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Decentralized Allocation of Geo-distributed Edge Resources using Smart Contracts



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Decentralized Allocation of Geo-distributed Edge
Resources using Smart Contracts
Jinlai Xu, Balaji Palanisamy, Qingyang WangHeiko Ludwig§and Sandeep Gopisetty§
School of Computing and Information, University of Pittsburgh, Pittsburgh, PA 15213, USA
Computer Science and Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
§Almaden Research Center, IBM Research, San Jose, CA 95120, USA
Abstract—In the Internet of Things (IoT) era, edge computing
is a promising paradigm to improve the quality of service for
latency sensitive applications by filling gaps between the IoT
devices and the cloud infrastructure. Highly geo-distributed edge
computing resources that are managed by independent and
competing service providers pose new challenges in terms of
resource allocation and effective resource sharing to achieve a
globally efficient resource allocation. In this paper, we propose a
novel blockchain-based model for allocating computing resources
in an edge computing platform that allows service providers
to establish resource sharing contracts with edge infrastructure
providers apriori using smart contracts in Ethereum. The smart
contract in the proposed model acts as the auctioneer and
replaces the trusted third-party to handle the auction. The
blockchain-based auctioning protocol increases the transparency
of the auction-based resource allocation for the participating edge
service and infrastructure providers. The design of sealed bids
and bid revealing methods in the proposed protocol make it
possible for the participating bidders to place their bids without
revealing their true valuation of the goods. The truthful auction
design and the utility-aware bidding strategies incorporated in
the proposed model enables the edge service providers and
edge infrastructure providers to maximize their utilities. We
implement a prototype of the model on a real blockchain test
bed and our extensive experiments demonstrate the effectiveness,
scalability and performance efficiency of the proposed approach.
Index Terms—Edge Computing, Blockchain, Smart Contract,
Incentive Design, Resource Allocation
In the Internet of Things (IoT) era, edge computing is
a promising paradigm to improve the quality of service for
latency sensitive applications by filling gaps between the IoT
devices and the cloud infrastructure. Edge/Fog Computing [1]–
[8] provides an additional layer of computing infrastructure
for storing and processing data at the edge, allowing low
latency applications to meet their response time requirements
effectively. Edge service providers (SPs) use edge computing
resources available near the end devices (e.g., smart Internet
of thing devices) for scheduling computation tasks to meet
the strict latency requirement of edge computing applications.
However, the state-of-the-art edge platforms are specifically
designed for customized applications for a specific service
provider (SP), rather than a public service/platform that can
be used for various applications by users. While such a model
reduces the complexity of managing the edge computing
resources, it leads to significant inefficiencies including lower
cost-effectiveness. In the current model, the resources available
at the edge are not completely utilized because of the isolation
that exists between service providers. Besides the security
considerations, the technical barriers (e.g., elasticity of the
distributed edge computing platform, seamless computation
migration between the cloud and the edge and geo-distributed
resource management) prevent public edge resource sharing.
This becomes further challenged with financial considerations
such as profitability to run and participate in a public edge
federation, guaranteeing fairness of the platform, monitoring
and auditing the resource usage and payments.
Blockchain-based smart contracts [9] provide a promising
approach to build decentralized, transparent, trustworthy, and
self-organized ecosystems between multiple independent par-
ties. The smart contracts can act as a trusted third-party to
execute the resource allocation algorithms between the edge
service providers and the edge infrastructure providers. A
blockchain-based resource allocation scheme can enhance the
data and execution transparency of the resource allocation
process and enable a transparent payment distribution scheme
among the providers in an autonomous manner. Besides in-
creasing the transparency, smart contracts can make it easier
to establish the relationship and trust between the providers.
Designing a blockchain-based transparent resource allocation
model incurs several challenges including designing appropri-
ate incentive structures to make sure that all the participants
can benefit by participating in the ecosystem.
In this work, we propose, design and implement a decen-
tralized platform for sharing geo-distributed edge resources
among multiple entities. This paper makes the following novel
We first propose a decoupled decentralized resource al-
location model that manages the allocation of computing
resources distributed at the edges to the service providers
that have application demand to use those resources.
We propose a sealed bid double auction protocol based
on decentralized smart contracts with a two-phase sealed
bidding and revealing mechanism.
Previous Block Hash
Blockchain Network
Transaction Hash Root
State Hash Root Receipt Hash Root
Timestamp Nonce
Block header
Block Body (Transactions)
Other Transactions
Smart Contract Record
Smart Contract Record auction.reveal()
Block nBlock n-1 Block n+1
Smart Contract(Auctioneer)
#@version ^0.2.15
def bid(_blindedBid:bytes32,
_weight: uint256):
def reveal(…):
def clearMarket():
def withdraw():
Edge Infra
Provider Edge Infra
Service Provider
Edge Infrastructure
Service Provider
Fig. 1. Blockchain-based Edge Resource Sharing
Based on the protocol, we develop a decentralized
auction-based resource sharing contract establishment
and allocation mechanism that ensures truthfulness and
utility-maximization for the providers.
Finally, we implement a prototype of the proposed model
on a real blockchain test-bed and our extensive ex-
periments demonstrate the effectiveness, scalability and
performance efficiency of the proposed approach.
