CN114866557A - Computing resource sharing platform and method under edge computing and V2V converged network - Google Patents
Computing resource sharing platform and method under edge computing and V2V converged network Download PDFInfo
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Abstract
The invention discloses a computing resource sharing platform and method under a mobile edge computing and V2V converged network, wherein the platform comprises: the method comprises the following steps: the edge layer is connected with the equipment layer through a wireless data link. The edge layer includes: the system comprises a local base station LBS and an edge server, wherein the LBS receives vehicle information of CRR and CRP and provides network assistance to trigger the execution of an intelligent contract, 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 CRP with free resources sends vehicle information to the LBS, and finally carries out transaction according to an intelligent contract, the CRR provides computing resources for the CRR, and the CRR pays the expense 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) The optimal strategy is obtained in a shorter time, and the time cost of algorithm operation is reduced.
Description
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 converged network.
Background
Mobile Edge Computing (MEC) deploys servers at the edge of the network, which not only can provide computing or storage resources for mobile devices, but also can overcome the high latency brought by cloud computing. The technology which is unavailable in the car networking environment with the requirement of ultrahigh real-time performance is combined with the point-to-point communication technology to build a car networking architecture of V2V and MEC fusion. In the MEC network from vehicle to vehicle (V2V), the intelligent vehicle can even unload the calculation task of the intelligent vehicle to the nearby intelligent vehicle for calculation, and the calculation resource sharing among the vehicles relieves the pressure of normal operation of calculation-intensive applications such as road condition analysis, navigation and the like caused by insufficient calculation resources [1 ].
However, the resource sharing process has some problems, on one hand, it is difficult to implement a fair and safe incentive strategy in a completely centerless peer-to-peer environment, and more automobile users are encouraged to participate in resource sharing. On the other hand, due to the limited energy of the vehicle, the energy consumption of both parties is to be 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 existing edge computing resource sharing studies do not comprehensively consider the above-presented problems.
The blockchain is an open, distributed ledger that effectively records transactions between buyers and sellers in a verifiable and distributed manner [2 ]. Generally, a resource transaction mode in a block chain is adopted to encourage users to participate in resource sharing in the MEC, so that the purposes of fairness, safety and satisfaction of user benefits are achieved. Fernando [3], et al, established an operator-assisted data offload platform supported by a blockchain to implement a rating system for sellers to perform reliable payment transactions. Li 4 et al established a resource auction market based on block chains and proposed an iterative bilateral auction scheme to prompt both the Internet of things devices and the edge server to submit bids at a true price to ensure overall benefits. Liu [5] et al proposed a secure, decentralized Internet of vehicles data transaction system using blockchain technology, which designed loan mechanisms to support data transactions.
First, they focus only on the benefits to the device user through resource trading and do not address the energy consumption issues in the resource offloading and computing process, which can result in excessive energy consumption by the offloading and computing process. Second, they do not consider the situation where the resource provider device has limited resources and cannot provide resources for multiple devices.
In recent years, the problem of energy saving has been a great concern, and many efforts have been made to minimize the energy consumption generated by offloading and calculation in MECs. Hassija [6] et al propose a game model of a peer-to-peer network based on a block chain, and enable a mobile device to complete a calculation task in an energy-saving manner by finding an optimal unloading time and unloading cost strategy. Sheng [7] et al studied the problem of multi-user partial computation offload in static scenarios and proposed a dynamic matching algorithm to minimize terminal energy consumption. Chen et al studied the resource allocation strategy of D2D with mixed energy harvesting in [8], using quantum-behaved particle swarm optimization to maximize energy efficiency. Lin [9] et al consider a multiple-input multiple-output system, and in order to reduce overall user energy consumption to the greatest extent and satisfy delay constraints, propose a new iterative algorithm based on successive convex approximation, which can converge to the local optimal solution of the original non-convex problem.
