The minting protocol and the trading rules put in place will
reflect the consortium view on how to steer behaviors and
share the value created. As an example, a green token could
be created and used only in environmental-friendly scenarios.
Going a step further, a decentralized autonomous organization
(DAO) is another way to create new ecosystems. A DAO
is an organization that relies on the blockchain to manage
interactions between participants. Rules are implemented in
smart contracts executed on the blockchain, so the governance
is executed automatically with transparent rules and immutable
actions.
B. Case Studies
While we classified above the blockchain use cases in
several categories, we can now look at concrete examples in
different application domains.
1) Finance and Insurance: The first blockchain applica-
tion was the cryptocurrency Bitcoin. But many use cases
have followed since. As an example, it can be used at the
”data level” to issue and trade assets, such as bonds, in a
decentralized market place (see the proof-of-concept from
Caisse Des Depots in France). At the ”infrastructure level”
Chaincore implements the distributed ledger technology for
clearing and settlement, as a way to lower costs and improve
efficiency. The blockchain can also help with processes such as
KYC (Know Your Customer), by sharing the proof of identity
and not the data itself between banks (see KYC-chain as
an implementation example). At the ”service level”, we can
imagine new offers such as personalized short term insurances
[44], created on the spot and taking into account diverse pieces
of information about the beneficiary. Finally, crowd-sharing an
insurance deductible can be a good DAO application in the
insurance sector.
2) Energy: With the rise of solar panels and other green
sources of energy, the energy production is becoming more
decentralized and offers a promising field for blockchain
applications. As an example, the distributed ledger technology
can be used to certify the data, the source of energy production,
therefore, guaranteeing that it is environment-friendly. It can
also be used to trade energy at the local grid level, between
individual producers and consumers (see the proof-of-concept
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from LO3 Energy in Brooklyn or [45]). We can imagine further
benefits in the home where devices can schedule their energy
charging to optimize costs and exchange data autonomously
between them. This can be a totally new ecosystem model,
where tokens are directly exchanged between parties to incen-
tivize appropriate (green) behaviors.
3) Mobility: In this sector, the distributed ledger technol-
ogy can be used to safely store the car data (for example,
its mileage [9] or certificates [46]). We can also look at
electro-mobility use cases, where the mobility infrastructure
(say electric vehicles) interoperates more easily with the energy
infrastructure (say the charging points). Another application
example is chasyr, which is a blockchain-based ride-sharing
platform that matches passengers and drivers. So, this is
basically an uber-like service, in a decentralized architecture.
One last example would be a decentralized transportation
ecosystem, where people can use a same token to ride on a
bus, rent a bike or carpool, without any central authority to
organize its operation.
4) Logistics: In this sector, the distributed ledger technol-
ogy can be used to track an asset. For example, Everledger
tracks diamonds to ensure their authenticity, Provenance can
track food origin to guarantee its sanitary safety. Another
example would be using a blockchain to create a collaborative
IT system, which matches transporters and customers timetable
for efficient delivery.
V. A PR ACT IC AL CA SE ST UDY - A NALYZI NG T HE
ETH ER MINT TECHNOLOGY
In this section, we provide a detailed study related
to the performance evaluation of the Ethermint consortium
blockchain technology. Being based on Ethereum, this technol-
ogy inherits all the capabilities including the EVM and smart
contracts. Moreover, the consensus relies on the Tendermint
protocol. By combining those two characteristics, a versatile
protocol and a lightweight consensus that remains secure, we
believe that Ethermint is a great candidate for various use
cases.
Although extensive studies have been conducted to assess
the performance of the Tendermint consensus protocol such
as [31] or other blockchain technologies such as [22], to the
best of our knowledge, the literature has not provided yet
any detailed study related to the technical performance of
Ethermint. That is the first reason behind using this technology
for benchmarking. The second reason lies in the fact that many
industrials, such as in [9], are looking for the usage of this
technology for their own use cases. However, they do not
have yet any detailed assessment of the performance of this
technology. Therefore, for the sake of helping them testing this
technology, this study is conducted.
The remainder of this section is organized as follows. First
we give a detailed description of the Tendermint consensus
protocol before presenting Ethermint itself (Sections V-A and
V-B). Then, we specify the experimental setup and the re-
sults regarding the transaction validation speed depending on
multiple factors such as the transaction load and the network
topology (Sections V-C and V-D).