In the IoT era, the demands for low-latency computing for
latency-sensitive applications (e.g., location-based augmented
reality games, real-time smart grid management, real-time
navigation) have been growing rapidly. Edge Computing pro-
vides an additional layer of infrastructure to fill latency gaps
between the IoT devices and the back-end cloud computing
In current edge computing models, we have cloud providers
that provide geo-distributed cloud infrastructure or edge com-
puting infrastructure as a service. With the development of
cloud-native techniques [10], it makes the migration of ser-
vices (micro-services, containers) easier between datacenters
including the micro datacenters (MDCs) deployed at the edge
layer. Current models also include cloud services that are
extended to support edge computing. For instance, Amazon
Greengrass [11] supports Amazon Lambda [12] and other
AWS services on the edge devices and Azure IoT Edge [13]
extends the Azure cloud services to the edge devices. The
extension motivates the service providers to invest and deploy
their own edge infrastructure at the network edge. However, on
one hand, current models operate by restricting each service
provider to only utilize their own edge infrastructure resources
for providing service. It creates a strong barrier between the
providers and makes the infrastructure investment not only
inefficient but also redundant. On the other hand, the previous
fixed-price model of cloud computing may not be suitable to
be used in the highly diverse and dynamic edge computing
Sharing edge infrastructure has significant benefits to op-
timizing resource usage cost and meeting strict latency re-
quirements. Geo-distributed edge infrastructures that cooperate
together to optimize resource allocation can build a seamless
geo-distributed edge federation platform to provide infrastruc-
ture to a wide range of edge computing applications (e.g.
virtual reality, smart city, big data analytic) that have strict
latency and bandwidth requirements.
In this work, we design a decentralized mechanism that
enables resource sharing among the service providers and
the edge infrastructure providers (Figure 1). The proposed
approach implements a long-term persistent resource sharing
scheme at the edge using decentralized blockchain networks.
In the remaining part of this section, we describe the back-
ground of edge resource sharing and blockchain-based smart
A. Edge Resource Sharing
We assume that the resources are organized in a layered
architecture [14] where the edge infrastructure acts as the
middle layer between the cloud infrastructure and the smart
things. The dense geo-distributed edge infrastructure includes
the MDCs and the smart gateways placed at the edge of the
network, which is located one hop away from the end devices.
We model the edge resource sharing platform similar to the
model described in [8]. The platform includes (i) potential
buyers namely service providers (SPs) who are responsible
to provide services directly to the end users and want to
lease edge resources on demand to increase their revenues,
and (ii) potential sellers namely edge infrastructure providers
(EIP) who operate either the MDCs or the smart gateways that
support multi-tenant resource allocation by running containers.
To model the resource sharing problem at the edge, we
assume that there are |N|SPs that require edge computing
infrastructure to support their services. For simplicity, we
assume that for each SP iN, where Nis the set of the
SPs, there is a quantifiable service demand of the SPs in each
geographic region for every discrete time slot of a day. The
resource requirement is represented by the application con-
tainer [15], [16] (a configured virtual machine integrated with
the service software), which has several requirements such
as CPU consumption, memory size, network bandwidth and
latency requirement. We use λp
i(τ)to represent the workload
coming from a particular location pin time slot τfor SP ias
shown in Figure 2. We assume that there are several EIPs and
each of them handles a large number of highly geo-distributed
MDCs and smart gateways that represent the edge computing
resources. We assume that each MDC or smart gateway can act
as a virtual node dD, which can support the deployment
of several containers. For simplicity, we assume that every
container consumes an equal amount of resources for running
the application service. The capacity of the overall node is
denoted as Cdand it represents the number of containers
that can be run on the node. The final resource allocation
decision can be simplified as a mapping between the edge
resources Cd,dD, and the workloads, λp
which decides the actual node that run the containers to serve
the corresponding workloads from a location p. The decision
problem is NP-hard [8] and in this work, we simplify the
problem by first delineating non-overlapping regions and then
dividing the continuous sharing time into non-overlapping time
slots (e.g., one hour). Thus, each trade (auction) is handled for
a particular region rRand for a particular time slot τ, which
allows the problem to be solved using the proposed auction
mechanism (Section III-C).
The key challenges of the edge resource sharing problem
are two folds: (i) the geo-distributed nature and the dis-
tributed ownership of the edge infrastructure make it hard
to centralize the resource allocation decision and (ii) the
competitive relationship between the EIPs and the SPs make
it challenging to guarantee efficiency and fairness. To tackle
these challenges, we employ blockchain-based smart contracts
to deploy a truthful auction that automatically processes the
bids from the potential buyers and sellers to generate resource
contracts between them guaranteeing both efficiency, fairness,
and decentralization at the same time.
B. Blockchains and Smart Contracts
Blockchain is a distributed ledger that stores transaction
records as a chain of blocks maintained by a set of miners in
the decentralized blockchain network (Figure 1). The miners
mine the blocks to include the transactions to form the
blockchain for the state in which all miners reach an agreement
through a consensus protocol (e.g., proof-of-work (PoW), or
proof-of-stake (PoS)). The architecture of blockchain makes
it possible to achieve decentralization, integrity, auditablility,
transparency and high availability at the same time. Smart
contracts are built on top of blockchain and they allow user-
defined programs to be executed on the blockchain. More
specifically, the smart contract can be treated as a program
deployed on the blockchain network, which resides at a spe-
cific address (generated when deploying) on the blockchain,
including algorithms (functions within a contract) and data
(the state of the smart contract) as shown in Figure 1. To
interact with smart contracts, there are two ways: (i) retrieving
the state or data from the smart contracts which can be
directly restored from the blockchain data without sending
transactions, and (ii) change the state of the smart contracts
which require calling the functions of the smart contract by
sending transactions and the execution is completed after the
transaction is included in the blockchain network. Ethereum is
well-known blockchain network that supports smart contracts.