These efforts only address the problem of offloading energy savings during resource sharing, while ignoring the premise that mobile devices are willing to provide computing services, not considering how to incentivize users to participate in 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。
Abbreviations and Key term definitions
Moving Edge Computing (Mobile Edge Computing): an MEC;
vehicle to Vehicle (Vehicle to Vehicle): V2V;
local Base Station (Local Base Station): LBS;
computing Resource Requester (Computing Resource request): CRR;
computing Resource Provider (Computing Resource Provider): CRP.
Disclosure of Invention
The invention provides a computing resource sharing platform and a method in a converged network of edge computing and V2V, aiming at the defects of the prior art.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
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 includes: the system comprises a local base station LBS and an edge server, wherein each LBS is provided with one edge server, the LBS receives vehicle information of CRR and CRP and provides network assistance to trigger the execution of an intelligent contract, 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 CRP with free resources sends vehicle information to the LBS, and finally carries out transaction according to an intelligent contract, the CRR provides computing resources for the CRR, and the CRR pays the expense to the CRP.
The invention provides a computing resource sharing platform and a method under a fusion network of edge computing and V2V, which comprises the following steps:
s1: initializing a system: all automobile users CRR and CRP participating in transaction and unloading on the platform need to register identities at a trusted local base station LBS, and become legal entities with identity identifications to join the blockchain system.
S2: triggering intelligent contracts for resource trading and unloading: and after vehicle information of the CRR and the CRP is received, automatically triggering an intelligent contract, and executing a resource transaction algorithm and an energy-saving calculation unloading algorithm to obtain a joint optimal strategy of resource transaction and calculation unloading.
S3: paying using the resource currency: according to the resource price obtained from the intelligent contract of the resource Transaction, the CRR uses a private key to unlock the Unpend Transaction Output (UTXO), and the CRP unlocks the corresponding UTXO through the private key. The resource currency is transferred from the wallet address of the CRR to the CRP.
S4: validating and broadcasting transaction records: and after the CRR finishes resource transaction and uninstallation, acquiring resource transaction records, encrypting the complete transactions with the signatures and broadcasting the encrypted transactions to all users on the platform.
S5: executing a consensus process: a new block consisting of all transactions, including resource transaction records and resource offerings of the requester, is constructed by the vehicle user with the highest offering score. The new block is broadcast to other users, and the user who accepts 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 amount of resource provision and send the verification result to other users. If all verifications are correct, new blocks of data will be placed into the blockchain according to the time stamps.
Further, the resource trading algorithm comprises:
a computing resource transaction model of N CRPs and one CRR is designed. The utility function of the CRR is as follows:
wherein d is max Representing the amount of computing resources required to complete a computing task, a j Is from a resource provider CRP j The amount of resources purchased.
Indicating the purchase intention of the CRR, d min To minimize the amount of resources that need to be purchased, z j Denotes the 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 j The utility function of (a) is set as follows:
wherein R is j Is CRP j The amount of free resources of the network,
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 constraint conditions.
The resource transaction algorithm comprises the following specific processes:
inputting: 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, initialization: a is j ＝d max /N，
i＝1
2:CRP j According to a j Solving P2 to obtain the latest resource pricing strategy P of CRP new And utility collections
3: iteration:
4：i＝i+1
5: according to p new Solving P1 to obtain new resource allocation policy a new And the utility of CRR
6: according to a new Resolution P2 obtaining a new resource pricing policy P new And a utility set of CRPs
7: if it is not
And is
Stopping the iteration, otherwise continuing the iteration
8: return to a new And p new
Next, the present invention sets the obtained resource allocation policy a ═ { a ═ a 1 ,...,a j ,...,a N Substituting the computation unloading process, wherein the energy consumption of local computation is as follows:
wherein f is l Denotes 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 is j Represents CRR j The CPU frequency of (c). The invention utilizes the unloading power and the calculation rateThe relationship between them, deducing the unloaded power
The total energy consumed by the unloading process is therefore:
wherein t is j Indicating offloading to CRP j Time of (a) < lambda > j Is given to CRP j Reciprocal of the proportion of bandwidth allocation to total bandwidth, B represents total bandwidth, h is channel gain, σ is variance of complex white Gaussian 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 is l max Denotes the maximum CPU frequency, t, of the CRR loc And T represents the time consumed by the local computation and the maximum delay acceptable for the CRR. λ ═ λ 1 ,...,λ j ,...,λ N Is the bandwidth allocation policy, t ═ t 1 ,...,t j ,...,t N Is the time allocationAnd (4) strategy. C5-C11 are constraints.