A. Tendermint: Overview and Architecture
Tendermint [30] can be seen as a software for replicating
an application on many machines in a secure and consistent
manner. Tendermint protocol guarantees that machines com-
pute the same state and it can tolerate up to 1/3 of malicious
or failing machines.
From a functional architecture point of view, Tendermint
consists of two main components: a blockchain consensus
engine called Tendermint Core, which is used to ensure that
the same transactions are recorded on every machine in the
same order, and a generic application interface called the
Application blockchain Interface (ABCI) that is used to enable
the transactions to be processed in any programming language.
In contrast to most current blockchain solutions (such as
Bitcoin) that come pre-packaged with built in state machines,
Tendermint can be used for replicating state machines related
to applications written in any programming language or de-
velopment environment. Thanks to these features, Tendermint
has been widely used in a variety of distributed applications
including blockchain platforms such as Ethermint [47] and
Hyperledger Burrow [48].
From a consensus point of view, Tendermint belongs to
the family of BFT (Byzantine Fault Tolerance) consensus
protocols [49]. More precisely, participants in this protocol
are called validators; their main role is to propose blocks of
transactions and vote on them. Blocks come in the form of a
chain and only one block is committed at each height.
As in most consensus protocols, a block may fail to be
committed due to many reasons such as network connectivity
or malicious behaviors. In such a case, the Tendermint protocol
moves to the next round, and a new validator is designed to
propose a block at this height level. The selection of a proposer
is proportional to its validation power. Considering the voting
phase, two stages are required to successfully commit a block;
a pre-vote and a pre-commit. For any block to be committed,
more than 2/3 of validators must pre-commit for it in the same
round. We show in Figure 4 the optimal validation scheme of
transactions using Tendermint.
Commit
New
height
Propose
block
Prevote
block
Precommit
block
+2/3 prevote for
block
+2/3 precommit for
block
Figure 4. Tendermint consensus: optimal workflow
Finally, it should be noticed that Tendermint is overall
qualified as a weakly synchronous protocol since validators
continue to do their work only after hearing from more than
2/3 of the validators set. Furthermore, validators have also to
wait for a small amount of time in order to receive a complete
proposal block from the proposer before voting to move to the
next round.
B. Ethermint: Ethereum + Tendermint
As previously mentioned, Tendermint has a generic appli-
cation interface that enables the transactions to be processed
in any programming language. Indeed, to use Ethereum with a
Tendermint consensus protocol, Ethermint has been developed.
Using Ethermint was first introduced in May 2017, as part of
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Tendermint‘s goal to launch the COSMOS hub [50], the first
blockchain in the Cosmos network, which is a decentralized
network of independent parallel blockchains.
The key idea of Ethermint is to enable Ethereum to run
on top of Tendermint. This allows developers to have all the
nice features of Ethereum, while at the same time benefit from
Tendermints consensus protocol implementation. Tendermint
combined with Ethereum is supposed to result in fast block
times, transaction finality while also getting the goodies of
smart contracts.
In the next sections, the performance of the blockchain
technology Ethermint is considered. This will encompass
explaining the experimental setup, describing the assessment
workflow, as well as, analyzing several indicators related to
the performance of this technology.
C. Experimental Setup
To assess the performance of Ethermint, several parameters
are studied and many performance indicators are considered.
The evaluation process consists of dynamically deploying a
blockchain network on an Openstack virtual machine [51]
having the following properties (20 GB of RAM and 6 Virtual
Central Processing Unit). The used Tendermint version is
0.12.0and 0.5.3for Ethermint.
Parameters used for building the blockchain are: the num-
ber of nodes n, the number of validators vand the network
topology t. By this latter, we mean how nodes are connected
to each other. In this work, the network may be complete (each
node iis directly connected to all other nodes), in the form
of line (each node iis connected to node i+ 1 except for the
last node n), cycle chain (line and the last node is connected
to the first node), enhanced chain (a node iis connected to
nodes i+ 1 and i+ 2; the i+ 2 node of the node n1is the
first node; the i+ 1 node of the last node is the first node and
the i+ 2 node of the last node is the second node).