Ether is the cryptocurrency used on Ethereum. It is held in
and can be transferred between accounts including externally
owned accounts (EOAs) and contract accounts (CAs). An
EOA is determined by a unique public-private key pair owned
by an individual who can use the private key to sign the
transactions sent from the account. CAs are different than
EOAs. Each CA does not have a key pair and is associated
with a deployed smart contract that is activated (deployed) by
an EOA. To execute the smart contract, the EOA that deploys
a new smart contract or calls a function of a deployed smart
contract needs to pay Gas [9] included in the transaction. Gas
can be exchanged from Ether. A transaction in Ethereum is
Station PoP
Station Edge Node
Fig. 2. Regions
a build-in instruction signed by an EOA. Each transaction
specifies several information including the sender’s address,
the receiver’s address and data (e.g., smart contract bytecode,
and a function call with the arguments). Each function call
and its input are included in the transaction so that the output
can be verified by multiple miners with the same input and
the given program. The correctness can be guaranteed by the
miners and the consensus protocol of the blockchain.
In this work, the proposed resource sharing (auction) proto-
col is enforced without trusted third-parties using smart con-
tracts. Additionally, the proposed truthful auction is designed
to guarantee that the bidders will bid with their true valuation
on the goods and thus, it will reduce the complexity of the
auction algorithm. The resource contracts that are persistent
and distributed on the blockchain can be used for assessment,
billing, and auditing. To cooperate with the geo-distributed
utility-aware task scheduling, the edge resource sharing plat-
form can be seamlessly integrated with the existing utility-
aware cloud platform to handle a wide range of applications
with different requirements.
We design the proposed edge resource sharing platform
using blockchain-based smart contracts. The blockchain acts as
the decentralized ledger that stores the trade information (e.g.,
when and where the resource is shared), and the procedure
(smart contracts) of the trade between the buyers and sellers
transparently for all the participants.
A. System Architecture
The architecture of the proposed edge resource sharing
platform is shown in Figure 1. The basic functionality of
the platform is to match the supply and demand of the edge
resources based on the auction algorithm deployed in the
smart contracts. To make the process decentralized, we employ
blockchain-based smart contracts to act as the auctioneer.
As shown in Figure 1, we can see that the decentralized
consensus and mining of the new blocks are controlled by
the miners connected to the blockchain network. Any node
can download the client of the blockchain network and be a
miner of the network. The public blockchain can be audited by
anyone who participates in the blockchain network and accepts
the broadcast. The blockchain is established by a sequence of
Region Coordinator
Region Coordinator
Region Coordinator
Smart Contract (Auctioneer)
Smart Contract (Auctioneer)
Smart Contract (Auctioneer)
bid() Function
Service Provider
Utility Estimate
Contract Manage
Container Provision
Service Provider
Utility Estimate
Contract Manage
Container Provision
Service Provider
Utility Estimate
Contract Manage
Container Provision
Edge Infrastructure Provider
Micro DataCenter
Cost Estimate
Resource Manage
Contract Manage
Micro DataCenter
Cost Estimate
Resource Manage
Micro DataCenter
Cost Estimate
Resource Manage
Edge Infrastructure Provider
Micro DataCenter
Cost Estimate
Resource Manage
Contract Manage
Micro DataCenter
Cost Estimate
Resource Manage
Micro DataCenter
Cost Estimate
Resource Manage
Edge Infrastructure Provider
Contract Manage
Edge Node
Cost Estimate
Resource Manage
Edge Node
Cost Estimate
Resource Manage
Edge Node
Cost Estimate
Resource Manage
Sealed Buy Bids Sealed Sell Bids
reveal() Function
Blockchain Gateway
Blockchain Gateway
Real Buy Bids Real Sell Bids
determine() Function 𝑟,𝜏
Create smart contract
Determine winning bids
Fig. 3. Edge Resource Sharing Framework
blocks, each of them is generated by the consensus mechanism
and connected to its parent block by its hash value. The
block stores all the information related to the state changes
of the blockchain (e.g., transactions between accounts, and
modifications of values in the smart contracts) in their block
body as records of transactions. The smart contracts deployed
on the blockchain can achieve automatic execution of the
algorithms and guarantee the correctness of transactions. In
the proposed system, we assume that there are four types of
entities namely the service providers (SPs), Edge Infrastructure
Providers (EIPs) discussed in Section II, Region Coordinators,
and smart contracts as shown in Figure 3.
We use the notion of divided regions to reduce the com-
plexity of matching the latency or location requirement of the
edge application to the resources available in a certain area.
We assume that the map corresponding to the geographic area
is divided into |R|sub-regions, where Ris the set of all the
regions. The region division can be generated by negotiation
between the EIPs and SPs, which can either be based on the
location of base stations, Point of Presences (PoPs) of Internet
Service Providers (ISPs) or based on administrative divisions
(e.g., counties) as shown in Figure 2. Each region rRonly
includes a specific area and there is no overlap between the
regions. We also assume that the expected workload distribu-
tion for each time slot τis λr
i(τ)for SP iin the region r. The
workload distribution contains all the workloads coming from
the region. We use λp
i(τ)to represent the workload coming
from a particular position pin region r. As we primarily
consider the workload which needs real-time service, λp
is often the upper bound of the workload during the time slot
τfrom position p. In each region, there can be multiple nodes
that serve as the infrastructure. We use Erto represent the list
of nodes which serves in region r. In addition, the nodes that
serve in one region can guarantee the lowest possible latency
of placing the services of the SPs with an average latency, lr,
as they either directly connect to the PoP or base station of the
region, which is one hop from the end devices that send the
requests. With the above assumptions, the edge nodes can be
either MDCs, smart gateways, or even mega datacenters that
satisfy the placement requirement for a certain region.
