CN114866557B - Computing resource sharing platform and method under edge computing and V2V fusion network - Google Patents

Computing resource sharing platform and method under edge computing and V2V fusion network Download PDF

Info

Publication number
CN114866557B
CN114866557B CN202210486575.3A CN202210486575A CN114866557B CN 114866557 B CN114866557 B CN 114866557B CN 202210486575 A CN202210486575 A CN 202210486575A CN 114866557 B CN114866557 B CN 114866557B
Authority
CN
China
Prior art keywords
resource
crr
crp
computing
lbs
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202210486575.3A
Other languages
Chinese (zh)
Other versions
CN114866557A (en
Inventor
刘鹏
景维鹏
符新雨
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Northeast Forestry University
Original Assignee
Northeast Forestry University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Northeast Forestry University filed Critical Northeast Forestry University
Priority to CN202210486575.3A priority Critical patent/CN114866557B/en
Publication of CN114866557A publication Critical patent/CN114866557A/en
Application granted granted Critical
Publication of CN114866557B publication Critical patent/CN114866557B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
    • H04L67/1004—Server selection for load balancing
    • H04L67/1023—Server selection for load balancing based on a hash applied to IP addresses or costs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/04—Payment circuits
    • G06Q20/06—Private payment circuits, e.g. involving electronic currency used among participants of a common payment scheme
    • G06Q20/065—Private payment circuits, e.g. involving electronic currency used among participants of a common payment scheme using e-cash
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
    • H04L67/1004—Server selection for load balancing
    • H04L67/1008—Server selection for load balancing based on parameters of servers, e.g. available memory or workload
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
    • H04L67/1004—Server selection for load balancing
    • H04L67/101—Server selection for load balancing based on network conditions
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
    • H04L67/1004—Server selection for load balancing
    • H04L67/1012—Server selection for load balancing based on compliance of requirements or conditions with available server resources
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/46—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Business, Economics & Management (AREA)
  • Accounting & Taxation (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Finance (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • General Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Computing Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Computer Hardware Design (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Development Economics (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Technology Law (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a computing resource sharing platform and a computing resource sharing method under a mobile edge computing and V2V fusion network, wherein the platform comprises: comprising the following steps: the edge layer is connected with the equipment layer through a wireless data link. The edge layer comprises: the system comprises a local base station LBS and an edge server, wherein the LBS receives vehicle information of CRRs and CRPs and provides network assistance to trigger the execution of intelligent contracts, and the edge server processes data records of vehicles covered by the LBS; the device layer includes: the CRR sends resource request information to the LBS, the CRR sends vehicle information to the LBS, finally, the CRR carries out transaction according to intelligent contracts, the CRR provides computing resources for the CRR, and the CRR pays fees to the CRP. The invention has the advantages that: 1) The safety of resource sharing is ensured, and the utilization rate of the computing resources of the automobile is improved. 2) The energy consumption of the intelligent vehicle for completing the calculation task can be saved. 3) And the optimal strategy is obtained in a shorter time, so that the time cost of algorithm operation is reduced.