The above explained topologies are represented in Figure
5. The main reason why the network topology is considered
is due to the fact that the Tendermint consensus protocol lies
in a very important gossiping phase between all nodes in the
network in order to accomplish the validation steps (look at
Figure 4 for more information). Therefore, any topology will
certainly affect the speed at which the information circulates
in the network.
12
1 2 n-1
1 2 n
n
n
n-1
12n-1
n-2
n-1
Chain
Cycle chain
Complete
Enhanced chain n
Figure 5. Network topology
To perform the deployment process, a NodeJS web service
has been developed. The aforementioned parameters are used
as an input for this service. A ready to use blockchain respect-
ing those parameters is generated as an output. More precisely,
the service works as follows: A NodeJS server always listens
on a specific port; once a correct request is received, n
containers are built (ncorresponds to the number of nodes in
the blockchain). Each container will hold a Tendermint node
associated with an Ethermint node.
To deal with the validators for the Tendermint consensus,
the first vTendermint nodes will be selected. Besides, each
node in the validators set will ask for the public key of all other
validator nodes in order to build a common genesis file. This
latter contains the list of validators and the power associated
with each of them. We assume in this work that validators have
the same validating power.
Once the genesis file is ready and successfully shared,
Tendermint nodes will start and so will do Ethermint nodes.
Moreover, the web service will check that all containers are
successfully started and the blockchain is ready to use by
asking for the first block information; a success flag is returned
if and only if the blockchain already starts validating blocks
(in this case empty blocks are generated). Moreover, another
service has been developed in order to destroy the blockchain.
By destroying, we mean removing all containers and data
related to a blockchain.
Besides, a separate Java program that is run on a 8 GB of
RAM is used for interacting with the blockchain, as well as,
sending transactions and computing performance indicators.
As for all Ethereum based blockchains, instances of web3j
client have been used. To assess the blockchain performance,
this program will dynamically create and remove a blockchain
by calling the corresponding services.
Moreover, this program will be in charge of sending
transactions in asynchronous mode (i.e., we do not wait for
the transaction to be validated). The number of transactions to
be sent in second as well as, the sent interval duration and the
dynamic blockchain parameters are considered as input for this
program. The sent transaction consists of adding an element
to a map in the smart contract that is written in Solidity. The
map assigns a random value to the account calling the smart
contract.
At the end of each scenario (i.e., after the assessment
duration), a validator blockchain node is contacted in order
to fetch all validated transactions and map them with the sent
transactions. By doing so, several metrics can be computed
such as the duration between the sending time of a transaction
and the time at which the transaction has been written on the
blockchain (the validation time of a transaction). Furthermore,
the number of transactions that each block contains is com-
puted, as well as, the time between two blocks. We show in
Algorithm 1 the assessment workflow from a high level point
of view.
D. Experimental Results
We start this section by analyzing the impact of both the
number of transactions sent per second, and the number of
validators on the blockchain performance. By this latter, we
mean the number of transactions validated per second. The
network topology in this scenario is fixed to be complete. The
results are compared with the ideal case line where all sent
transactions are validated in one second or less.
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Algorithm 1: Assessment workflow
input : Number of nodes n,
Number of validators v,
Topology of the network t,
Assessment duration d
Number of transactions per second (frequency f)
output: Performance indicators (average transaction validation
time, average block size, number of transactions
validated per second, etc)
1begin
// BlockChain Deployment
2buildBlockChain(n,v,t)
3waitForBlockChainToBeReady()
// Send Transactions
4while duration < d do
5for i= 0; i < f ;i+ + do
6tx PrepareTransaction()
7send(tx)// Send Asynchronously
8
9end
10 sleep(1s)
11 end
// Extract performance indicators
12 ComputeAverageValidationTime()
13 ComputeAverageBlockSize()
14 ComputeAverageTransactionsPerSecond()
15 end
As can be seen from Figure 6, the results show that
more the number of validators increases, more the number
of transactions validated per second decreases. This can be
explained by the fact that more communications are required
to pre-vote and pre-commit a block when the number of
validators becomes important. Besides, it has been noticed that
the number of transactions sent per second has an impact on
the output of the blockchain.
Figure 6. Average transactions per second
More precisely, more transactions sent per second, more
the network accumulates some delays to validate transactions.