We use smart contracts to run the resource trading between
the SPs and the EIPs in each region. The smart contract acts
Smart Contract EIP SP
construct bid
reveal determine withdraw
resource contracts
resource contracts
Fig. 4. Decentralized Sealed Bid Double Auction Procedure
as the trusted third party that uses the predefined auction
algorithm to decide the winning bids and establish the resource
contracts. However, as we need both the resource buyers
(SPs) and the sellers (EIPs) to bid in the (double) auction,
both of them are not suitable to create or handle the smart
contract as the owner. Therefore, we assume that there is a
region coordinator for each region. The coordinator creates
the smart contract for the auctions based on the rules (e.g.,
when the participants can bid, and what is the time duration
for the resource contracts for a particular auction). It notifies
the participants the information of the smart contract (e.g.,
address, and interfaces), monitors the smart contract and calls
the function of the smart contract to determine the winning
bids. The coordinator is the owner of the smart contract and the
cost of running the smart contract is paid either by registering
the auction or from the subsidy of the auction.
The auction algorithm and the status is published in the
smart contract. The participants and the coordinator can audit
the status of the smart contract from the blockchain data. The
smart contract acts as the auctioneer and runs the auction au-
tomatically from the predefined auction algorithm and decides
the winning bids. Then, based on the policy, the resource
contracts are established and recorded in the blockchain for
further accounting and auditing when the resources are used.
B. Decentralized Sealed Bid Double Auction Protocol
In this section, we present the proposed resource alloca-
tion techniques for EIPs and SPs to establish relationships
(resource contracts) with each other and explain how smart
contracts help in this process.
We design a sealed bid double auction using smart contracts
on the blockchain that enables the EIPs and the SPs to bid
in the auction with their sealed bids. We assume that for
each region rand each time slot τ, there is an auction that
decides which EIP and SP pairs trade with each other. We note
that all participants and the coordinator can interact with the
blockchain network using blockchain gateway services such
as Infura ( We assume that each EIP and
SP have at least a client that is responsible to interact with
the region coordinators, resource management, and the smart
contracts to control the process of evaluating the resource
# @version ˆ0.2.15
# SealedBidDoubleAuction.vy
struct Bid:
bidder: address
blindedBid: bytes32
deposit: uint256
weight: uint256
value: uint256
# Auction parameters
coordinator: public(address)
biddingEnd: public(uint256)
revealEnd: public(uint256)
# State of the bids
asks: public(HashMap[address, Bid[MAX_BIDS]])
bids: public(HashMap[address, Bid[MAX_BIDS]])
askCounts: public(HashMap[address,uint256])
bidCounts: public(HashMap[address,uint256])
validAsks: public(Bid[MAX_CONTRACTS])
validAskCount: public(uint256)
validBids: public(Bid[MAX_CONTRACTS])
validBidCount: public(uint256)
# Allowed refund map (withdraw or payment)
pendingReturns: public(HashMap[address,uint256])
Fig. 5. Smart Contract Initial Parameters
valuation. They interact with the smart contract (auctioneer)
and record and report the established resource contracts to the
internal resource management for resource allocation and task
The auction procedure consists of five phases as shown in
Figure 4:
Phase 1. Construct:includes the registration and smart con-
tract construction. Before the auction smart contract is created,
all the EIPs and SPs that want to participate need to register
their accounts to the region coordinator through the steps
shown below. The region coordinator will also record the
account information.
Smart Contract Construction
Input: region id r, resource sharing time slot τ, region coordi-
nator account raddr
Output: smart contract address z
Before time T1, for each region rand each time slot τ:
1. The region coordinator creates the smart contract by calling
z=raddr.deploy(Z, T1, T2, T3, r, τ ), where Zis the class
definition of the resource sharing auction.
2. The region coordinator waits for the transaction to be included
in the blockchain network and gathers the address zfor the
smart contract.
3. The region coordinator broadcasts the smart contract tuple <
z, r, τ > to all the participants registered.
We use the notations T1, T2, T3, T4to represent the end time
of the first four phases (construct,bid,reveal, and determine)
as shown in Figure 4. When handling the construction of
the smart contract, the region coordinator will obey the rules
of phase periods. For example, for the construct phase, the
region coordinator will call the constructor function of the
smart contract before T1. It is worth noting that T1, T4are
enforced by the auction protocol which needs to obey the
region coordinator but T2, T3can be enforced directly by
the smart contract which is included in the program of the
smart contract. Based on the negotiated time slot length of
the edge resource sharing period (for example, in an one hour
period), the region coordinator creates a smart contract for
each resource sharing period. Several parameters are initialised
together with the smart contract as shown in Figure 5. It
written in Vyper [17] to include the details. We define a
Bid structure at the beginning of the smart contract and we
initialize the map between the sellers and the sell bids (asks),
the map between the buyers and the buy bids (bids) and
the counts of them. After the smart contract for the auction is
created, the region coordinator will get an address for the smart
contract. Then, both the address and other related information
(e.g., region id, and time slot) are broadcasted to all the
Phase 2. Bid:After the auction smart contract is broadcasted
to all the participants, the participant can fetch the auction
calendar (e.g., when the participants can bid, and reveal)
from the smart contract. It is as mentioned above as noted
by T1, T2, T3. When the bidding period is started, all the
participants who registered can bid to the smart contract. For
each bid, the EIP or SP sends the bid by interacting with the
smart contract by sending a transaction including the function
for bidding, the blinded bid (hash value of the entire bid
with a randomly generated secret), the number of containers
requested or available, and the deposit to the smart contract.
We omit the procedure for the seller (EIPs) to place their sell
bids (asks) as it is similar to the bid procedure shown below.
The deposit of the sell bid (ask) will be used to guarantee that
the resources are preserved for the resource contracts during
the effective time slot.
Bid Procedure
Input: smart contract address z, bid value b, weight c, SP account
After time T1, before time T2:
1. SP igenerates the blinded bid β=hash(b, c, α)with a
randomly generated secret α.
2. SP idecides a deposit value, which is γ=bc+random(b
3. SP iuses its registered account iaddr to send the
bids by interacting with the smart contract zby callingβ , c, {from : iaddr ,value : γ}), where the entries
in the brackets describe the corresponding entries included in
the transaction.