Description

Computing resource sharing platform and method under edge computing and V2V fusion network
Technical Field
The invention relates to the technical field of computing resource sharing, in particular to a computing resource sharing platform and a computing resource sharing method under a mobile edge computing and V2V fusion network.
Background
Mobile Edge Computing (MEC) deploys servers at the network edge that can not only provide computing or storage resources for mobile devices, but also overcome the high latency of cloud computing. The technology which is not available in the car networking environment with the ultra-high real-time requirement is combined with the point-to-point communication technology, and the car networking architecture with the V2V and MEC integrated is constructed. In a vehicle-to-vehicle (V2V) MEC network, the intelligent vehicle may even offload its own computing tasks to nearby intelligent vehicles for computing, and the sharing of computing resources between vehicles relieves the pressure [1] of high real-time and computationally intensive applications of normal operation due to insufficient computing resources, such as road condition analysis, navigation, etc.
However, the resource sharing process has some problems, on the one hand, it is difficult to implement a fair and safe incentive strategy in a completely centerless point-to-point environment, encouraging more automobile users to participate in the resource sharing. On the other hand, due to the limited energy source of the vehicle, it is ensured that the energy consumption of both sides is minimized when offloading the computing task to other intelligent vehicles and when performing the computing task. Furthermore, the computing resources of a single vehicle are often insufficient to provide computing services for multiple vehicles. Some of the existing edge computing resource sharing studies do not take the above mentioned problems into consideration.
A blockchain is an open distributed ledger that effectively records transactions between buyers and sellers in a verifiable and distributed manner [2]. The method generally adopts a mode of carrying out resource transaction in a blockchain to stimulate users to participate in resource sharing in MEC so as to achieve the purposes of fairness, safety and user benefit. Fernando [3] et al set up an operator assistance data offloading platform supported by blockchain to implement a rating system for sellers for reliable payment transactions. Li 4 et al set up a blockchain-based resource auction market and propose an iterative double-sided auction scheme to promote both Internet of things devices and edge servers to submit bids at real prices, guaranteeing overall benefits. Liu [5] et al have proposed a secure, decentralized Internet of vehicles data transaction system using blockchain technology, which devised a lending mechanism to support data transactions.
First, they only focus on the benefits of device users through resource transactions, and not on the energy consumption problems in the resource offloading and computing process, which can result in excessive energy consumption by the offloading and computing process. Second, they do not take into account the fact that the resources of the resource provider device are limited and cannot be provided for multiple devices.
In recent years, energy conservation has been a widespread concern, and many efforts have been made to minimize the energy consumption generated by offloading and computation in MECs. Hassija [6] et al propose a blockchain-based point-to-point network gaming model that enables mobile devices to perform computing tasks in an energy-efficient manner by finding the optimal offload time and offload cost policies. The system researches the problem of multi-user partial calculation unloading in a static scene by the aid of the Sheng [7] and proposes a dynamic matching algorithm to minimize the energy consumption of the terminal. Chen et al, in [8], studied a strategy for the allocation of D2D resources with mixed energy harvesting, and used quantum performance particle swarm optimization to maximize energy efficiency. Lin [9] et al consider a multiple-input multiple-output system and propose an iterative algorithm based on a new successive convex approximation that can converge to a locally optimal solution of the original non-convex problem in order to minimize overall user energy consumption while meeting delay constraints.
These efforts have only investigated the offloading energy saving problem in the resource sharing process, and neglecting the resource sharing premise is that the mobile device is willing to provide computing services, without considering how to motivate users to participate in the resource sharing.
Reference to the literature
[1]N.Abbas,Y.Zhang,A.Taherkordi,and T.Skeie,“Mobile edge computing:A survey,”IEEE Internet of Things Journal,vol.5,pp.450–465,Feb 2018；
[2]M.H.u.Rehman,K.Salah,E.Damiani and D.Svetinovic:Trust in Blockchain Cryptocurrency Ecosystem[J].IEEE Transactions on Engine ering Management,2020:1196-1212；
[3]P.Fernando,L.Gunawardhana,W.Rajapakshe,M.Dananjaya,T.Gamage and M.Liyanage,"Blockchain-Based Wi-Fi Offloading Platform for 5G,"2020 IEEE International Conference on Communications Workshops(ICC Workshops),2020,pp.1-6,doi:10.1109/ICCWorkshops49005.2020.9145369；
[4]Z.Li,Z.Yang and S.Xie,"Computing Resource Trading for Edge-Cloud-Assisted Internet of Things,"in IEEE Transactions on Industrial Informatics,vol.15,no.6,pp.3661-3669,June 2019,doi:10.1109/TII.2019.2897364；