By inference, get E req The minimum optimal local computation frequency is:
when solving for λ and t, first get the minimum E off +E pro Lagrange function of the problem:
wherein mu j And γ is the lagrange multiplier. The KKT condition is then used to infer the optimal time allocation policy as:
order to
The invention deduces lambda j The maximum value of (d) is:
while gamma denotes lambda j Obtaining:
wherein W (x) represents a Lambertian function and is W (x) e W(x) The solution of x.
Further, the energy-saving calculation unloading algorithm is as follows:
inputting: a, B, n, T, f ═ f { f } 1 ,...,f j ,...,f N }
And (3) outputting: optimal offload time allocation t, optimal offload bandwidth allocation λ
1: initialization:
2: is obtained by the formula (11)
3: when sum h >1 and sum l <1, iterating:
4：γ m ＝1/2(γ h +γ l )
5: updating λ according to equation (13)
6：
7: if sum λ <1, then:
8：γ h ＝γ m
9：sum h ＝sum λ
10: if sum λ >1, then:
11：γ l ＝γ m
12：sum l ＝sum λ
13: and returning t and lambda.
Compared with the prior art, the invention has the advantages that:
1) the transaction process on the block chain can ensure the safety of resource sharing, effectively stimulates the user to participate in resource sharing, meets the experience of the user 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 the supply and demand parties for completing the calculation task, prolong the service time of the automobile and further stimulate the users to participate in sharing.
3) By applying the method, 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 diagram of a scenario of an embodiment of the present invention;
FIG. 2 is a flow diagram of a method of computing resource sharing according to an embodiment of the invention;
FIG. 3 is a simulation diagram of the resource transaction policy and utility provided by the present embodiment;
FIG. 4 is a schematic diagram of a simulation of the computation offload policy and energy consumption provided in this embodiment;
FIG. 5 is a performance diagram of the computational offload optimization algorithm provided by the present embodiment;
FIG. 6 is a simulation diagram of the joint optimization strategy and utility provided by the present embodiment;
fig. 7 is a simulation diagram of the joint optimization strategy and energy consumption provided by the present embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings by way of examples.
The invention uses the Stackelberg game in the resource transaction stage and solves the problem of the convex optimization under the KKT condition in the calculation unloading stage. And the method is realized by simulation by python on an association computer provided with a 3.4GHz I7 Intel processor and a 20GB memory, and a convex optimization algorithm in a scipy optimization packet is used.
As shown in fig. 1, the present invention is applied to a MEC-based car networking scenario, which includes: the edge layer is connected with the equipment layer through a wireless data link. The edge layer includes: the system comprises a local base station LBS and an edge server, wherein each LBS is provided with one edge server, the LBS receives information of the intelligent vehicle CRR and CRP and provides network assistance to trigger execution of an intelligent contract, 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 CRP sends vehicle information to the LBS, and finally carries out transaction according to an intelligent contract, the CRP provides computing resources for the CRR, and the CRR pays the cost 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, system initialization: all automobile users CRR and CRP participating in transaction and unloading on the platform need to register identities at a trusted local base station LBS, and become legal entities with identity identifications to join the blockchain system.
S2, intelligent contract for triggering resource transaction and unloading: and after vehicle information of the CRR and the CRP is received, automatically triggering an intelligent contract, and executing a resource transaction algorithm and an energy-saving calculation unloading algorithm to obtain a joint optimal strategy of resource transaction and calculation unloading.
S3, paying by using resource currency: according to the resource price obtained from the intelligent contract of the resource Transaction, the resource requester uses the private key to unlock the open Transaction Output (UTXO), and the resource provider unlocks the corresponding UTXO through the private key. The resource currency is transferred from the wallet address of the resource requestor to the resource provider.