For instance, in the worst case when 100 transactions are sent
per second, a network of 1, 2, 3 or 4 validators can almost
validate them in one second or less. However, for a network
containing 8, 12, 16 or 20 validators, the blockchain requires
several seconds to validate all the input.
When it comes to assessing the impact of the size of
validators set on the average transaction‘s validation time, the
results in Figure 7 show that more the number of validators
is important, more the time required to valid a transaction
increases.
For instance, in a network containing 1 validator, the
average validation time is very low (less than 1 second),
however, that increases to approximately two minutes when
20 validators are considered. The confidence interval is also
given in this figure.
Figure 7. Validation time
From both Figures 6 and 7 it can be said that a compromise
has to be made between the number of validators considered,
the number of transactions sent per second and the desired
blockchain performance. More precisely, an additional effort
has to be made in order to select the appropriate number of
validators in the network, and to fix the adequate number of
transactions that the blockchain can deal with.
This usually depends on the use case where the blockchain
is used. For instance, within a blockchain based energy market
place, that requires validating 100 of energy exchange trans-
actions every 20 seconds, it is obvious that it wont be possible
to achieve that using more than 4 validators.
In the following, the impact of the network topology is
studied. As previously mentioned, four topologies are consid-
ered: complete, chain, cycle chain and enhanced chain. To do
the assessment, the number of validators has been fixed at 20
and the input transactions number is varying.
The results in Figure 8 indicate that the topologies are in
the following order (from best to worse) enhanced chain, cycle,
chain, complete regarding their performance in terms of the
number of transactions that have been validated. This can be
explained by the fact that in a complete network, the validator
nodes spend more time in order to synchronize their state with
the rest of the network. As a result, such nodes have less time
in order to accomplish the validation work.
In a chain topology, the time required to spread an infor-
mation (i.e., send or receive votes) is quite high in comparison
with other topologies. In contrast to those latter, the enhanced
chain topology, represents a good compromise between the
spread information time and the synchronization effort. More
precisely, each node is to be synchronized with only four nodes
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(see Figure 5) and the time required to spread information is
more optimized in comparison with other topologies.
In the following the time between two blocks is studied.
The results in Figure 9 show that more the number of val-
idators increases, more the inter-blocks delay increases. This
is coherent with what have been previously obtained. Indeed,
in a network containing an important number of validators, a
validator node requires additional time in order to inform/get
informed about the state of other nodes. That will certainly
delay the validation/creation of new blocks in the network.
Figure 8. Network topology effect
Figure 9. Average inter blocks delays
The last parameter studied in this paper is the size of the
blockchain. By this we mean the actual size of the folder
containing the blockchain information. For this purpose, four
scenarios have been considered. The first one consists of
sending 20 transactions per second during 2 hours to a network
containing 1 validator.
The results in Figure 10 show that the blockchain size
increases with time. After two hours, the final size increases
to approximately 90 Megabytes. When 20 validators are
considered, the size is not so important as in the previous
scenario since few transactions will be validated. As a result,
it can obviously be said that the size of a blockchain is
proportional to the transactions that the blockchain writes. That
is, more validated transactions will obviously result in higher
blockchain size.
A final note is related to the sudden decrease in the size.
This is actually due to a compression mechanism used in
Ethermint. More precisely, at each increase of approximately
35 megabytes, a compress mechanism is applied by which
approximately 15 megabytes are gained.
Figure 10. Blockchain folder data evolution
From the above results, it can be concluded that the
performance of the Ethermint technology will certainly limit its
usage in practice to specific use cases where the time required
to valid a transaction is very high, and the number of validators
joining the network is not very important. Besides, the storage
space can also constitute a bottleneck for this technology to
be used for real world applications. For instance, running the
blockchain for several weeks will certainly be a problematic
for certain use cases.
VI. RESEARCH DIRECTIONS AND OP PO RTU NI TI ES
Blockchain is currently under extensive research and de-
velopment from both the academia and the industry, however,
there are still major challenges to be overcome before mass
market penetration and adoption. In this section, we highlight
major research directions and opportunities that we believe are
important to investigate.
A. Data Analysis and Visualization
A blockchain being no more than a ledger of transactions
between accounts, data from a blockchain can be seen as no
more than nodes connected by occasionally existing multi-
property edges. Under which structural form should they be
tackled depends on the aimed analysis. From a blockchain
network supervision point of view, crucial in a private com-
panies consortium, the relevant data aggregation level is the
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block, with a time-series scheme. From the point of view of
auditing the quality of the user activity, transactions should
be considered the atomic level to investigate, under a graph
scheme, and more specifically under a time-varying graph
(TVG) scheme [52].
The aim of efficiently auditing a blockchain brings several
challenges:
1) Real-time analysis: Because of the possibility of forks,
there is no such thing as absolute reliability of the data
retrieved from the blockchain. It is decreasingly high toward
the most recent blocks data, as one only get the version
of the ledger stored on a node at a given time, so that a
blockchain-specific time-dependent reliability weight has to be
determined. This procedure must be highly dependent on the
chosen consortium governance scheme.
2) Exploitable visual representation of TVG: From a graph
point of view, each edge (transaction) represents a unique
and directed communication bridge between nodes, having an
infinitesimally narrow time-width. To be able to graphically
analyze a blockchain networks, or to compute common graph
indicators such as centrality or community borders, systematic
smart ways to define edges weight based on non-Dirac delta
function in time have to be conceived.
3) Smart contract internal transactions unraveling: Unless
explicitly coded as so, the transactions from and to smart
contracts, or from smart contracts to users, are no written down
in the ledger, and this can be used for transaction obfuscation
allowing token laundering [53], Ponzi scheme [54] or other
uses where the blockchain only serves itself. In order to
determine whether or not blockchain transactions are related to
real-world event, or more generally what it is used for, studies
on specific key quality indicators related to smart contract have
to be conducted.
B. Blockchain Audit
Data immutability is generally put forward when referring
to blockchain technologies. However, as already discussed
in Section II.A, the written data could still be tampered
and the blockchain rebuilt as long as the majority of the
participants (or miners) have reached a consensus. This is
especially true in consortium and private blockchains where
the number of miners is generally limited in comparison with
public blockchains.
In this context, it becomes extremely difficult for a reg-
ulation authority to audit consortium based blockchains and
to check whether the data and transactions have been tam-
pered with or not. A commonly adopted solution consists
in piggybacking data hashes from the consortium blockchain
into the Bitcoin network, by embedding those hashes inside
the OP RETURN field of Bitcoin transactions. However, this
contribute in polluting and increasing the size of the Bitcoin
network with nonsense and non-financial data.
More recently, alternatives solutions have been proposed
to reduce the impact of piggybacking on public blockchains,
including the concepts of side-chains and notary chains whose
main objective is to make it extremely hard for malicious users
and/or the network participants to alter the blockchain data.
C. Governance
The governance in a private blockchain assigns authority
and responsibility among the consortium members. It deter-
mines nodes that will be able to create blocks (i.e., miners), to
read/write data, to contribute in the consensus mechanism (e.g.,
voting for a miner) and/or to participate in decisions for the
system evolution (e.g., operations management [55], software
updates, allow new nodes to join the system etc.). This power
distribution has an impact not only within the system but also
on the business model of the use case.
Costs linked with the system activity such as the system
set-up, its execution or maintenance are shared within the
consortium according to the governance scheme. It also affects
future incomes or losses at a business level since the governing
nodes decide the rules of the system. For example, the majority
of governing nodes can agree to allow the membership of a
new entity within the consortium, and which is competitor of
another member who has no power over this decision. Hence,
this could jeopardize the viability of the system.
The viability of the system can also be affected by the
governance definition. In many cases, to be durable, the
consortium has to be able to grow by allowing new members to
integrate the system. It is the case for example of new services
over blockchain like dematerialized car service books. More
companies join the consortium such as car manufacturers,
car repair shops or insurance companies, the more durable
and available is the system. On the other hand, the power is
dissolved with the growth of consortium.
One should also take into account the impacts on the
business model when building the governance scheme as it
will be discussed in the next section.
D. Incentives and Business Models
Blockchain solves the issues of trust between actors in
situations of exchange where the temptation of cheating is high
by removing this need of trust. Any business model based on
a solution that would not claim to solve a trust issue would
inevitably fail, as its solution could be replaced by a less
constraining and probably already existing centralized system.