[5]K.Liu,W.Chen,Z.Zheng,Z.Li and W.Liang,"A Novel Debt-Credit Mechanism for Blockchain-Based Data-Trading in Internet of Vehicles,"in IEEE Internet of Things Journal,vol.6,no.5,pp.9098-9111,Oct.2019,doi:10.1109/JIOT.2019.2927682；
[6]V.Hassija,V.Saxena,and V.Chamola,"A mobile data offloading framework based on a combination of blockchain and virtual voting,"Software Practice and Experience,2020(3)；
[7]M.Sheng,Y.Wang,X.Wang and J.Li,"Energy-Efficient Multiuser Partial Computation Offloading With Collaboration of Terminals,Radio Access Network,and Edge Server,"in IEEE Transactions on Communications,vol.68,no.3,pp.1524-1537,March 2020,doi:10.1109/TCOMM.2019.2959338；
[8]J.Chen,Y.Zhao,Z.Xu and H.Zheng,"Resource Allocation Strategy for D2D-Assisted Edge Computing System With Hybrid Energy Harvesting,"in IEEE Access,vol.8,pp.192643-192658,2020,doi:10.1109/ACCESS.2020.3032033；
[9]Lin Y D,Lai Y C,Huang J X,et al.Three-Tier Capacity and Traffic Allocation for Core,Edges,and Devices for Mobile Edge Computing[J].IEEE Transactions on Network and Service Management,2018,15(3):923-933；
[10]Li,H.,Pei,L.,Liao,D.et al.BDDT:use blockchain to facilitate IoT data transactions.Cluster Comput 24,459–473(2021)；
[11]R.M.Corless,G.H.Gonnet,D.E.G.Hare,D.J.Jeffrey,and D.E.Knuth,"On the Lambert W function,"Adv.Comput.Math.,vol.5,no.1,pp.329–359,Dec.1996；
[12]Y.Wang,M.Sheng,X.Wang,L.Wang,and J.Li,"Mobile-edgecomputing:Partial computation offloading using dynamic voltage scaling,"IEEE Trans.Commun.,vol.64,no.10,pp.4268–4282,Aug.2016；
[13]C.You,K.Huang,H.Chae and B.Kim,"Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading,"in IEEE Transactions on Wireless Communications,vol.16,no.3,pp.1397-1411,March 2017,doi:10.1109/TWC.2016.2633522；
[14]N.T.Ti and L.B.Le,"Computation offloading leveraging computing resources from edge cloud and mobile peers,"2017 IEEE International Conference on Communications(ICC),2017,pp.1-6,doi:10.1109/ICC.2017.7997138；
[15]C.You,K.Huang,and H.Chae,"Energy efficient mobile cloud computing powered by wireless energy transfer,"IEEE J.Select.Areas Commun.,vol.34,no.5,pp.1757–1771,May 2016。
Abbreviation and key term definitions
Moving edge calculation (Mobile Edge Computing): MEC;
vehicle-to-vehicle (Vehicle to Vehicle): V2V;
local base station (Local Base Station): LBS;
computing resource requestor (Computing Resource Requester): CRR;
computing resource provider (Computing Resource Provider): CRP.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a computing resource sharing platform and a computing resource sharing method in an edge computing and V2V fusion network.
In order to achieve the above object, the present invention adopts the following technical scheme:
a computing resource sharing platform in an edge computing and V2V converged network, comprising: the edge layer is connected with the equipment layer through a wireless data link.
The edge layer comprises: a local base station LBS and edge servers, wherein each LBS is provided with an edge server, the LBS receives vehicle information of CRR and CRP and provides network assistance to trigger the execution of intelligent contracts, and the edge servers process data records of vehicles covered by the LBS;
the device layer includes: the CRR sends resource request information to the LBS, the CRR sends vehicle information to the LBS, finally, the CRR carries out transaction according to intelligent contracts, the CRR provides computing resources for the CRR, and the CRR pays fees to the CRP.
The invention provides a computing resource sharing platform and a computing resource sharing method under an edge computing and V2V fusion network, comprising the following steps:
s1: initializing a system: all car users CRR and CRP participating in transaction and unloading on the platform need to register identities at trusted local base station LBS, becoming legal entities with identities to join the blockchain system.
S2: an intelligent contract that triggers resource trading and offloading: after receiving the vehicle information of CRR and CRP, automatically triggering intelligent contract, executing resource transaction algorithm and energy-saving calculation unloading algorithm to obtain the combined optimal strategy of resource transaction and calculation unloading.
S3: pay using resource currency: based on the resource price derived from the smart contract for the resource transaction, the CRR uses a private key to unlock Unspent Transaction Output (UTXO), and the CRP unlocks the corresponding UTXO with the private key. Resource currency is transferred from the wallet address of the CRR to the CRP.
S4: validating and broadcasting transaction records: after the CRR finishes the resource transaction and uninstallation, the resource transaction records are acquired, and then the complete transactions with the signatures are encrypted and then broadcast to all users on the platform.
S5: performing a consensus process: a new block is built by the highest-offered-score automobile user, consisting of all transactions, including the resource transaction record and the resource offer of the requester. The new block is broadcast to other users, and the user receiving the new block verifies the validity and correctness of the new block according to the hash value and the digital signature. At the same time, each user also needs to verify whether the building block has the highest total amount of resource provisioning and send the verification result to other users. If all the validations are correct, the new data block will be put into the blockchain according to the timestamp.
Further, the resource transaction algorithm includes:
an N-bit CRP and one-bit CRR computing resource transaction model is designed. The utility function of CRR is as follows:
wherein d is max Representing the amount of computing resources required to complete a computing task, a j Is from resource provider CRP j Amount of resources purchased.Representing the purchase intention of the CRR, d min Z to minimize the amount of resources to purchase j Represents a distance factor, p j Representing the price of the resource.
To maximize the utility of CRR, the resource allocation problem is set to P1:
P1:Max U b (a) (2)
C1:0≤a j ≤R j ,j∈N
CRP is processed by j The utility function of (2) is set as follows:
wherein R is j Is CRP j Is used to determine the amount of free resources,is the highest pricing given by CRP, n 1 And n 2 Is a cost factor.
To maximize the utility of CRP, the resource pricing problem is set to P2:
P2:Max U s (p j ) (4)
wherein, C1, C2, C3 and C4 are constraints.
The specific flow of the resource transaction algorithm is as follows:
input: d, d max ，d min ，n 1 ，n 2 ，z＝{z 1 ,...,z j ,...,z N }，N，
And (3) outputting: optimal resource allocation policy a and pricing policy p
1, initializing: a, a j ＝d max /N，i＝1
2:CRP j According to a j Solving P2 and obtaining the latest resource pricing strategy P of CRP new And utility collection
3: iteration:
4：i＝i+1
5: according to p new Solving P1 to obtain new resource allocation strategya new And utility of CRR
6: according to a new Solving P2 to obtain new resource pricing policy P new And utility collection of CRP
7: if it isAnd->Stopping the iteration, otherwise continuing the iteration
8: return a new And p new
Next, the present invention will obtain a resource allocation policy a= { a 1 ,...,a j ,...,a N Carry into the computation offload process, the energy consumption of local computation is:
wherein f l Representing the CPU frequency of the CRR, k is a coefficient related to the chip architecture.
Similarly, the calculated energy consumption of CRP is expressed as:
wherein f j Representing CRR j Is set to the CPU frequency of the memory device. The invention utilizes the relation between the unloading power and the calculation rate to infer the unloading powerThe total energy consumed by the unloading process is therefore:
wherein t is j Indicating offloading to CRP j Time lambda of (a) j Is given to CRP j The inverse of the ratio of the total bandwidth occupied by the bandwidth allocation, B denotes the total bandwidth, h is the channel gain, σ is the variance of the complex Gaussian white noise, c j Is the computational complexity.
The energy consumption minimization problem is expressed as:
P3:Min E(f l ,λ,t)＝E req +E pro +E off (8)
C5:0<f l ≤f l max
C6:t loc ≤T
C8:λ j ≥1
C9:t j ≥0
wherein f l max Represents the maximum CPU frequency, t, of the CRR loc And T represents the time spent by the local computation and the maximum delay acceptable to the CRR. Lambda = { lambda 1 ,...,λ j ,...,λ N And is a bandwidth allocation policy, t= { t 1 ,...,t j ,...,t N And is a time allocation policy. C5-C11 is a constraint.
By inference, get E req The minimum optimal local calculation frequency is:
when solving lambda and t, first a minimum E is obtained off +E pro Lagrangian function of the problem:
wherein mu j And γ is the Lagrangian multiplier. The optimal time allocation strategy is then inferred using the KKT condition as follows:
order theThe invention deduces lambda j The maximum value of (2) is:
at the same time, lambda is represented by gamma j The method comprises the following steps:
wherein W (x) represents a Lanbo function, is W (x) e W(x) Solution of =x.
Further, the energy-saving calculation unloading algorithm is as follows:
input: a, B, n, T, f= { f 1 ,...,f j ,...,f N }
And (3) outputting: optimal offload time allocation t, optimal offload bandwidth allocation lambda
1: initializing:
2: obtained by the formula (11)
3: when sum is h >1 and sum l <1, iterating:
4：γ m ＝1/2(γ h +γ l )
5: updating lambda according to equation (13)
6：
7: if sum is λ <1 is:
8：γ h ＝γ m
9：sum h ＝sum λ
10: if sum is λ >1 is:
11：γ l ＝γ m
12：sum l ＝sum λ
13: returning to t, lambda.
Compared with the prior art, the invention has the advantages that:
1) The transaction process on the blockchain can ensure the safety of resource sharing, effectively motivates users to participate in the resource sharing, meets the experience of the users on the intelligent automobile, and improves the utilization rate of the computing resources of the automobile.
2) The energy-saving unloading strategy obtained by the invention can save the energy consumption of the intelligent vehicles of both the supply and the demand for completing the calculation task, and further encourage the users to participate in sharing while prolonging the service time of the automobile.
3) By applying the method and the device, the optimal strategy can be obtained in a shorter time, and the time cost of algorithm operation is reduced.
Drawings
FIG. 1 is a schematic illustration of a scenario of an embodiment of the present invention;
FIG. 2 is a flow chart of a method of computing resource sharing in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of the resource transaction strategy and the utility provided in the present embodiment;
FIG. 4 is a schematic diagram of the simulation of the calculation offloading strategy and energy consumption provided in the present embodiment;
FIG. 5 is a schematic diagram of the performance of the computing offload optimization algorithm provided by the present embodiment;
FIG. 6 is a schematic diagram of a simulation of the joint optimization strategy and utility provided in this embodiment;