S4, validating and broadcasting transaction records: and after the resource requester completes the resource transaction and the resource unloading, acquiring resource transaction records, encrypting the complete transactions with the signatures and broadcasting the encrypted transactions to all users on the platform.
S5, executing a consensus process: a new block consisting of all transactions, including resource transaction records and resource offerings of the requester, is constructed by the vehicle user with the highest offering score. The new block is broadcast to other users, and the user accepting the new block verifies the validity and correctness of the new block based on the hash value and the digital signature. Meanwhile, each user also needs to verify whether the building block has the highest total amount of resource provision and send the verification result to other users. If all verifications are correct, new blocks of data will be placed into the blockchain according to the time stamps.
In step S2, modeling a model of computing resource trading and energy saving offloading;
in an embodiment, a transaction model for providing computing resources for a 1-bit CRR by an N-bit CRP is as follows:
the utility function of the CRR is:
wherein d is max Representing the amount of computing resources required to complete a computing task, a j Is from a resource provider CRP j The amount of resources purchased.
Indicating the purchase intention of the CRR, d min To minimize the amount of resources that need to be purchased, z j Denotes the distance factor, p j Representing the price of the resource.
To maximize the utility of CRR, the resource allocation problem of the present embodiment is represented as P1:
P1:Max U b (a) (2)
C1:0≤a j ≤R j ,j∈N
reference [4]]Work of (2), CRP in this example j Is set to:
wherein R is j Is CRP j The amount of free resources of the network,
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 constraint conditions.
In this embodiment, resource allocation and pricing between CRRs and CRPs are established as a Stackelberg game, the CRR in the first round is used as a leader to firstly propose a resource allocation policy, and the CRP is used as a follower to set optimal resource pricing according to the resource allocation policy published by the CRR so that the best benefit is achieved, that is, P2 is solved. In a new round, the CRR adjusts the resource allocation policy to obtain the maximum benefit, i.e., to solve P1, according to the pricing policy published by the CRP. In order to obtain a resource trading strategy that satisfies both CRR and CRP, the following algorithm is used in this embodiment:
algorithm 1: resource transaction algorithm based on game theory
Inputting: 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, initialization: a is j ＝d max /N，
2:CRP j According to a j Solving P2 to obtain the latest resource pricing strategy P of CRP new And utility collections
3: iteration:
4：i＝i+1
5: according to p new Solving P1 to obtain a new resource allocation policy alpha new And the utility of CRR
6: according to a new Resolution P2 obtaining a new resource pricing policy P new And a utility set of CRPs
7: if it is not
And is
Stopping the iteration, otherwise continuing the iteration
8: return to a new And p new
In an embodiment, the energy saving computation offload model is as follows:
this embodiment will obtain resource allocation policy a ═ { a ═ a 1 ,...,a j ,...,a N Substituting the computation unloading process, wherein the energy consumption of local computation is as follows:
wherein f is l Denotes the CPU frequency of the CRR, and κ is a coefficient related to the chip architecture. Similarly, the calculated energy consumption of a CRP can be expressed as:
wherein f is j Represents CRR j The CPU frequency of (c). Using the relationship between the unloaded power and the calculated rate, the unloaded power can be inferred
The total energy consumed by the unloading process is therefore:
wherein t is j Indicating offloading to CRP j Time of (a) < lambda > j Is given to CRP j Reciprocal of the proportion of bandwidth allocation to total bandwidth, B represents total bandwidth, h is channel gain, σ is variance of complex white Gaussian noise, c j Is the computational complexity.
The present embodiment then expresses 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 is l max Denotes the maximum CPU frequency, t, of the CRR loc And T represents the time consumed by the local computation and the maximum delay acceptable for the CRR. λ ═ λ 1 ,...,λ j ,...,λ N Is the bandwidth allocation policy, t ═ t 1 ,...,t j ,...,t N Is the time allocation policy. C5-C11 are constraints.