4. The smart contract zrecords the blinded bid information as a
tuple, < β, c, iaddr , γ >.
5. SP iwaits for the transactions to be verified and included in
the blockchain network. Then, it record the bid in the local
bid array Biincluding tuples of the true bids, < b, c, α >.
Phase 3. Reveal:in the reveal phase, the participant needs to
interact with the smart contract to reveal the bids they sent by
sending the real bid to be verified by the smart contract. The
bid value, weight and secret will be sent to the smart contract
for verification. If the stored hash value matches the hash
value of the above tuple, the bid is valid and will be reserved
for the auction. Otherwise the bid will be removed and the
corresponding deposit will be appended to the withdraw fund
list. We omit the procedure for the seller (EIPs) to reveal their
sell bids (asks) as it is similar to the reveal procedure of the
buyers (SPs) shown below. It is worth noting that, in the reveal
phase, it is possible for the bidders to cancel the previous bids
by sending a wrong bid value to the smart contract in the
corresponding bid entry. Therefore, we do not implement the
cancellation functionality in the smart contract and we leave it
to the client to implement it. As only the bidder has the private
key to sign its own bids, the authentication of the blockchain
network can make sure that the denial of service attack (e.g.,
send the wrong bids to the smart contract to cancel others
bids) is hard to conduct.
Reveal Procedure
Input: smart contract address z, bid array Bi, SP account iaddr,
After time T2, before time T3:
1. SP isends the bid array Biwhere each entry includes the bid
value b, weight c, secret αof the bid, to the smart contract
for verification by calling z.reveal(Bi,{from : iaddr }).
2. Smart contract zverifies each bid by the blinded bid hash β
placed in the bid phase. If the buy bid matches the hash value
and the deposit is larger than the total bid value (γbc), it
will be appended to the valid buy bid array, B, stored in the
smart contract shown as an array validBids in Figure 5.
Phase 4. Determine:In the winner determination phase, the
auctioneer (smart contract) decides the winning bids. For
simplicity, we omit the region id rand time slot τin the
following discussion. We use a binary notation xbto denote
whether the buy bid bwins or not (xb= 1 wins, and vice
versa). Similarly, xsdenotes whether the sell bid (ask), s,
wins or not. We denote the buy price as πband sell price
as πs. Similarly, we denote the auction result as two sets,
Xs={xs|sS}and Xb={xb|bB}. Each entry of
the set determines the decision of one sell or buy bid in the
auction. Besides the winners, the algorithm also sets the map
(shown as pendingReturns in Figure 5) between the bidders
and the pending withdraw amounts, which determines how
much fund can be withdrawn for each bidder including the
bid deposit of the fail bids and the overvalued deposit of the
winning bids. The resource contracts are also established in
this phase based on the results of the auction. Each of them
can be denoted as a tuple < i, D,C, τ , πb, πs>, where Dis
the list of sellers (edge nodes) that provides the resources to
SP iin the resource contract and Cis the array of the number
of containers provided by each seller.
Winner Determination Procedure
Input: smart contract address z
After time T3, before time T4:
1. The region coordinator calls z.determine(), the predefined
auction algorithm, in the smart contract to decide the winning
bids, which is discussed in Section III-C. The decision Xb
and Xsare stored in the smart contract along with the initial
resource contracts.
Phase 5. Withdraw:After the auction is closed and the re-
source contracts are established, the participants can withdraw
their remaining funds (e.g., the excess value deposit and the
deposit of the fail bids) from the smart contract.
Withdraw Procedure
Input: smart contract address z, bidder’s account a
For each bidder (either EIP or SP):
1. The bidder calls z.withdraw() with its account a.
2. Smart Contract zverifies the available withdraw
amount pendingReturns[a] defined in Figure 5. If
pendingReturns[a]>0, the fund will be sent back
to account aand set pendingReturns[a]=0 to avoid
double withdrawal.
C. Auction Algorithm
From the Myerson–Satterthwaite theorem [18], we can see
that there are no auction algorithms that can satisfy all of the
four auction properties at the same time namely: (i) Individual
Rationality, which means that no participants should lose from
bidding in the auction, (ii) Weak Balanced Budget, which
means that the auctioneer will not subsidize the auction, (iii)
Truthfulness that ensures that bidding with true valuation is
the dominant strategy of all the bidders and (iv) Economic
efficiency ensures that the good should be finally allocated
to the bidder who values it the most. However, there are
auction algorithms that can satisfy three of the properties with
a bounded loss on the remaining one. McAfee mechanism
[19] can satisfy individual rationality, weak balanced bud-
get, truthfulness with a bounded loss of economic efficiency
(1/min(|B|,|S|)in our problem). In our work, we use the
McAfee mechanism in the auction design. The truthfulness
property guarantees that for the participants whose objectives
are to maximize their utilities, the dominant bidding strategy is
to bid by their true valuation. Based on the above assumption,
we first define the utility of the SP and EIP.
Utility of Service Provider: We model the utility of the SP
to run the service at the edge. Here, we consider services
having higher requirements for latency such as location-based
augmented reality games [20] and intelligent traffic light
control [21]. The utility gain of the SP can be expressed by
the gain in changing the execution of the real-time service
from the cloud to the edge which can be represented by the
i(τ) = f(lr)f(lpi(τ)) (1)
where f(l)is a function which estimates the utility that can
be obtained by providing the service with a latency l. We
assume that the function is a non-increasing function related
to the latency which means that when the latency is increased,
the utility will decrease. Here lpi(τ)represents the latency
between the mega datacenter of SP iand the position p. We
note that the utility gain can be also modeled by other criteria
such as the bandwidth cost (e.g., when moving an aggregator
operator to the edge to reduce the overall bandwidth cost of
moving the data to the cloud).