fig. 7 is a schematic diagram of simulation of the joint optimization strategy and energy consumption provided in this embodiment.
Detailed Description
The invention will be described in further detail below with reference to the accompanying drawings and by way of examples in order to make the objects, technical solutions and advantages of the invention more apparent.
The invention uses the Stackelberg game in the resource transaction stage and uses the KKT condition to solve the problem of protruding optimization in the calculation unloading stage. And by simulation with python on an associative computer equipped with a 3.4GHz I7 Intel processor and 20GB memory, the convex optimization algorithm in the scipy optimization package was used.
As shown in fig. 1, the present invention is applied to an MEC-based internet of vehicles scene, where the scene includes: the edge layer is connected with the equipment layer through a wireless data link. The edge layer comprises: the system comprises local base stations LBS and edge servers, wherein each LBS is provided with an edge server, the LBS receives information of intelligent vehicles CRR and CRP and provides network assistance to trigger execution of intelligent contracts, and the edge servers process data records of vehicles covered by the LBS; the device layer includes: the method comprises the steps that a CRR of an intelligent vehicle with short computational resources and a CRP of an intelligent vehicle with idle computational resources send resource request information to an LBS, the CRP sends vehicle information to the LBS, finally, transaction is conducted according to intelligent contracts, the CRP provides computational resources for the CRR, and the CRR pays fees to the CRP.
The flow of the computing resource sharing method in this embodiment is shown in fig. 2, and specifically includes the following steps:
s1, initializing a system: all car users CRR and CRP participating in transactions and offloads on the platform need to register identities at trusted local base station LBS to become legitimate entities with identities to join the blockchain system.
S2, triggering intelligent contracts of resource transaction and unloading: after receiving the vehicle information of CRR and CRP, automatically triggering intelligent contract, executing resource transaction algorithm and energy-saving calculation unloading algorithm to obtain the combined optimal strategy of resource transaction and calculation unloading.
S3, paying by using resource coins: based on the resource price derived from the smart contract for the resource transaction, the resource requester uses a private key to unlock Unspent Transaction Output (UTXO), through which the resource provider unlocks the corresponding UTXO. The resource currency is transferred from the resource requester's wallet address to the resource provider.
S4, verifying and broadcasting transaction records: after the resource requester finishes the resource transaction and uninstallation, the resource transaction records are obtained, and then the complete transactions with the signatures are encrypted and then broadcast to all users on the platform.
S5, executing a consensus process: a new block is built by the highest-offered-score automobile user, consisting of all transactions, including the resource transaction record and the resource offer of the requester. The new block is broadcast to other users, and the user accepting the new block verifies the validity and correctness of the new block according to the hash value and the digital signature. At the same time, each user also needs to verify whether the building block has the highest total amount of resource provisioning and send the verification result to other users. If all the validations are correct, the new data block will be put into the blockchain according to the timestamp.
In the step S2, modeling and calculating a resource transaction and energy-saving unloading model;
in an embodiment, the transaction model for providing computing resources by an N-bit CRP for a 1-bit CRR is as follows:
the utility function of CRR is:
wherein d is max Indicating the need to complete a computing taskCalculating the resource quantity, a j Is from resource provider CRP j Amount of resources purchased.Representing the purchase intention of the CRR, d min Z to minimize the amount of resources to purchase j Represents a distance factor, p j Representing the price of the resource.
To maximize the utility of CRR, the resource allocation problem of this embodiment is expressed as P1:
P1:Max U b (a) (2)
C1:0≤a j ≤R j ,j∈N
reference [4]]In the present embodiment, CRP j The utility function of (2) is set as follows:
wherein R is j Is CRP j Is used to determine the amount of free resources,is the highest pricing given by CRP, n 1 And n 2 Is a cost factor.
To maximize the utility of CRP, the present embodiment sets the resource pricing problem to P2:
P2:Max U s (p j ) (4)
wherein, C1, C2, C3 and C4 are constraints.
In the embodiment, resource allocation and pricing between CRR and CRP are established as a Stackelberg game, a first round of CRR is taken as a leader to firstly put forward a resource allocation strategy, and CRP is taken as a follower to set optimal resource pricing according to the resource allocation strategy published by the CRR so as to maximize benefits of the CRR, namely P2 is solved. In a new round, the CRR adjusts the resource allocation policy to obtain the maximum benefit, i.e., to address P1, according to the pricing policy published by the CRP. In order to obtain a resource transaction policy that satisfies both CRR and CRP, the present embodiment will utilize the following algorithm:
algorithm 1: resource transaction algorithm based on game theory
Input: d, d max ，d min ，n 1 ，n 2 ，z＝{z 1 ,...,z j ,...,z N }，N，
And (3) outputting: optimal resource allocation policy a and pricing policy p
1, initializing: a, a j ＝d max /N，
2:CRP j According to a j Solving P2 and obtaining the latest resource pricing strategy P of CRP new And utility collection
3: iteration:
4：i＝i+1
5: according to p new Solving P1 to obtain new resource allocation strategy alpha new And utility of CRR
6: according to a new Solving P2 to obtain new resource pricing policy P new And utility collection of CRP
7: if it isAnd->Stopping the iteration, otherwise continuing the iteration
8: return a new And p new
In an embodiment, the energy saving calculation offload model is as follows:
the resource allocation policy a= { a to be obtained in this embodiment 1 ,...,a j ,...,a N Carry into the computation offload process, the energy consumption of local computation is:
wherein f l Representing the CPU frequency of the CRR, κ is a coefficient related to the chip architecture. Similarly, the calculated energy consumption of CRP can be expressed as:
wherein f j Representing CRR j Is set to the CPU frequency of the memory device. By using the relationship between the unload power and the calculation rate, the unload power can be deducedThe total energy consumed by the unloading process is therefore:
wherein t is j Indicating offloading to CRP j Time lambda of (a) j Is given to CRP j The inverse of the ratio of the total bandwidth occupied by the bandwidth allocation, B representingTotal bandwidth, h is channel gain, σ is variance of complex gaussian white noise, c j Is the computational complexity.
The present embodiment then represents the energy consumption minimization problem as:
P3:Min E(f l ,λ,t)＝E req +E pro +E off (8)
C5:0<f l ≤f l max
C6:t loc ≤T
C8:λ j ≥1
C9:t j ≥0
wherein f l max Represents the maximum CPU frequency, t, of the CRR loc And T represents the time spent by the local computation and the maximum delay acceptable to the CRR. Lambda = { lambda 1 ,...,λ j ,...,λ N And is a bandwidth allocation policy, t= { t 1 ,...,t j ,...,t N And is a time allocation policy. C5-C11 is a constraint.
By inference, the present embodiment obtains the result of E req The minimum optimal local calculation frequency is:
when solving lambda and t, first a minimum E is obtained off +E pro Lagrangian function of the problem:
wherein mu j And γ is the Lagrangian multiplier. The optimal time allocation strategy is then inferred using the KKT condition as follows:
order theThe invention deduces lambda j The maximum value of (2) is:
while the present embodiment represents lambda by gamma j The method comprises the following steps:
wherein W (x) represents a Lanbo function, is W (x) e W(x) Solution of =x [11 ]]。
In order to find the optimal bandwidth allocation strategy, the following algorithm is designed:
algorithm 2: energy-saving unloading mechanism
Input: a, B, n, T, f= { f 1 ,...,f j ,...,f N }
And (3) outputting: optimal offload time allocation t, optimal offload bandwidth allocation lambda
1: initializing:
2: obtained by the formula (11)
3: when sum is h >1 and sum l <1, iterating:
4：γ m ＝1/2(γ h +γ l )
5: updating lambda according to equation (13)
6：
7: if sum is λ <1 is:
8：γ h ＝γ m
9：sum h ＝sum λ
10: if sum is λ >1 is:
11：γ l ＝γ m
12：sum l ＝sum λ
13: return t, lambda
Finally, the present embodiment refers to the parameter settings in [4], [12-15], creating a simulation dataset. And respectively aiming at the resource transaction strategies, calculating an unloading strategy and carrying out simulation experiments by combining the effects of the strategies.
The effect of the resource trade strategy is shown in fig. 3, where p=optimal p and PTS are the resource pricing strategy and the resource allocation strategy obtained by the algorithm 1 in this embodiment, and compared with the fixed resource pricing and fixed resource trade total amount strategy, the trade strategy in this embodiment can give consideration to the utility of CRR and CRP, and can make all users obtain higher utility.
The effect of the computation offload policy is shown in fig. 4, where COS is the computation offload policy obtained by algorithm 2, and the policy obtained in this embodiment is the most energy-efficient compared to the random computation frequency, random allocation offload time and offload bandwidth.
Algorithm performance as shown in fig. 5, the complexity of the present embodiment is represented by O (n 2 ) Down to O (log) 2 n), the algorithm efficiency is improved.
The effect of the joint policy is shown in fig. 6 and 7, where OCS is the obtained policy of joint resource transaction and computation offload, RAS is the random resource allocation policy, erat is the policy of considering only energy-saving computation offload and not considering user utility, and ROS is the policy of considering only resource utility and not considering energy-saving computation offload. Experimental results prove that the OCS obtained by the embodiment is the only strategy capable of meeting the utility of the intelligent vehicle user and realizing energy-saving calculation unloading, and can effectively stimulate the user to participate in resource sharing.
Those of ordinary skill in the art will appreciate that the embodiments described herein are intended to aid the reader in understanding the practice of the invention and that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.