In a blockchain whose users are exclusively individuals,
the pecuniary incentives must ensure that, because members
either receive additional incomes or just lessen their expenses,
they find a financial interest in participating to the process.
However, in a consortium of commercial entities, it should
be pointed out that the simple fact not to be part of the
consortium might represent a handicap that could lead to
loss of turnover or customers attrition, because of the latter
attraction to blockchain promises and interest in financial
incentives.
E. Data Privacy
Data privacy is an imperative for enterprise blockchains.
But lets first distinguish anonymity and privacy. A transaction
is considered anonymous if we cannot identify its owner,
whereas a transaction is called private if the object and the
amount of transaction are unknown.
We have seen many schemes on public blockchains to
improve privacy: Stealth Addresses, Pedersen Commitments,
Ring signature, Homomorphic encryption, Zero-knowledge-
proof. No scheme can hide the sender, the receiver and the
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amount at the same time, so we see actual implementations
mixing these techniques in order to achieve the desired level
of privacy. In addition, there are some known drawbacks such
as computational time, so further research is needed. But
we can expect that these initiatives on public blockchains
will drive improvement on enterprise blockchains privacy as
well. Interested readers may refer to [56] for more detailed
information on privacy issues in blockchain.
F. Security
Guaranteeing End-to-End security means identifying vul-
nerabilities and mitigating risks at each element level and at
the system level. This goes beyond looking at the blockchain
building blocks (consensus, distributed network, cryptographic
tools) and includes evaluating the virtual machine, the Smart
Contracts, the Oracle, the user client, the hardware component,
the keys management and PKI, etc. Some areas of research are
the following: Formal verification of smart contracts, Usage of
trusted platform modules for key storage, Identification of the
different types of attack vectors and their counter strategies
(sybil attacks, double spending attacks, distributed denial of
service attacks, botnet attacks, storage specific attacks, cen-
sorship, etc.), Audit (detect issues a priori or a posteriori),
Supervision (detect issues during run time). Interested readers
may refer to [57] for more detailed information on security
challenges in blockchain.
G. Scalability
As usual, there is always a trade-off between costs, security
and performance. Because participants are known in enterprise
blockchains, the scalability issue is therefore easier to solve,
as compared to public blockchains. Yet, in order to achieve
scalability, we first need to keep in mind the usage context
and the performance metrics we want to optimize: transactions
throughput, validation latency, number of participant nodes,
number of validating nodes, energy costs, computation costs,
storage costs or other criteria? As always, remember the trade-
off principle: A round robin consensus algorithm will scale
well, but the participants need to be honest. A PBFT algorithm
can recover from malicious behaviors (up to 1/3) but the
validating nodes should not be too many (tens of nodes at
most) if the system is to work [58]. All in all, scalability is
an active area of research and we can mention some initiatives
such as: fragmenting the global ledger into smaller sub-ledgers
run by sub-groups of nodes, removing old transactions in
order to optimize the storage, using a hierarchy of blockchains
(transactions are done at a higher level and settled optionally
afterwards in the blockchain), and so on.
VII. CONCLUSIONS
The blockchain technology represents a major paradigm
shift in the way business applications will be designed, oper-
ated, consumed and marketed in the near future. In this paper,
we discussed in details the concept of consortium blockchains,
in terms of architecture, technological components and ap-
plications. In particular, we analyzed and compared various
consensus algorithms. An experimental study was then pro-
posed in order to assess the performance of the Ethermint
technology. The performance indicators analyzed are the time
required to validate a transaction, the number of transactions
validated per second, as well as the average inter blocks delay
and the size of the blockchain data folder. Parameters that
have been varied are the number of validators, the number
of transactions submitted per second and the topology of the
network. The results highlighted some limitations in terms
of transactions validation time and storage requirements that
may hinder the usage of Ethermint to deal with some real
world use cases. Finally, we highlighted some major research
challenges that need to be addressed before achieving mass
market penetration, including the issues related to governance,
audit, scalability, incentives, data privacy and security.
ACKNOWLEDGMENT
This research work has been carried out under the leader-
ship of the Institute for Technological Research SystemX, and
therefore granted with public funds within the scope of the
French Program Investissements d’Avenir.
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