Utility of Edge Infrastructure Provider For the EIP, its ob-
jective is to earn higher revenue by providing the infrastructure
to SPs. Therefore, the utility for the EIP is obviously the profit
that it can obtain by renting the resource to the SPs. The true
valuation of the resource for the EIP can be defined using
the operating cost of the resources. For each node, the unit
operating cost function Costd(τ)can be defined as the ratio
of the sum of the operating cost of each server and the capacity
of the node:
Costd(τ) = PMd
where Costm
d(τ)is the fluctuating operating cost of server m
in node din time slot τ. To determine the winning bids, we
extend the McAfee mechanism [19] by allowing each bid to
contain both the unit price and the number of containers at the
same time. The group bidding method can save a significant
amount of cost when the auction is running on the smart
The auction algorithm is shown in Algorithm 1. As we can
see, the time complexity is O(max(|B|log|B|,|S|log|S|)). We
omit the region id rand the time slot τin the algorithm
definition. In the algorithm, we can see that the buy bids and
sell bids (asks) are sorted in their natural ordering (ascending
order for sell bids and descending order for buy bids). Then,
we find the break-even index by accumulating either from
the buy bids or sell bids (asks) by counting the number of
containers. When the break-even index is found (the next buy
bid price is lower than the next sell bid price), the supply
is filled (all the possible containers are sold) or we meet the
end of the bid array. Then based on the McAfee mechanism,
we decide the winning bids and the final selling and buying
Algorithm 1 Algorithm for determining the winning bids
1: procedure DETERMINE(B, S)πs, πb, Xs, Xb
2: sort Bin descending order by the bid price
3: re-index Bas B={bi, ci|i[1,|B|]}
4: sort Sin ascending order by the bid price
5: re-index Sas S={sj, cj|j[1,|S|]}
6: set the overall supply Cr=PdErCd
7: set current buy price bas the first bid (highest price) in B
8: set the sell price sas the first sell bid (lowest ask) in S
9: set number of buying containers h= 0, and selling containers k= 0
10: set current index i= 1, j = 1
11: while T rue do
12: if hCrthen
13: h=Cr
14: break
15: else if i+ 1 >|B|or j+ 1 >|S|or bi+1 < sj+1 then
16: break
17: if h>kthen
18: s=sj,k+ = cj,xj= 1
19: j+ +
20: else
21: b=bi,h+ = ci,xi= 1
22: i+ +
23: ρ= (bi+1 +sj+1)/2
24: if bρsthen
25: πs=πb=ρ
26: else
27: xi= 0, xj= 0
28: πb=bi
29: πs=sj
D. Smart Contract Implementation
We implement the smart contract by Vyper [17], which
can run on any blockchains that support Ethereum Virtual
Machine (EVM) (such as Ethereum, Hyperledger Fabric, etc.).
Our choice of using Vyper is due to its security, auditability
and being predictable by implicitly limiting the features of the
16:00 9/30
Phase Time Participants/Executor
Construct 0:00 9/29 coordinator
Bid 0:00-16:00 9/29 EIPs and SPs
Reveal 16:00-23:00 9/29 EIPs and SPs
Determine 23:00-23:59 9/29 coordinator
Withdraw after 23:59 9/29 Everyone
language such as recursive function calls, which may lead to
unpredictable results when interacting with the smart contract.
As Vyper does not support the dynamic array, we set the
size of all the arrays that appear in the smart contract as
64. Limiting the number of bids can decrease the cost of
establishing the smart contract and running the functions.
As our resource contracts can be established before the Ser-
vice Providers actually use the resources, it provides adequate
time for the coordinator to conduct the auction. As shown in
Table I, the auction can be handled a day ahead and the two
phases can be scheduled with in certain time ranges. As the
time range is sufficient for each participant to interact with the
smart contract and to wait for the transaction to be included
in a block, and verify the transaction, the gas price can be set
with a low priority fee or even without setting the priority fee
to save the overall cost.
E. Resource Contract
After the auction is cleared, the resource contracts for time
slot τare established. The length of the time slot τcan be
negotiated by the EIPs and SPs to determine an appropriate
granularity for the resource allocation by considering both
low-cost (e.g., the cost of running the auction on the smart
contract) and efficiency for placing services (e.g., minimizing
the migration when the resource contracts are expired). We
assume the length of the time slot is one hour, and the auction
will be handled on the previous day when the resource will be
used. We present an example in Table I. After the winning bids
are determined, the resource contracts are built between the
EIP and SP pairs one by one, and the buyer i, the sellers D(the
nodes provide the resources), the buying price πb, selling price
πs, the array of the number of containers C(each determines
the resources provided by an edge node), and effective time
slot τare recorded either in the auction smart contract (e.g.,
by a smart contract event) or in a new smart contract that
can track the resource usage on the run. The client of each
EIP and SP will monitor the resource contract record in the
smart contract to negotiate the resource allocation and gather
the payment or provision the tasks.
In this section, we present the experimental evaluation of
the proposed smart contract-based resource allocation imple-
mented and deployed on the real testbed, Rinkeby [22].
A. Setup
In the experiment evaluation, we focus on testing the
performance of the algorithms when they are implemented in
a smart contract and deployed on the real testbed, Rinkeby.
number of bidders
gas cost
Fig. 6. Gas cost of different number of bidders
number of bids
gas cost
Fig. 7. Gas cost of different number of bids
    
Fig. 8. Total gas cost comparison of different
number of bids and different number of bidders
We assume that in each region, multiple EIPs participate as
sellers, multiple SPs participate as buyers and a coordinator
handles the coordination. For each auction, we keep the
default setting as shown in Table II. We estimate the power
# of EIPs 6 # of bids (total) 30
# of SPs 6 # of containers per bid 100
PUE 1.2 electricity cost/operating cost 10%
gas price 20 gwei gas limit (per block) 30M
consumption using a real server model that has the same
performance as that of the IBM server x3550 (2 x [Xeon
X5675 3067 MHz, 6 cores], 16GB) [23]. Each server hosts up
to 5 service containers at a given time. The electricity price
is generated based on the hourly real-time electricity price
from NationalGrid’s dataset [24]. We use the distribution of
the data in 2015 from NationalGrid’s hourly electricity price
to simulate the fluctuation of the real electricity market. We
also set the energy cost to 10% of the overall operating cost
[25] and the Power Usage Effectiveness (PUE) is 1.2. For the
utility model of the service provider, we choose a base rate
similar to that of the a1.large instance (2 vCPUs and 4 GB
memory) of Amazon EC2, which is $0.05 per hour usage.
The linear growth of utility is based on the utility gain of the
latency improvement which is in the 1-100 range.
B. Methodology
In our experiments, we evaluate two kinds of performance:
(i) the performance of the smart contract which includes the
gas cost of different scenarios and participants and function
calls, (ii) the performance of the auction algorithms in com-
parison to other baselines. The evaluation metrics are defined
as follows:
Gas cost: is the measurement of the cost of smart contracts.
It is measured by the EVM which executes the function and
each assembly operation (opcode) has a fixed gas cost based
on its expected execution time.
Social Welfare: is a metric used to evaluate the performance
of the auction. The social welfare can be calculated as the
sum of the true valuation of the winners. The social welfare
measures the efficiency of the auction. It is maximized if the
goods are allocated to the buyers who value them the most.
Subsidy: is the difference between the payment from the
buyers and the payment given to the sellers. The subsidy is
generated based on the auction algorithms. As discussed in
the definition, when it is negative, the auctioneer can gather
fees from the auction, and when it is positive, the auctioneer
needs to subsidize the trade to make up the difference.
C. Smart Contract Performance
As shown in Figure 6, we evaluate the gas cost with
different number of bidders (sellers and buyers). The default
number of bids is 30 as shown in Table II and the bids are
evenly distributed to each bidder using a simple round robin
algorithm. We illustrate the breakdown of the gas cost in five
phases: (i) construct, in which the coordinator creates the
smart contract, (ii) bid, in which the participants bid, (iii)
reveal, in which the participants reveal the sealed bid, (iv)
determine, in which the smart contract determines the winning
bids by the auction algorithm, and (v) withdraw, in which the
coordinator and participants withdraw their funds. As shown in
the results, we can see that when there are more participants,
the overall gas cost has only a small increase and it only
influences the reveal and withdraw phases as it increases the
number of reveal and withdraw function calls. In Figure 7,
the impact of the number of bids is evaluated. The setup is
similar to the above experiment and we fixed the number of
bidders to 12. We can see that with increasing the number of
bids, the gas cost increases significantly especially for the bid,
reveal and determine phases. The reason is that the number
of bids influence the time complexity of each function call in
the three phases. The influence of the number of bidders and
bids are illustrated in Figure 8. We can get similar conclusion
that the overall number of bids influences the gas cost much
more than the number of bidders.
We also evaluate the gas cost for different participants and
function calls in Figure 9 and 10. As shown in Figure 9, we
can see that most of the gas cost is paid by the coordinator and
the participants only pay gas cost when they bid or reveal the
bids. As we design the auction mechanism based on McAfee
mechanism, the coordinator has the opportunity to get payment
from the auction and the participants can also pay management
fees to the coordinator to cover such cost. In Figure 10, we
observe similar results that show that construct and determine
phases cost most of the gas. As shown in Table III, we convert
the gas cost to real ether cost using the price listed in July 2021
(1 ether=$1787). As we can see, the coordinator needs to pay
nearly 240 dollars to complete one auction, which is relatively
high for the current market but there are many alternatives
that can decrease the cost. For example, the coordinators can
phase gas cost cost in $
mean min 25% 50% 75% max mean
construct 3400175 3400175 3400175 3400175 3400175 3400175 121.52
bid 126469 120002 120026 120056 137126 137156 4.52
reveal 433973 207976 218673 355149 491625 1873449 15.51
determine 3284284 1457251 2371536 3318685 4291935 5273596 117.38
withdraw 26899 23458 23458 28465 28465 28465 0.96
gas cost
Fig. 9. Gas cost of different partici-
construct bid reveal determine withdraw
function call
gas cost
Fig. 10. Gas cost distribution of each
function call
build a private blockchain (e.g., by using Hyperledger Fabric
or Ethereum 2.0) to decrease the operating cost of running the
smart contract. Each bidder may need to pay $4.5 for one bid
and $15.5 for revealing all the bids (linear to the number of
bids) on average. The withdrawal only needs less than $1.
D. Auction Performance
In this experiment, we test the performance of different
auction algorithms, in terms of social welfare and subsidy.
We compare the following auction algorithms:
McAfee [19]: is the auction mechanism we use in our method.
It guarantees both truthfulness and weak balanced budget at
the same time.
OPT: is a straightforward auction mechanism that always
chooses the highest buy bids and lowest sell bids to trade.
It is named as optimal single price omniscient (OPT) [26]. It
can always guarantee balanced budge but not truthfulness.
VCG: is a well-known auction mechanism [27] that guarantees
truthfulness but not balanced budget.
In Figure 11, we evaluate the social welfare of the above
three auction algorithms with a different number of bids. We
can see that all of the three methods have similar social welfare
in different setups. From the theoretic aspect, only Mcafee
may lose social welfare in the second condition (as shown
in Algorithm 1) and because the bids are evenly distributed,
the probability of the occurrence of the second condition is
low. The result is similar when we increase the number of
containers being traded in each bid as shown in Figure 12.
When the subsidy is considered in the evaluation ( Figure 13
and 14), we can see that VCG will suffer from positive
subsidy and the coordinator needs to subsidize the trade.
However, McAfee mechanism has weak balanced budget and
the subsidy can be only negative. It means that this can be one
possible way for the coordinator to gather fees and mitigate
the operating cost of running the smart contracts. OPT always
has balanced budget and the subsidy is always zero.
number of bids
social welfare
Fig. 11. Social welfare of different
methods with different number of bids
number of containers per bid
social welfare
Fig. 12. Social welfare of different
methods with different number of con-
tainers per bid
number of bids
Fig. 13. Subsidy of different methods
with different number of bids
number of containers per bid
Fig. 14. Subsidy of different methods
with different number of containers
per bid
Edge Computing has gained a lot of attention from the
system community in the recent years. Resource allocation
and management is a fundamental and important aspect in
edge computing domain. Do et al. [28] propose a system for
allocating fog computing resources to minimize the carbon
footprint. Wang et al. [29] propose a mechanism to allocate
the edge network and computing resources based on a deep
reinforcement learning method. However, the above efforts
do not consider the resource sharing problem between the
different entities and they may suffer from inefficient resource
usage due to not considering the resources from other entities.
To solve the problems above, there have been solutions based
on mechanism design (auction) to design resource sharing
protocols between different entities. Xu et al. [8] proposed a
framework to support sharing of edge devices among mul-
tiple communities by incentivizing the utility gain of the
participants, which contains the results to complement the
system performance aspects (latency, utilization, etc.) of the
current work. However, the centralized auction handled by
the infrastructure provider may lead to trust concern for the
participants. Sun et al. [30] proposed two double auction
algorithms to determine the matched pairs between the IoT
devices and edge servers. Here, the usage of the above auction-
based methods may be limited by their centralized trust
assumption which requires all the participants to trust the
centralized auctioneer. There have also been several efforts on
decentralized auction-based mechanisms to allocate the edge
resources. Zavodovski et al. [31] proposed DeCloud which
uses blockchain for managing the auction mechanism of edge
resource usage using a weighted matching mechanism. Lin et
al. [32] proposed a hierarchical real-time auction mechanism
for allocating the resources. The real-time auction described
in this work may not possible to be deployed in the public
blockchain network and it may consume high transaction fees.
In contrast to these related efforts, the proposed work develops
a decentralized resource allocation platform, which enables
long-term resource sharing between the ECIPs and SPs to
increase their utilities based on a smart contract enabled double
auction mechanism.
In this paper, we propose a blockchain-based auction for
allocating computing resources in an edge computing platform
that allows service providers to establish resource sharing con-
tracts with edge infrastructure providers using smart contracts
in Ethereum. The proposed auction protocol decentralizes
the trust using the blockchain network and reduces the trust
concerns of centralized auction and the risks associated with
a single point of failure. We implement a prototype of the
proposed model on a real blockchain test-bed and our exten-
sive experiments demonstrate the effectiveness, scalability and
performance efficiency of the proposed approach.
This work is partially supported by an IBM Faculty award
for Balaji Palanisamy. Qingyang Wang acknowledges the
partial support from the NSF CISE’s CNS-2000681 grant.
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Mobile edge computing (MEC) yields significant paradigm shift in industrial Internet of things (IIoT), by bringing resource-rich data center near to the lightweight IIoT mobile devices (MDs). In MEC, resource allocation and network economics need to be jointly addressed to maximize system efficiency and incentivize price-driven agents, whereas this joint problem under the constraints of locality, i.e., an edge server can only serve multiple IIoT MDs in the vicinity constrained by its limited computing resource. In this paper, we investigate the joint problem of network economics and resource allocation in MEC where IIoT MDs request offloading with a claimed bid and edge servers provide their limited computing service with an ask. Particularly, we propose two double auction schemes with dynamic pricing in MEC, namely a breakeven-based double auction (BDA) and a more efficient dynamic pricing based double auction (DPDA), to determine the matched pairs of IIoT MDs and edge servers and pricing mechanisms for high system efficiency, under the constraints of locality and limited computing resource of edge servers. Through theoretical analysis, both algorithms are proved to be budget-balanced, individual profit, system efficient, and truthful. Extensive simulations have been conducted to evaluate the performance of the proposed algorithms and the simulation results indicate that the performance of DPDA and BDA can significantly improve the system efficiency of MEC. IEEE
Augmented (AR) and Virtual Reality (VR) technologies are increasingly being used in manufacturing processes. These use real and simulated objects to create a simulated environment that can be used to enhance the design and manufacturing processes. Virtual Reality and Augmented Reality Applications in Manufacturing is written by experts from the world’s leading institutions working in virtual manufacturing and gives the state of the art of the field. Features: - Chapters covering the state of the art in VR and AR technology and how these technologies can be applied to manufacturing. - The latest findings in key areas of AR and VR application to manufacturing. - The results of recent cross-disciplinary research projects in the US and Europe showing application solutions of AR and VR technology in real industrial settings. Virtual Reality and Augmented Reality Applications in Manufacturing will be of interest to all engineers wishing to keep up-to-date with technologies that have the potential to revolutionize manufacturing processes over the next few years.
The cloud is migrating to the edge of the network, where routers themselves may become the virtualisation infrastructure, in an evolution labelled as \the fog". However, many other complementary technologies are reaching a high level of maturity. Their interplay may dramatically shift the information and communication technology landscape in the following years, bringing separate technologies into a common ground. This paper o ers a comprehensive definition \the fog", comprehending technologies as diverse as cloud, sensor networks, peer-to-peer networks, network virtualisation functions or configuration management techniques. We highlight the main challenges faced by this potentially break-through technology amalgamation.