Claims (2)

1. A computing resource sharing method under an edge computing and V2V fusion network is characterized by comprising the following steps: the method is completed on the basis of a computing resource sharing platform,
the computing resource sharing platform comprises: the edge layer is connected with the equipment layer through a wireless data link;
the edge layer comprises: a local base station LBS and edge servers, wherein each LBS is provided with an edge server, the LBS receives vehicle information of CRR and CRP and provides network assistance to trigger the execution of intelligent contracts, and the edge servers process data records of vehicles covered by the LBS;
the device layer includes: the method comprises the steps that a computing resource requester CRR and a computing resource provider CRP, wherein the CRR sends resource request information to the LBS, the CRP with idle resources sends vehicle information to the LBS, finally, transaction is conducted according to an intelligent contract, the CRP provides computing resources for the CRR, and the CRR pays fees to the CRP;
the computing resource sharing method comprises the following steps:
s1: initializing a system: all car users CRR and CRP participating in transaction and unloading on the platform need to register identities in a trusted local base station LBS, and become legal entities with identity identification to join a blockchain system;
s2: an intelligent contract that triggers resource trading and offloading: after receiving the vehicle information of CRR and CRP, automatically triggering intelligent contract, executing resource transaction algorithm and energy-saving calculation unloading algorithm to obtain the combined optimal strategy of resource transaction and calculation unloading;
s3: pay using resource currency: according to the resource price obtained from the intelligent contract of the resource transaction, the CRR uses a private key to unlock Unspent Transaction Output (UTXO), and the CRP uses the private key to unlock the corresponding UTXO; transferring the resource currency from the wallet address of the CRR to the CRP;
s4: validating and broadcasting transaction records: after the CRR finishes resource transaction and unloading, acquiring a resource transaction record, encrypting the complete transactions with the signature, and broadcasting the encrypted complete transactions to all users on a platform;
s5: performing a consensus process: constructing a new block consisting of all transactions, including a resource transaction record and a resource supply of the requester, by the vehicle user with the highest supply score; the new block is broadcasted to other users, and the user receiving the new block verifies the validity and correctness of the new block according to the hash value and the digital signature; meanwhile, each user also needs to verify whether the building block has the highest total resource supply amount or not and send the verification result to other users; if all the verifications are correct, the new data block will be put into the blockchain according to the timestamp;
the resource transaction algorithm comprises:
designing a computing resource transaction model of N-bit CRP and one-bit CRR; the utility function of CRR is as follows:
wherein d is max Representing the amount of computing resources required to complete a computing task, a j Is from resource provider CRP j Amount of resources purchased at the location;representing the purchase intention of the CRR, d min Z to minimize the amount of resources to purchase j Represents a distance factor, p j Representing a resource price;
to maximize the utility of CRR, the resource allocation problem is set to P1:
P1：Max U b (a) (2)
C1：0≤a j ≤R j ，j∈N
C2：
CRP is processed by j The utility function of (2) is set as follows:
wherein R is j Is CRP j Is used to determine the amount of free resources,is the highest pricing given by CRP, n 1 And n 2 Is a cost factor;
to maximize the utility of CRP, the resource pricing problem is set to P2:
P2：Max U s (p j ) (4)
C3：
C4：
wherein, C1, C2, C3 and C4 are constraint conditions;
the specific flow of the resource transaction algorithm is as follows:
input: d, d max ，d min ，n 1 ，n 2 ，z＝{z 1 ，...，z j ，...，z N }，N，
And (3) outputting: optimal resource allocation policy a and pricing policy p
1: initializing: a, a j ＝d max /N，
2：CRP j According to a j Solving P2 and obtaining the latest resource pricing strategy P of CRP new And utility collection
3: iteration:
4：i＝i+1
5: according to p new Solving P1 to obtain new resource allocation policy a new And utility of CRR
6: according to a new Solving P2 to obtain new resource pricing policy P new And utility collection of CRP
7: if it isAnd->Stopping the iteration, otherwise continuing the iteration
8: return a new And p new
Next, the resulting resources are dividedConfiguration strategy a= { a 1 ，...，a j ，...，a N Carry into the computation offload process, the energy consumption of local computation is:
wherein f l Representing the CPU frequency of the CRR, κ is a coefficient related to the chip architecture;
similarly, the calculated energy consumption of CRP is expressed as:
wherein f j Representing CRR j Is a CPU frequency of (2); the invention utilizes the relation between the unloading power and the calculation rate to infer the unloading powerThe total energy consumed by the unloading process is therefore:
wherein t is j Indicating offloading to CRP j Time lambda of (a) j Is given to CRP j The inverse of the ratio of the total bandwidth occupied by the bandwidth allocation, B denotes the total bandwidth, h is the channel gain, σ is the variance of the complex Gaussian white noise, c j Is the computational complexity;
the energy consumption minimization problem is expressed as:
P3：Min E(f l ，λ，t)＝E req +E pro +E off (8)
C5：0＜f l ≤f l max
C6：t loc ≤T
C7：
C8：λ j ≥1
C9：t j ≥0
C10：
C11：
wherein f l max Represents the maximum CPU frequency, t, of the CRR loc And T represents the time spent by local computation and the maximum delay acceptable to CRR; lambda = { lambda 1 ，...，λ j ，...，λ N And is a bandwidth allocation policy, t= { t 1 ，...，t j ，...，t N -time allocation policy; C5-C11 is a constraint condition;
by inference, get E req The minimum optimal local calculation frequency is:
when solving lambda and t, first a minimum E is obtained off +E pro Lagrangian function of the problem:
wherein mu j And γ is the Lagrangian multiplier; the optimal time allocation strategy is then inferred using the KKT condition as follows:
order theThe invention deduces lambda j The maximum value of (2) is:
at the same time, lambda is represented by gamma j The method comprises the following steps:
wherein W (x) represents a Lanbo function, is W (x) e W(x) Solution of =x.
2. The method for sharing computing resources under an edge computing and V2V converged network according to claim 1, wherein: the energy-saving calculation unloading algorithm is as follows:
input: a, B, n, T, f= { f 1 ，...，f j ，...，f N }
And (3) outputting: optimal offload time allocation t, optimal offload bandwidth allocation lambda
1: initializing:
2: obtained by the formula (11)
3: when sum is h > 1 and sum l When < 1, iterate:
4：γ m ＝1/2(γ h +γ l )
5: updating lambda according to equation (13)
6：
7: if sum is λ < 1:
8：γ h ＝γ m
9：sum h ＝sum λ
10: if sum is λ > 1 then:
11：γ l ＝γ m
12：sum l ＝sum λ
13: returning to t, lambda.
CN202210486575.3A 2022-05-06 2022-05-06 Computing resource sharing platform and method under edge computing and V2V fusion network Active CN114866557B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210486575.3A CN114866557B (en) 2022-05-06 2022-05-06 Computing resource sharing platform and method under edge computing and V2V fusion network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210486575.3A CN114866557B (en) 2022-05-06 2022-05-06 Computing resource sharing platform and method under edge computing and V2V fusion network