By inference, this example results in E req The minimum optimal local computation frequency is:
when solving for λ and t, first get the minimum E off +E pro Lagrange function of the problem:
wherein mu j And γ is the lagrange multiplier. The KKT condition is then used to infer the optimal time allocation policy as:
order to
The invention deduces lambda j The maximum value of (d) is:
meanwhile, the embodiment uses gamma to represent lambda j Obtaining:
wherein W (x) represents a Lambertian function and is W (x) e W(x) Solution of x [11 ]]。
In order to obtain an optimal bandwidth allocation strategy, the following algorithm is designed:
and 2, algorithm: energy saving unloading mechanism
Inputting: a, B, n, T, f ═ f { f } 1 ,...,f j ,...,f N }
And (3) outputting: optimal offload time allocation t, optimal offload bandwidth allocation λ
1: initialization:
2: is obtained by the formula (11)
3: when sum h >1 and sum l <1, iterating:
4：γ m ＝1/2(γ h +γ l )
5: updating λ according to equation (13)
6：
7: if sum λ <1, then:
8：γ h ＝γ m
9：sum h ＝sum λ
10: if sum λ >1, then:
11：γ l ＝γ m
12：sum l ＝sum λ
13: returning t, λ
Finally, the present embodiment refers to parameter settings in [4], [12-15], creating a simulation data set. And respectively carrying out simulation experiments aiming at the effects of the resource transaction strategy, the calculation unloading strategy and the combination strategy.
The resource trading strategy effect is shown in fig. 3, where p ═ Optimal p and PTS are the resource pricing strategy and resource allocation strategy obtained by algorithm 1 in this embodiment, and compared with the fixed resource pricing and fixed resource trading total quantity strategy, the trading strategy in this embodiment can give consideration to the utilities of CRR and CRP, so that all users can obtain higher utility.
The computational offload policy effect is shown in fig. 4, where COS is the computational offload policy obtained by algorithm 2, and compared with random computational frequency, randomly allocated offload time and offload bandwidth, the policy obtained in this embodiment is most energy-saving.
The algorithm performance is shown in fig. 5, and compared with the traditional optimization algorithm, the complexity of the embodiment is O (n) 2 ) Down to O (log) 2 n), improving algorithm efficiency.
The effect of the union policy is shown in fig. 6 and 7, where OCS is the obtained policy for uniting resource transaction and computation offload, RAS is the random resource allocation policy, ERAS is the policy considering only energy-saving computation offload without considering user utility, and ROS is the policy considering only resource utility without considering energy-saving computation offload. Experimental results prove that the OCS obtained by the embodiment is the only strategy which can meet the utility of the intelligent vehicle user and realize energy-saving calculation unloading, and the user can be effectively stimulated to participate in resource sharing.
It will be appreciated by those of ordinary skill in the art that the examples described herein are intended to assist the reader in understanding the practice of the invention, and it is to be understood that the scope of the invention is not limited to such specific statements and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (4)
1. A platform for sharing computing resources 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 includes: the system comprises a local base station LBS and an edge server, wherein each LBS is provided with one edge server, the LBS receives vehicle information of CRR and CRP and provides network assistance to trigger the execution of an intelligent contract, 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 CRP with free resources sends vehicle information to the LBS, and finally carries out transaction according to an intelligent contract, the CRR provides computing resources for the CRR, and the CRR pays the expense to the CRP.
2. A computing resource sharing method under a fusion network of edge computing and V2V is characterized in that: the method is completed on the basis of the computing resource sharing platform of claim 1, and comprises the following steps:
s1: initializing a system: all automobile users CRR and CRP participating in transaction and unloading on the platform need to register identities at a credible local base station LBS, and become legal entities with identity marks to be added into a block chain system;
s2: triggering intelligent contracts for resource trading and unloading: after vehicle information of CRR and CRP is received, automatically triggering an intelligent contract, executing a resource transaction algorithm and an energy-saving calculation unloading algorithm to obtain a joint optimal strategy of resource transaction and calculation unloading;
s3: paying by 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 an Unpend Transaction Output (UTXO), and the CRP unlocks the corresponding UTXO through the private key; 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 uninstallation, acquiring resource transaction records, encrypting the complete transactions with the signatures and broadcasting the encrypted transactions to all users on the platform;
s5: executing a consensus process: constructing a new block consisting of all transactions by the automobile user with the highest supply score, wherein the new block comprises resource transaction records and the resource supply amount of the requester; the new block is broadcasted to other users, and the user who receives 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 and send the verification result to other users; if all verifications are correct, new blocks of data will be placed into the blockchain according to the time stamps.
3. The method for sharing computing resources under a converged network of edge computing and V2V, according to claim 2, wherein: the resource transaction algorithm comprises the following steps:
designing a computing resource transaction model of N CRP and one CRR; the utility function of the CRR is as follows:
wherein d is max Representing the amount of computing resources required to complete a computing task, a j Is from a resource provider CRP j The amount of resources purchased;
indicating the purchase intention of the CRR, d min To minimize the amount of resources that need to be purchased, z j Denotes the 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
CRP j The utility function of (a) is set as follows:
wherein R is j Is CRP j The amount of free resources of the network,
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 constraint conditions;
the resource transaction algorithm comprises the following specific processes:
inputting: 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, initialization: a is j ＝d max /N，
i＝1
2:CRP j According to a j Solving P2 to obtain the latest resource pricing strategy P of CRP new And utility collections
3: iteration:
4：i＝i+1
5: according to p new Solving P1 to obtain new resource allocation policy a new And the utility of CRR
6: according to a new Resolution P2 obtaining a new resource pricing policy P new And a utility set of CRPs
7: if it is used
And is
Stopping the iteration, otherwise continuing the iteration
8: return to a new And p new
Next, the present invention sets the obtained resource allocation policy a ═ { a ═ a 1 ,...,a j ,...,a N Substituting the computation unloading process, wherein the energy consumption of local computation is as follows:
wherein f is l Denotes 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 is j Represents CRR j The CPU frequency of (1); the invention utilizes the relation between the unloading power and the calculation rate to deduce the unloading power
The total energy consumed by the unloading process is therefore:
wherein t is j Indicating offloading to CRP j Time of (a) < lambda > j Is given to CRP j Bandwidth allocation is proportional to total bandwidthNumber, B denotes total bandwidth, h is channel gain, σ is variance of 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 is l max Denotes the maximum CPU frequency, t, of the CRR loc And T represents the time consumed by the local computation and the maximum delay acceptable for the CRR; λ ═ λ 1 ,...,λ j ,...,λ N Is the bandwidth allocation policy, t ═ t 1 ,...,t j ,...,t N Is the time allocation policy; C5-C11 are constraint conditions;
by inference, get E req The minimum optimal local computation frequency is:
when solving for λ and t, first get the minimum E off +E pro Lagrange function of the problem:
wherein mu j And γ is the lagrange multiplier; the KKT condition is then used to infer the optimal time allocation policy as:
order to
The invention deduces lambda j The maximum value of (d) is:
while gamma denotes lambda j Obtaining:
wherein W (x) represents the Lambert function and is W (x) e W(x) The solution of x.
4. The method of claim 3, wherein the computing resource sharing method under the converged network of edge computing and V2V is characterized in that: the energy-saving calculation unloading algorithm is as follows:
inputting: a, B, n, T, f ═ f { f } 1 ,...,f j ,...,f N }
And (3) outputting: optimal offload time allocation t, optimal offload bandwidth allocation λ
1: initialization:
2: is obtained by the formula (11)
3: when sum h >1 and sum l <1, iterating:
4：γ m ＝1/2(γh+γ l )
5: updating λ according to equation (13)
6：
7: if sum λ <1, then:
8：γ h ＝γ m
9：sum h ＝sum λ
10: if sum λ >1, then:
11：γ l ＝γ m
12：sum l ＝sum λ
13: and returning t and lambda.
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