Publications (2)

Publication Number Publication Date
CN114866557A CN114866557A (en) 2022-08-05
CN114866557B true CN114866557B (en) 2024-01-23

Family

ID=82634514

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210486575.3A Active CN114866557B (en) 2022-05-06 2022-05-06 Computing resource sharing platform and method under edge computing and V2V fusion network

Country Status (1)

Country Link
CN (1) CN114866557B (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108492550A (en) * 2018-05-17 2018-09-04 济南浪潮高新科技投资发展有限公司 V2X high speeds fleet self-organizing system based on block chain
AU2020101611A4 (en) * 2020-08-02 2020-09-10 Ahirwar, Gajendra Kumar MR 4- layered iot architecture for hyperledger-fabric blockchain based self-managed vehicle ad-hoc networks (vanet)
CN112105062A (en) * 2020-09-16 2020-12-18 云南大学 Mobile edge computing network energy consumption minimization strategy method under time-sensitive condition
CN112512065A (en) * 2020-12-02 2021-03-16 中国刑事警察学院 Mobility aware offloading and migration algorithms in small cell networks supporting MEC
CN113114714A (en) * 2020-11-03 2021-07-13 吉林大学 Energy-saving method and system for unloading large-scale tasks to 5G edge server
CN113676755A (en) * 2021-08-20 2021-11-19 福州极骤智能科技有限公司 Edge calculation video stream transcoding method based on block chain

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11528611B2 (en) * 2018-03-14 2022-12-13 Rose Margaret Smith Method and system for IoT code and configuration using smart contracts

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108492550A (en) * 2018-05-17 2018-09-04 济南浪潮高新科技投资发展有限公司 V2X high speeds fleet self-organizing system based on block chain
AU2020101611A4 (en) * 2020-08-02 2020-09-10 Ahirwar, Gajendra Kumar MR 4- layered iot architecture for hyperledger-fabric blockchain based self-managed vehicle ad-hoc networks (vanet)
CN112105062A (en) * 2020-09-16 2020-12-18 云南大学 Mobile edge computing network energy consumption minimization strategy method under time-sensitive condition
CN113114714A (en) * 2020-11-03 2021-07-13 吉林大学 Energy-saving method and system for unloading large-scale tasks to 5G edge server
CN112512065A (en) * 2020-12-02 2021-03-16 中国刑事警察学院 Mobility aware offloading and migration algorithms in small cell networks supporting MEC
CN113676755A (en) * 2021-08-20 2021-11-19 福州极骤智能科技有限公司 Edge calculation video stream transcoding method based on block chain

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
基于移动边缘计算的V2X任务卸载方案;张海波;栾秋季;朱江;贺晓帆;;电子与信息学报(11);全文 *
移动边缘计算卸载策略综述;吕洁娜;张家波;张祖凡;甘臣权;;小型微型计算机系统(09);全文 *
车辆异构网中基于移动边缘计算的任务卸载与资源分配;张海波;栾秋季;朱江;李方伟;;物联网学报(03);全文 *
车辆网络多平台卸载智能资源分配算法;王汝言;梁颖杰;崔亚平;;电子与信息学报(01);全文 *

Also Published As

Publication number Publication date
CN114866557A (en) 2022-08-05

Similar Documents

Publication Publication Date Title
CN113228600B (en) Method and apparatus for stimulating participation in a mist network
Li et al. Credit-based payments for fast computing resource trading in edge-assisted Internet of Things
Li et al. An online incentive mechanism for collaborative task offloading in mobile edge computing
Gao et al. An integrated contract and auction design for secondary spectrum trading
Qiu et al. Applications of auction and mechanism design in edge computing: A survey
Zhu et al. A blockchain-based two-stage secure spectrum intelligent sensing and sharing auction mechanism
Zhang et al. Truthful auction mechanisms for resource allocation in the Internet of Vehicles with public blockchain networks
Liu et al. Smart contract-based long-term auction for mobile blockchain computation offloading
Shi et al. Mpcstoken: Smart contract enabled fault-tolerant incentivisation for mobile p2p crowd services
CN111949409A (en) Method and system for unloading calculation tasks in electric wireless heterogeneous network
Kong et al. Secure and efficient computing resource management in blockchain-based vehicular fog computing
Lin et al. A novel Latency-Guaranteed based Resource Double Auction for market-oriented edge computing
Li et al. POEM+: Pricing longer for mobile blockchain computation offloading with edge computing
Zhang et al. Electricity trade strategy of regional electric vehicle coalitions based on blockchain
Liu et al. Multi-item auction based mechanism for mobile data offloading: A robust optimization approach
Le et al. Sharing incentive mechanism, task assignment and resource allocation for task offloading in vehicular mobile edge computing
Zhou et al. Blockchain-assisted computation offloading collaboration: A hierarchical game to fortify IoT security and resilience
CN114866557B (en) Computing resource sharing platform and method under edge computing and V2V fusion network
CN111343595B (en) D2D task unloading system and multi-type resource transaction method thereof
Wang et al. A game theory-based computation offloading method in cloud-edge computing networks
Cui et al. Intelligent task offloading algorithm for mobile edge computing in vehicular networks
Wu et al. A truthful auction mechanism for resource allocation in mobile edge computing
Liu et al. Consortium blockchain-based security and efficient resource trading in V2V-assisted intelligent transport systems
WO2018032613A1 (en) Method, device and system for processing d2d throughput
Gao et al. Distributed double auctions for large-scale device-to-device resource trading

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant