CN114047971B - Edge computing resource allocation method and device - Google Patents
Edge computing resource allocation method and device Download PDFInfo
- Publication number
- CN114047971B CN114047971B CN202111320858.2A CN202111320858A CN114047971B CN 114047971 B CN114047971 B CN 114047971B CN 202111320858 A CN202111320858 A CN 202111320858A CN 114047971 B CN114047971 B CN 114047971B
- Authority
- CN
- China
- Prior art keywords
- task
- user equipment
- edge server
- information
- bid
- 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
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000013468 resource allocation Methods 0.000 title claims abstract description 43
- 238000012545 processing Methods 0.000 claims abstract description 60
- 230000008901 benefit Effects 0.000 claims description 27
- 230000002860 competitive effect Effects 0.000 claims description 27
- 230000008569 process Effects 0.000 claims description 20
- 238000004590 computer program Methods 0.000 claims description 12
- 238000005457 optimization Methods 0.000 claims description 9
- 238000003860 storage Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000004422 calculation algorithm Methods 0.000 description 33
- 230000007246 mechanism Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 6
- 238000004891 communication Methods 0.000 description 5
- 238000009826 distribution Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 239000011159 matrix material Substances 0.000 description 4
- 238000004088 simulation Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000009977 dual effect Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003825 pressing Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/445—Program loading or initiating
- G06F9/44594—Unloading
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
- G06F9/5072—Grid computing
-
- 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0283—Price estimation or determination
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- Mathematical Physics (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Economics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method and a device for distributing edge computing resources, wherein the method comprises the following steps: acquiring task information to be offloaded of each user equipment, processable task information of each edge server, bid information of each user equipment for single task to be offloaded to different edge servers for processing, ask price information of each edge server for processing single task and distance information between each user equipment and each edge server respectively; determining a matching task and a transaction price corresponding to each pair of successfully matched user equipment and an edge server combination based on task information to be offloaded, task information to be processed, bid information, price information to be charged, distance information and a double auction resource transaction model; and based on the determined matching task and the transaction price of each pair of successfully matched user equipment and the edge server combination, completing the computing resource allocation of each edge server. Thus, task allocation requirements are completed as much as possible, and the efficiency of the system is improved.
Description
Technical Field
The present invention relates to the field of edge computing technologies, and in particular, to a method and an apparatus for allocating edge computing resources.
Background
With the development of cloud computing and the internet of things, more and more data are generated at the edge of a network, and a new computing mode, namely edge computing, is generated, and is performed near a data source, so that data loading, data storage, caching, processing and the like can be performed, the efficiency can be improved, and the energy consumption of transmission and the like can be reduced. Since edge servers exhibit heterogeneity in terms of computing power and services provided, they typically cooperate to allocate resources to mobile devices (also referred to as user devices) in a cross-server manner. At present, how to effectively allocate edge computing resources to improve system efficiency is an important issue that needs to be solved in the industry.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a method and a device for distributing edge computing resources.
In a first aspect, the present invention provides a method for allocating edge computing resources, including:
acquiring task information to be offloaded of each user equipment, processable task information of each edge server, bid information of each user equipment for single task to be offloaded to different edge servers for processing, ask price information of each edge server for processing single task and distance information between each user equipment and each edge server respectively;
Based on the task information to be offloaded, the processable task information, the bid information, the ask price information, the distance information and a double auction resource transaction model, matching edge servers for the tasks to be offloaded of all user equipment, and determining matching tasks and transaction prices corresponding to each pair of user equipment and edge server combination which are successfully matched;
based on the determined matching task and the transaction price corresponding to each pair of successfully matched user equipment and the edge server combination, completing the computing resource allocation of each edge server;
the double auction resource transaction model aims at maximizing the number of successfully matched user equipment and edge servers, takes profits obtained by processing single tasks by unloading the user equipment to the edge servers, profits obtained by processing the single tasks by the edge servers and the price of the accomplishment as constraint conditions, and matches an optimization model of the edge servers for the tasks to be unloaded of the user equipment;
the benefits obtained by the user equipment for offloading a single task to the edge server for processing are determined according to the bidding information of the user equipment on the edge server, the price paid by the user equipment for the price in the transaction and the distance cost between the user equipment and the edge server.
Optionally, the benefit obtained by the user device offloading a single task to an edge server for processing is determined by the following formula:
wherein,representing the benefits obtained by the user equipment i in offloading the task r to the edge server for processing, c i,j Bid indicating that user equipment i is offloading to edge server j for a single task to handle, +.>Representing the price to be paid by the user equipment i in the transaction price assuming that the task r to be offloaded of the user equipment i is successfully matched with the edge server j, τ represents the cost per unit distance, e i,j Representing the distance between the user device i and the edge server j.
Optionally, the determining, based on the task information to be offloaded, the processable task information, the bid information, the ask information, the distance information, and the dual auction resource transaction model, a matching task and a transaction price corresponding to each pair of user equipment and the edge server combination that are successfully matched for the task to be offloaded of each user equipment includes:
according to the bidding information, bidding of each user equipment on different edge servers is arranged in descending order, and a bidding set is obtained;
according to the task information to be offloaded, the processable task information and the ask price information, starting from the first bid in the bid set, sequentially judging whether an intersection task exists between the residual processable task set of the target edge server corresponding to the target bid and the residual task set to be offloaded of the target user equipment corresponding to the target bid;
If the target bid is larger than the ask price of the target edge server, determining the target user equipment and the target edge server combination as candidate matching combination, determining the intersection task as a matching task corresponding to the candidate matching combination, and determining the highest competitive bid of other user equipment to the target edge server for the intersection task according to other bids positioned behind the target bid in the bid set;
determining that the target bid is greater than or equal to the sum of the highest competitive bid and the maximum distance cost, determining the candidate matching combination as a pair of successfully matched user equipment and edge server combination, determining the intersection task as a matching task corresponding to the pair of successfully matched user equipment and edge server combination, and determining the highest competitive bid as an exchange price corresponding to the pair of successfully matched user equipment and edge server combination; wherein the maximum distance cost is determined according to a distance maximum value and a cost per unit distance in the distance information.
Optionally, each of the user devices and each of the edge servers are nodes on a blockchain.
In a second aspect, the present invention further provides an edge computing resource allocation apparatus, including:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring task information to be offloaded of each user equipment, task information which can be processed by each edge server, bid information which is offloaded to different edge servers by each user equipment for a single task to process, ask price information which is processed by each edge server for a single task and distance information between each user equipment and each edge server respectively;
the matching module is used for matching the edge servers for the tasks to be offloaded of all the user equipment based on the task information to be offloaded, the processable task information, the bid information, the ask price information, the distance information and the double auction resource transaction model, and determining matching tasks and transaction prices corresponding to each pair of user equipment successfully matched with the edge servers;
the allocation module is used for completing the calculation resource allocation of each edge server based on the determined matching task and the transaction price corresponding to each pair of successfully matched user equipment and the edge server combination;
the double auction resource transaction model aims at maximizing the number of successfully matched user equipment and edge servers, takes profits obtained by processing single tasks by unloading the user equipment to the edge servers, profits obtained by processing the single tasks by the edge servers and the price of the accomplishment as constraint conditions, and matches an optimization model of the edge servers for the tasks to be unloaded of the user equipment;
The benefits obtained by the user equipment for offloading a single task to the edge server for processing are determined according to the bidding information of the user equipment on the edge server, the price paid by the user equipment for the price in the transaction and the distance cost between the user equipment and the edge server.
Optionally, the benefit obtained by the user device offloading a single task to an edge server for processing is determined by the following formula:
wherein,representing the benefits obtained by the user equipment i in offloading the task r to the edge server for processing, c i,j Indicating that user equipment i is for a singleBid for task offloading to edge server j for processing,/-)>Representing the price to be paid by the user equipment i in the transaction price assuming that the task r to be offloaded of the user equipment i is successfully matched with the edge server j, τ represents the cost per unit distance, e i,j Representing the distance between the user device i and the edge server j.
Optionally, the matching module is configured to:
according to the bidding information, bidding of each user equipment on different edge servers is arranged in descending order, and a bidding set is obtained;
according to the task information to be offloaded, the processable task information and the ask price information, starting from the first bid in the bid set, sequentially judging whether an intersection task exists between the residual processable task set of the target edge server corresponding to the target bid and the residual task set to be offloaded of the target user equipment corresponding to the target bid;
If the target bid is larger than the ask price of the target edge server, determining the target user equipment and the target edge server combination as candidate matching combination, determining the intersection task as a matching task corresponding to the candidate matching combination, and determining the highest competitive bid of other user equipment to the target edge server for the intersection task according to other bids positioned behind the target bid in the bid set;
determining that the target bid is greater than or equal to the sum of the highest competitive bid and the maximum distance cost, determining the candidate matching combination as a pair of successfully matched user equipment and edge server combination, determining the intersection task as a matching task corresponding to the pair of successfully matched user equipment and edge server combination, and determining the highest competitive bid as an exchange price corresponding to the pair of successfully matched user equipment and edge server combination; wherein the maximum distance cost is determined according to a distance maximum value and a cost per unit distance in the distance information.
In a third aspect, the present invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the edge computing resource allocation method according to the first aspect as described above when the program is executed.
In a fourth aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the edge computing resource allocation method according to the first aspect as described above.
In a fifth aspect, the present invention also provides a computer program product comprising a computer program which when executed by a processor implements the steps of the edge computing resource allocation method as described in any of the above.
According to the edge computing resource distribution method and device, the edge servers are matched for the tasks to be offloaded of the user equipment through the double auction resource transaction model, and the distance cost of the user equipment is fully considered, so that the resource distribution result can be approved by both the user equipment and the edge servers, task distribution requirements can be completed as much as possible, and the efficiency of the system is improved.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for allocating edge computing resources according to the present invention;
FIG. 2 is a schematic diagram of the overall architecture of a blockchain-based Internet of things resource transaction provided by the invention;
FIG. 3 is a time-consuming comparison of the dual auction algorithm provided by the present invention with a random selection algorithm;
FIG. 4 is a graph showing the comparison of the number of tasks completed by the double auction algorithm and the random selection algorithm provided by the invention;
FIG. 5 is a schematic diagram of an edge computing resource allocation apparatus according to the present invention;
fig. 6 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a flow chart of an edge computing resource allocation method provided by the present invention, as shown in fig. 1, the method includes the following steps:
Step 100, acquiring task information to be offloaded of each user equipment, task information which can be processed by each edge server, bid information which is offloaded to different edge servers by each user equipment for a single task to process, ask information which is processed by each edge server for a single task and distance information between each user equipment and each edge server respectively;
specifically, in the scenario of edge computing resource allocation, the method mainly includes two parts, namely an edge server and user equipment. The edge server is a device for storing resources, calculating resources and communicating information, and is also a device with limited capacity but capable of performing a large amount of calculation tasks, an application program capable of processing one or more specific tasks is deployed in the edge server, and the user equipment can offload the tasks to be processed to the edge server for processing.
The edge computing resource allocation process can be analogically to the resource transaction process, in the embodiment of the invention, the resource transaction is based on a classical auction mechanism, but is different from the classical auction in that both buyers and sellers are considered, namely, the invention adopts a double auction mechanism, the classical auction model is usually beneficial to sellers, the benefit of buyers is ignored, and the two auction mechanisms are considered in balance, so that a scheme which is more satisfactory to both parties is selected. Double auctions are a process of trading goods in which sellers submit their ask prices, buyers submit their bids to the auctioneers simultaneously, and the auctioneers then determine the successful buyers and sellers and the goods and prices to be traded according to a policy. The invention takes the edge server as a seller, the user equipment as a buyer, the computing resource of the edge server as a commodity, and a trusted third party (for example, the edge server) as a seller to perform edge computing resource allocation.
In the process of allocating the edge computing resources, the auctioneer needs to acquire task information to be offloaded of each user equipment, task information which can be processed of each edge server, bid information which is offloaded to different edge servers by each user equipment for a single task to process, ask price information which is processed by each edge server for a single task and distance information between each user equipment and each edge server respectively.
Wherein, the bids of the same user equipment i to the same edge server j are the same for different task types. The ask price of the edge server j is the same for different task types.
Considering the time delay effect generated by the distance between the user equipment and the edge server, the invention adds a distance factor when the edge computing resource is distributed, and the auction party needs to acquire the distance information between each user equipment and each edge server.
Step 101, matching edge servers for tasks to be offloaded of all user equipment based on the task information to be offloaded, the processable task information, the bid information, the price information, the distance information and the double auction resource transaction model, and determining matching tasks and transaction prices corresponding to each pair of user equipment successfully matched with the edge servers;
Specifically, after acquiring task information to be offloaded of each user equipment, task information which can be processed by each edge server, bid information which can be processed by each user equipment aiming at a single task and is offloaded to different edge servers, ask price information of each edge server for processing the single task and distance information between each user equipment and each edge server respectively, an auctioneer can match the edge servers for the tasks to be offloaded of each user equipment based on the information and a double auction resource transaction model, and determine matching tasks and transaction prices which are corresponding to each pair of user equipment successfully matched with the edge servers.
Wherein, the matching task refers to the task that the user equipment can be successfully unloaded to the edge server in each pair of successfully matched user equipment and edge server combination. The user equipment can respectively offload different tasks to be offloaded to different edge servers.
The price of the deal may include the price of the commodity that the user device needs to pay at the time of the deal, as well as the price of the commodity available to the edge server.
The double auction resource transaction model in the embodiment of the invention aims at maximizing the number of successfully matched user equipment and edge servers, takes the benefits obtained by the user equipment unloading a single task to the edge servers for processing, the benefits obtained by the edge servers for processing the single task and the price for success as constraint conditions, and matches the optimization model of the edge servers for the tasks to be unloaded of the user equipment; the benefits obtained by the user equipment for offloading a single task to the edge server for processing are determined according to the bid information of the user equipment on the edge server, the price paid by the user equipment for the price in the transaction and the distance cost between the user equipment and the edge server.
Alternatively, the benefit obtained by the user device offloading a single task to the edge server for processing may be determined by the following equation:
wherein,representing the benefits obtained by the user equipment i in offloading the task r to the edge server for processing, c i,j Bid indicating that user equipment i is offloading to edge server j for a single task to handle, +.>Representing the price to be paid by the user equipment i in the transaction price assuming that the task r to be offloaded of the user equipment i is successfully matched with the edge server j, τ represents the cost per unit distance, e i,j Representing the distance between the user device i and the edge server j.
Step 102, based on the determined matching task and the transaction price of each pair of successfully matched user equipment and the edge server combination, the computing resource allocation of each edge server is completed.
Specifically, after determining the matching task and the price of the transaction corresponding to each pair of successfully matched user equipment and the edge server combination, the auctioneer can determine which tasks can be offloaded to which edge servers by each user equipment, and the price to be paid by the user equipment and the payment available to the edge servers, and accordingly complete the computing resource allocation of each edge server.
According to the edge computing resource allocation method provided by the invention, the edge servers are matched for the tasks to be offloaded of the user equipment through the double auction resource transaction model, and the distance cost of the user equipment is fully considered, so that the resource allocation result can be approved by both the user equipment and the edge servers, the task allocation requirements can be completed as much as possible, and the efficiency of the system is improved.
Optionally, based on the task to be offloaded information, the processable task information, the bid information, the price information, the distance information and the double auction resource transaction model, matching edge servers for the task to be offloaded of the user equipment, and determining matching tasks and transaction prices corresponding to each pair of successfully matched user equipment and edge server combination, including:
according to the bidding information, bidding of each user equipment on different edge servers is arranged in descending order, and a bidding set is obtained;
according to the task information to be offloaded, the processable task information and the ask price information, starting from the first bid in the bid set, sequentially judging whether an intersection task exists between the residual processable task set of the target edge server corresponding to the target bid and the residual task set to be offloaded of the target user equipment corresponding to the target bid;
If the target bid is larger than the ask price of the target edge server, determining the target user equipment and the target edge server combination as candidate matching combination, determining an intersection task as matching task corresponding to the candidate matching combination, and determining the highest competitive bid of other user equipment to the target edge server for the intersection task according to other bids positioned after the target bid in the bid set;
determining that the target bid is greater than or equal to the sum of the highest competitive bid and the maximum distance cost, determining the candidate matching combination as a pair of successfully matched user equipment and edge server combinations, determining the intersection task as a matching task corresponding to the pair of successfully matched user equipment and edge server combinations, and determining the highest competitive bid as a transaction price corresponding to the pair of successfully matched user equipment and edge server combinations; wherein the maximum distance cost is determined according to the maximum distance value in the distance information and the cost per unit distance.
Specifically, in the embodiment of the present invention, after the auctioneer obtains the task information to be offloaded of each user equipment, the processable task information of each edge server, the bid information that each user equipment offloads to different edge servers for processing aiming at a single task, the ask price information that each edge server processes a single task, and the distance information between each user equipment and each edge server, the bids of each user equipment on different edge servers can be arranged according to the bid information in descending order to obtain the bid set.
Then, starting from the first bid in the bid set, whether intersection tasks exist between the residual processable task set of the target edge server corresponding to the target bid and the residual task set to be offloaded of the target user equipment corresponding to the target bid or not is judged in sequence.
For example, if the first bid in the bid set, i.e., the highest bid, is the bid of the user equipment i for the edge server j, and the tasks to be offloaded of the user equipment i include r1, r2, and r3, the tasks that the edge server j can process are r1, r3, and r4, then r1 and r3 are intersection tasks, after determining the intersection task corresponding to the first bid, the remaining processable task set of the edge server j is r4, i.e., the processable task set remaining after rejecting the intersection task of the first bid, and the remaining task set to be offloaded of the user equipment i is r2, i.e., the task set remaining after rejecting the intersection task of the first bid, and so on, sequentially determining whether there is an intersection task between the remaining processable task set of the target edge server corresponding to the target bid and the remaining task set to be offloaded of the target user equipment corresponding to the target bid.
Taking the first bid as an example, if the first bid, i.e. the bid of the user equipment i on the edge server j is greater than the ask of the edge server j, the combination of the user equipment i and the edge server j may be determined as a candidate matching combination, the intersection tasks r1 and r3 may be determined as matching tasks corresponding to the candidate matching combination, and the highest competitive bids of other user equipment on the intersection tasks r1 and r3 on the edge server j may be determined according to the other bids located after the first bid in the bid set. For example, for task r1, if other user devices also have a need to offload task r1, then their bids on edge server j are their competing bids for task r1, and for user device i, the highest of these competing bids is the highest competing bid for task r1 for other user devices.
Then, considering the distance cost of the user equipment, the auctioneer also needs to determine that the target bid is greater than or equal to the sum of the highest competitive bid and the maximum distance cost, so as to finally determine the candidate matching combination as a successfully matched pair of user equipment and edge server combination, determine the intersection task as a successfully matched pair of matching tasks corresponding to the edge server combination, and determine the highest competitive bid as a successfully matched pair of transaction prices corresponding to the edge server combination. It should be noted that, both the competitive bidding and the determination that the target bid is greater than or equal to the sum of the highest competitive bidding and the maximum distance cost are for a single task, if there are a plurality of intersecting tasks, it may be the case that only one or more of the intersecting tasks are ultimately determined as matching tasks.
Determining the highest competitive bid as the corresponding bid price of the successfully matched pair of user equipment and the edge server combination means that the price which the user equipment needs to pay is the highest competitive bid in the successfully matched pair of user equipment and the edge server combination, and the actual bid of the user equipment is not required, obviously, the highest competitive bid is lower than the actual bid of the user equipment, and the consideration which the edge server can obtain is correspondingly the highest competitive bid.
The maximum distance cost may be determined according to a maximum value of the distance in the distance information and a cost per unit distance, and may be calculated by multiplying the maximum value in a distance set of each user equipment and each edge server by the cost per unit distance, for example.
Optionally, each user device and each edge server are nodes on a blockchain.
Specifically, the blockchain technology is a shared distributed account book, data on the account book is disclosed and cannot be tampered, cryptography in the blockchain technology can enable the data to be incapable of being modified once being uplinked, a common recognition mechanism and the like can avoid the traditional centralized thought, nodes on the blockchain can view the whole network data anytime and anywhere without the help of others and the like, and transmission cost, delay influence and the like are effectively reduced. In the embodiment of the invention, each user equipment and each edge server can be nodes on the blockchain, and the resource allocation process can be executed in the blockchain in the form of intelligent contracts, so that a distributed, resistance-free, safe and fair resource allocation consensus mechanism is realized.
The above-mentioned edge computing resource allocation method is exemplified by the following specific embodiments. In the embodiment, the block chain-based internet of things edge computing resource transaction process is introduced, the internet of things edge computing resource transaction process is simulated according to a double auction algorithm, and intelligent contracts are written by using an Ethernet tool in a block chain technology to simulate resource transaction work.
Fig. 2 is a schematic diagram of an overall architecture of a block-chain-based resource transaction of the internet of things, and as shown in fig. 2, the scenario includes two parts, namely an edge server and user equipment. The devices are distributed and diffused, and have certain time delay characteristics. Before auction, each user device publishes its exact task requirements and distance factors between device nodes, and for the set of sellers represented by the edge server, what needs to be published is the exact task-completing application deployed therein.
The auction is then conducted, in which the buyer (i.e., user device) and seller (i.e., edge server) bid and ask, respectively, and the same task may have multiple buyers 'needs and multiple sellers' deployments, so that different tasks, different user devices and edge servers, have different bids and ask.
Double auctions refer to both buyers and sellers submitting respective offers to auction parties, and then the auction parties determine auction prices to facilitate transactions according to certain policies. During the transaction, the buyer or seller may be subjected to malicious price raising, pressing, etc. actions, and in order to avoid a potential malicious attacker, an edge server will be selected as the verifier, which determines the set of winners (i.e. the successfully matched user devices and edge servers), and selects a price for each buyer and each seller of the set of winners to conduct the transaction. Given transaction information, key information is determined including a winning set of buyers, a winning set of sellers, a distance matrix, a task matrix, a payment (i.e., payment of user devices in a bargain price), a collection of payouts (i.e., payouts of edge servers in a bargain price), and the like.
To formulate pricing policies, it is necessary to determine the interests z of the buyer and seller i,j . Suppose that buyer i pays for a single application (corresponding to task r) offered by seller j asBid for buyer i to seller j is c i,j . Taking into account the effect of time delay, adding a distance factor, wherein τ is the cost per unit distance, e i,j Is the distance between buyer i and seller j, the service benefit that buyer i obtains by loading task r into seller j>The definition is as follows:
let m be j Is the cost of seller j to process a single task, and the cost of providing a single application (corresponding to task r) to buyer i is taken by seller j asSeller j obtains +.>The definition is as follows:
in order to ensure that both parties to the transaction do not lose money and the whole transaction process is not subjected to adverse effects, namely, the clapper is not allowed to subsidize the transaction. In this embodiment, the double auction resource transaction model expression is as follows:
where x represents the number of buyers, c represents the number of sellers, s i,j Representing a combination of buyer i and seller j, S w Representing a set of winners, X w Representing winning buyer collections, Y w Sets of sellers representing winning s i,r =1 indicates that the task r of buyer i successfully matches the seller.
Equation (3) shows that the model is targeted to maximize the number of winner combinations; equation (4) ensures that no participant in the auction will lose money; equation (5) ensures that the buyer pays a price that must be greater than or equal to the revenue that the seller receives each time the seller successfully completes the task; equation (6) ensures that at the end of the auction, the buyer gets more benefit than the seller. The embodiment uses the auction theory driven by the blockchain to solve the resource allocation problem, the blockchain technology uses the encryption authentication technology and the consensus mechanism to avoid the proxy risk, and the auction theory can finish the loading and resource allocation tasks in a short time without causing excessive system loss.
The following is the core algorithm pseudo code of the resource allocation provided in this embodiment:
wherein C represents a buyer bid set (elements in the set are represented by C-under-the-heading), Q represents a seller ask set (elements in the set are represented by Q-under-heading), U represents a buyer task set to be offloaded (elements in the set are represented by U-under-heading), T represents a seller processable task set (elements in the set are represented by T-under-heading),e represents a distance matrix (elements in its set are denoted by E-subscript), P u Representing a winning buyer payment set (the elements of which set are denoted p u Indicated by the subscript), P s A collection of seller payouts representing a win (the elements in the collection of which are denoted p s Indicated by the subscript).
The double auction algorithm firstly performs descending order sequencing on the distance matrix to obtain E ', inquires the maximum value in E' to obtain the maximum distance valueAnd then sorting the buyer bid sets in a descending order to obtain C ', traversing the elements in the C' in order, distributing resources according to the newly sorted order to provide corresponding candidate buyers, loading the buyer tasks into sellers, then finding other candidates with the tasks (namely competitors of the candidate buyers), bidding for sellers by each buyer, and using one of the highest bids as a basis for the buyers to acquire loading tasks. If all of the buyer's tasks have been completed, the subsequent bids associated with the buyer are deleted. The remaining elements are operated in the same manner, taking into account the distance costs of the buyers, yielding a winner combination and corresponding pricing combination.
The simulation experiment results provided in this embodiment are described below.
The invention adopts java language to simulate the resource transaction process on the Eclipse platform. User devices (hereinafter referred to as users) that have no tasks to load will not submit bids nor participate in the auction. Each edge server (hereinafter referred to as a server) is directly or indirectly connected through a wired link. The prices and configurations of the buyers and sellers are randomly generated in a uniformly distributed form. It is assumed that all servers can handle only four types of tasks, and that each server can handle some or all of the types of tasks.
The method comprises the steps of carrying out resource transaction simulation on two design algorithms by using java language, defining 4 task types which can be processed on Eclipse by default, enabling other data volume to change on a server and the number of users, setting eight groups of data to enable the two algorithms to be compared, carrying out initialization data random generation on a bid set, a price set, a task set, a deployment set and the like, firstly determining the number of users and the number of servers, wherein the data volume of the eight groups of users and the number of servers are shown in a table 1, the bid value range of each user on each server is [1,10], collecting each group of data by using a folder, providing a corresponding number of users under each folder, providing a corresponding number of files for the user i on the server v by the ith row of data in the ith file, carrying out corresponding data random initial generation on other variables in the same way, and storing all data in the files.
For the task set of the buyer and the deployment set of the seller, the value is 1 if the task requirement exists and the application program with the task loads, otherwise, the value is 0, the value of the same reason of the set which records are served is {0,1}, and for the bid set, the value range of quotation carried out by the user with the task requirement is within [1,10], and the value is 0 without quotation.
The double auction mechanism algorithm is compared with the random selection algorithm, the core idea of the random selection algorithm is to randomly select a user and a server, if the selected server just can meet the requirement of the user, the two algorithms perform scheme distribution under the same data. For the performance and efficiency of the algorithm, the embodiment compares two aspects of algorithm time consumption and task completion number, wherein the algorithm time consumption condition is shown in table 2, and the algorithm task completion number condition is shown in table 3.
Table 1 experimental data set
Table 2 Algorithm time consuming case
Table 3 algorithm completion task count
Fig. 3 is a time-consuming comparison chart of the double-auction algorithm and the random selection algorithm provided by the invention, fig. 4 is a time-consuming comparison chart of the double-auction algorithm and the random selection algorithm provided by the invention, and in combination with fig. 3 and fig. 4, as the number of users increases, the number of tasks increases and the execution time also increases. When the number of tasks is small, the time consumption difference of the two algorithms is small. However, as the number of tasks increases, the algorithm time consuming process is significantly different. The time complexity of the double auction algorithm can be analyzed as O (xc 2 ) The time complexity of the random algorithm is O (x+c+xc) 2 ) The former algorithm is less time-complex than the latter algorithm, so the execution time of the double auction algorithm will be less than the random selection algorithm.
As can be seen from simulation results, the double auction algorithm provided by the invention is superior to the random selection algorithm in performance and efficiency, and can make the system more efficient in practical application.
The edge computing resource allocation device provided by the invention is described below, and the edge computing resource allocation device described below and the edge computing resource allocation method described above can be referred to correspondingly.
Fig. 5 is a schematic structural diagram of an edge computing resource allocation device according to the present invention, as shown in fig. 5, where the device includes:
the acquiring module 500 is configured to acquire task information to be offloaded of each user equipment, task information that can be processed by each edge server, bid information that each user equipment offloads a single task to different edge servers for processing, ask information that each edge server processes a single task, and distance information between each user equipment and each edge server respectively;
the matching module 510 is configured to match the edge servers for the tasks to be offloaded of the user devices based on the task information to be offloaded, the processable task information, the bid information, the price information, the distance information and the double auction resource transaction model, and determine a matching task and a transaction price corresponding to each pair of successfully matched user devices and the edge server combination;
An allocation module 520, configured to complete computing resource allocation of each edge server based on the determined matching task and the transaction price corresponding to each pair of successfully matched user equipment and the edge server combination;
the double auction resource transaction model aims at maximizing the number of successfully matched user equipment and edge servers, takes profits obtained by processing single tasks by unloading the user equipment to the edge servers, profits obtained by processing the single tasks by the edge servers and the price of the bargain as constraint conditions, and matches an optimization model of the edge servers for the tasks to be unloaded of the user equipment;
the revenue obtained by the user device offloading a single task to the edge server for processing is determined based on the user device's bid information for the edge server, the price paid by the user device for the price of the deal, and the cost of the distance between the user device and the edge server.
Optionally, the benefit obtained by the user device offloading a single task to the edge server for processing is determined by the following formula:
wherein,representing the benefits obtained by the user equipment i in offloading the task r to the edge server for processing, c i,j Bid indicating that user equipment i is offloading to edge server j for a single task to handle, +.>Representing the price to be paid by the user equipment i in the transaction price assuming that the task r to be offloaded of the user equipment i is successfully matched with the edge server j, τ represents the cost per unit distance, e i,j Representing the distance between the user device i and the edge server j.
Optionally, the matching module 510 is configured to: according to the bidding information, bidding of each user equipment on different edge servers is arranged in descending order, and a bidding set is obtained; according to the task information to be offloaded, the processable task information and the ask price information, starting from the first bid in the bid set, sequentially judging whether an intersection task exists between the residual processable task set of the target edge server corresponding to the target bid and the residual task set to be offloaded of the target user equipment corresponding to the target bid; if the target bid is larger than the ask price of the target edge server, determining the target user equipment and the target edge server combination as candidate matching combination, determining an intersection task as matching task corresponding to the candidate matching combination, and determining the highest competitive bid of other user equipment to the target edge server for the intersection task according to other bids positioned after the target bid in the bid set; determining that the target bid is greater than or equal to the sum of the highest competitive bid and the maximum distance cost, determining the candidate matching combination as a pair of successfully matched user equipment and edge server combinations, determining the intersection task as a matching task corresponding to the pair of successfully matched user equipment and edge server combinations, and determining the highest competitive bid as a transaction price corresponding to the pair of successfully matched user equipment and edge server combinations; wherein the maximum distance cost is determined according to the maximum distance value in the distance information and the cost per unit distance.
Optionally, each user device and each edge server are nodes on a blockchain.
It should be noted that, the device provided by the present invention can implement all the method steps implemented by the method embodiment and achieve the same technical effects, and the parts and beneficial effects that are the same as those of the method embodiment in the present embodiment are not described in detail herein.
Fig. 6 is a schematic structural diagram of an electronic device according to the present invention, as shown in fig. 6, the electronic device may include: processor 610, communication interface (Communications Interface) 620, memory 630, and communication bus 640, wherein processor 610, communication interface 620, and memory 630 communicate with each other via communication bus 640. The processor 610 may invoke logic instructions in the memory 630 to perform the steps of any of the edge computing resource allocation methods provided by the embodiments described above, such as: acquiring task information to be offloaded of each user equipment, processable task information of each edge server, bid information of each user equipment for single task to be offloaded to different edge servers for processing, ask price information of each edge server for processing single task and distance information between each user equipment and each edge server respectively; matching edge servers for tasks to be offloaded of all user equipment based on the task information to be offloaded, the processable task information, the bid information, the price information, the distance information and the double auction resource transaction model, and determining matching tasks and transaction prices corresponding to each pair of successfully matched user equipment and the edge servers; based on the determined matching task and the transaction price corresponding to each pair of successfully matched user equipment and the edge server combination, completing the computing resource allocation of each edge server; the double auction resource transaction model aims at maximizing the number of successfully matched user equipment and edge servers, takes profits obtained by processing single tasks by unloading the user equipment to the edge servers, profits obtained by processing the single tasks by the edge servers and the price of the bargain as constraint conditions, and matches an optimization model of the edge servers for the tasks to be unloaded of the user equipment; the revenue obtained by the user device offloading a single task to the edge server for processing is determined based on the user device's bid information for the edge server, the price paid by the user device for the price of the deal, and the cost of the distance between the user device and the edge server.
Further, the logic instructions in the memory 630 may be implemented in the form of software functional units and stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product comprising a computer program, the computer program being storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of executing the steps of any of the edge computing resource allocation methods provided in the above embodiments, for example: acquiring task information to be offloaded of each user equipment, processable task information of each edge server, bid information of each user equipment for single task to be offloaded to different edge servers for processing, ask price information of each edge server for processing single task and distance information between each user equipment and each edge server respectively; matching edge servers for tasks to be offloaded of all user equipment based on the task information to be offloaded, the processable task information, the bid information, the price information, the distance information and the double auction resource transaction model, and determining matching tasks and transaction prices corresponding to each pair of successfully matched user equipment and the edge servers; based on the determined matching task and the transaction price corresponding to each pair of successfully matched user equipment and the edge server combination, completing the computing resource allocation of each edge server; the double auction resource transaction model aims at maximizing the number of successfully matched user equipment and edge servers, takes profits obtained by processing single tasks by unloading the user equipment to the edge servers, profits obtained by processing the single tasks by the edge servers and the price of the bargain as constraint conditions, and matches an optimization model of the edge servers for the tasks to be unloaded of the user equipment; the revenue obtained by the user device offloading a single task to the edge server for processing is determined based on the user device's bid information for the edge server, the price paid by the user device for the price of the deal, and the cost of the distance between the user device and the edge server.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium, on which is stored a computer program, which when executed by a processor is implemented to perform the steps of any of the edge computing resource allocation methods provided in the above embodiments, for example: acquiring task information to be offloaded of each user equipment, processable task information of each edge server, bid information of each user equipment for single task to be offloaded to different edge servers for processing, ask price information of each edge server for processing single task and distance information between each user equipment and each edge server respectively; matching edge servers for tasks to be offloaded of all user equipment based on the task information to be offloaded, the processable task information, the bid information, the price information, the distance information and the double auction resource transaction model, and determining matching tasks and transaction prices corresponding to each pair of successfully matched user equipment and the edge servers; based on the determined matching task and the transaction price corresponding to each pair of successfully matched user equipment and the edge server combination, completing the computing resource allocation of each edge server; the double auction resource transaction model aims at maximizing the number of successfully matched user equipment and edge servers, takes profits obtained by processing single tasks by unloading the user equipment to the edge servers, profits obtained by processing the single tasks by the edge servers and the price of the bargain as constraint conditions, and matches an optimization model of the edge servers for the tasks to be unloaded of the user equipment; the revenue obtained by the user device offloading a single task to the edge server for processing is determined based on the user device's bid information for the edge server, the price paid by the user device for the price of the deal, and the cost of the distance between the user device and the edge server.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. A method for allocating edge computing resources, comprising:
acquiring task information to be offloaded of each user equipment, processable task information of each edge server, bid information of each user equipment for single task to be offloaded to different edge servers for processing, ask price information of each edge server for processing single task and distance information between each user equipment and each edge server respectively;
based on the task information to be offloaded, the processable task information, the bid information, the ask price information, the distance information and a double auction resource transaction model, matching edge servers for the tasks to be offloaded of all user equipment, and determining matching tasks and transaction prices corresponding to each pair of user equipment and edge server combination which are successfully matched;
Based on the determined matching task and the transaction price corresponding to each pair of successfully matched user equipment and the edge server combination, completing the computing resource allocation of each edge server;
the double auction resource transaction model aims at maximizing the number of successfully matched user equipment and edge servers, takes profits obtained by processing single tasks by unloading the user equipment to the edge servers, profits obtained by processing the single tasks by the edge servers and the price of the accomplishment as constraint conditions, and matches an optimization model of the edge servers for the tasks to be unloaded of the user equipment;
the user equipment offloads a single task to the edge server to process, and the obtained benefits are determined according to the bidding information of the user equipment on the edge server, the price paid by the user equipment for the price of the transaction and the distance cost between the user equipment and the edge server;
the step of determining matching task and deal price corresponding to each pair of user equipment and edge server combination which are successfully matched for matching the edge server of the task to be offloaded based on the task information to be offloaded, the processable task information, the bid information, the ask information, the distance information and the double auction resource transaction model comprises the following steps:
According to the bidding information, bidding of each user equipment on different edge servers is arranged in descending order, and a bidding set is obtained;
according to the task information to be offloaded, the processable task information and the ask price information, starting from the first bid in the bid set, sequentially judging whether an intersection task exists between the residual processable task set of the target edge server corresponding to the target bid and the residual task set to be offloaded of the target user equipment corresponding to the target bid;
if the target bid is larger than the ask price of the target edge server, determining the target user equipment and the target edge server combination as candidate matching combination, determining the intersection task as a matching task corresponding to the candidate matching combination, and determining the highest competitive bid of other user equipment to the target edge server for the intersection task according to other bids positioned behind the target bid in the bid set;
determining that the target bid is greater than or equal to the sum of the highest competitive bid and the maximum distance cost, determining the candidate matching combination as a pair of successfully matched user equipment and edge server combination, determining the intersection task as a matching task corresponding to the pair of successfully matched user equipment and edge server combination, and determining the highest competitive bid as an exchange price corresponding to the pair of successfully matched user equipment and edge server combination; wherein the maximum distance cost is determined according to a distance maximum value and a cost per unit distance in the distance information;
The expression of the double auction resource transaction model is as follows:
where x represents the number of buyers, c represents the number of sellers, s i,j Representing a group of buyers i and sellers jClosing, S w Representing a set of winners,representing the service benefit of buyer i by loading task r into seller j +.>Representing the benefit of seller j by providing service to buyer i->Representing payment of buyer i for a single application corresponding to task r provided by seller j,/>Representing the payment made by seller j for buyer i by a single application providing corresponding task r, X w Representing winning buyer collections, Y w Sets of sellers representing winning s i,r =1 indicates that the task r of buyer i successfully matches the seller.
2. The edge computing resource allocation method of claim 1, wherein the benefit obtained by the user device offloading a single task to an edge server for processing is determined by the following formula:
wherein,representing the benefits obtained by the user equipment i in offloading the task r to the edge server for processing, c i,j Bid indicating that user equipment i is offloading to edge server j for a single task to handle, +.>Representing the price to be paid by the user equipment i in the transaction price assuming that the task r to be offloaded of the user equipment i is successfully matched with the edge server j, τ represents the cost per unit distance, e i,j Representing the distance between the user device i and the edge server j.
3. The edge computing resource allocation method of claim 1 or 2, wherein each of the user devices and each of the edge servers are nodes on a blockchain.
4. An edge computing resource allocation apparatus, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring task information to be offloaded of each user equipment, task information which can be processed by each edge server, bid information which is offloaded to different edge servers by each user equipment for a single task to process, ask price information which is processed by each edge server for a single task and distance information between each user equipment and each edge server respectively;
the matching module is used for matching the edge servers for the tasks to be offloaded of all the user equipment based on the task information to be offloaded, the processable task information, the bid information, the ask price information, the distance information and the double auction resource transaction model, and determining matching tasks and transaction prices corresponding to each pair of user equipment successfully matched with the edge servers;
the allocation module is used for completing the calculation resource allocation of each edge server based on the determined matching task and the transaction price corresponding to each pair of successfully matched user equipment and the edge server combination;
The double auction resource transaction model aims at maximizing the number of successfully matched user equipment and edge servers, takes profits obtained by processing single tasks by unloading the user equipment to the edge servers, profits obtained by processing the single tasks by the edge servers and the price of the accomplishment as constraint conditions, and matches an optimization model of the edge servers for the tasks to be unloaded of the user equipment;
the user equipment offloads a single task to the edge server to process, and the obtained benefits are determined according to the bidding information of the user equipment on the edge server, the price paid by the user equipment for the price of the transaction and the distance cost between the user equipment and the edge server;
the matching module is used for:
according to the bidding information, bidding of each user equipment on different edge servers is arranged in descending order, and a bidding set is obtained;
according to the task information to be offloaded, the processable task information and the ask price information, starting from the first bid in the bid set, sequentially judging whether an intersection task exists between the residual processable task set of the target edge server corresponding to the target bid and the residual task set to be offloaded of the target user equipment corresponding to the target bid;
If the target bid is larger than the ask price of the target edge server, determining the target user equipment and the target edge server combination as candidate matching combination, determining the intersection task as a matching task corresponding to the candidate matching combination, and determining the highest competitive bid of other user equipment to the target edge server for the intersection task according to other bids positioned behind the target bid in the bid set;
determining that the target bid is greater than or equal to the sum of the highest competitive bid and the maximum distance cost, determining the candidate matching combination as a pair of successfully matched user equipment and edge server combination, determining the intersection task as a matching task corresponding to the pair of successfully matched user equipment and edge server combination, and determining the highest competitive bid as an exchange price corresponding to the pair of successfully matched user equipment and edge server combination; wherein the maximum distance cost is determined according to a distance maximum value and a cost per unit distance in the distance information;
the expression of the double auction resource transaction model is as follows:
Where x represents the number of buyers, c represents the number of sellers, s i,j Representing a combination of buyer i and seller j, S w Representing a set of winners,representing the service benefit of buyer i by loading task r into seller j +.>Representing the benefit of seller j by providing service to buyer i->Representing payment of buyer i for a single application corresponding to task r provided by seller j,/>Representing the payment made by seller j for buyer i by a single application providing corresponding task r, X w Representing winning buyer collections, Y w Sets of sellers representing winning s i,r =1 indicates that the task r of buyer i successfully matches the seller.
5. The edge computing resource allocation device of claim 4, wherein the benefit obtained by the user device offloading a single task to an edge server for processing is determined by the following formula:
wherein,representing the benefits obtained by the user equipment i in offloading the task r to the edge server for processing, c i,j Bid indicating that user equipment i is offloading to edge server j for a single task to handle, +.>Representing the price to be paid by the user equipment i in the transaction price assuming that the task r to be offloaded of the user equipment i is successfully matched with the edge server j, τ represents the cost per unit distance, e i,j Representing the distance between the user device i and the edge server j.
6. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the edge computing resource allocation method of any one of claims 1 to 3 when the program is executed by the processor.
7. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the edge computing resource allocation method according to any of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111320858.2A CN114047971B (en) | 2021-11-09 | 2021-11-09 | Edge computing resource allocation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111320858.2A CN114047971B (en) | 2021-11-09 | 2021-11-09 | Edge computing resource allocation method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114047971A CN114047971A (en) | 2022-02-15 |
| CN114047971B true CN114047971B (en) | 2023-12-08 |
Family
ID=80207675
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111320858.2A Active CN114047971B (en) | 2021-11-09 | 2021-11-09 | Edge computing resource allocation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114047971B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115022189B (en) * | 2022-05-31 | 2024-03-26 | 武汉大学 | Edge user allocation model construction method, device, equipment and readable storage medium |
| CN115328650B (en) * | 2022-08-11 | 2023-08-25 | 杭州电子科技大学 | Edge node distribution method for maximizing system profit based on intelligent contracts |
| CN116366661A (en) * | 2023-06-02 | 2023-06-30 | 江西师范大学 | Collaborative edge user allocation method based on blockchain and auction theory |
| CN116886706B (en) * | 2023-09-07 | 2023-11-28 | 典基网络科技(上海)有限公司 | Application program placement method and device, electronic equipment and storage medium |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108596746A (en) * | 2018-04-20 | 2018-09-28 | 东南大学 | Mobile intelligent perception and its resource allocation based on two way auction and incentive mechanism method |
| CN109714797A (en) * | 2019-02-18 | 2019-05-03 | 南京邮电大学 | A kind of mobile edge network resource allocation methods based on Game Theory |
| CN109819047A (en) * | 2019-02-26 | 2019-05-28 | 吉林大学 | A kind of mobile edge calculations resource allocation methods based on incentive mechanism |
| CN110276670A (en) * | 2019-05-16 | 2019-09-24 | 广东工业大学 | A kind of computational resource allocation method of task based access control migration |
| CN110417872A (en) * | 2019-07-08 | 2019-11-05 | 深圳供电局有限公司 | A kind of edge network resource allocation methods towards mobile block chain |
| CN111026547A (en) * | 2019-11-28 | 2020-04-17 | 云南大学 | Edge computing server resource allocation method based on auction mechanism |
| CN111274037A (en) * | 2020-01-21 | 2020-06-12 | 中南大学 | Method and system for unloading edge computing task |
| CN111835827A (en) * | 2020-06-11 | 2020-10-27 | 北京邮电大学 | Internet of things edge computing task unloading method and system |
| CN111901400A (en) * | 2020-07-13 | 2020-11-06 | 兰州理工大学 | Edge computing network task unloading method equipped with cache auxiliary device |
| CN111949409A (en) * | 2020-08-20 | 2020-11-17 | 全球能源互联网研究院有限公司 | Method and system for unloading calculation tasks in electric wireless heterogeneous network |
| CN112559187A (en) * | 2020-12-22 | 2021-03-26 | 杭州电子科技大学 | Method and system for dynamically allocating tasks to mobile edge computing server |
| CN113225377A (en) * | 2021-03-30 | 2021-08-06 | 北京中电飞华通信有限公司 | Internet of things edge task unloading method and device |
| CN113377516A (en) * | 2021-06-22 | 2021-09-10 | 华南理工大学 | Centralized scheduling method and system for unloading vehicle tasks facing edge computing |
| CN113515378A (en) * | 2021-06-28 | 2021-10-19 | 国网河北省电力有限公司雄安新区供电公司 | Method and device for migration and calculation resource allocation of 5G edge calculation task |
| CN113535408A (en) * | 2021-08-02 | 2021-10-22 | 东北大学 | Auction-type optimization method for edge side computing resources |
| CN113543055A (en) * | 2021-06-30 | 2021-10-22 | 云南大学 | Resource allocation method in vehicle edge calculation based on bidirectional auction mechanism |
| CN113553165A (en) * | 2020-04-23 | 2021-10-26 | 东北大学秦皇岛分校 | Game theory-based mobile edge computing task unloading and resource scheduling method |
-
2021
- 2021-11-09 CN CN202111320858.2A patent/CN114047971B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108596746A (en) * | 2018-04-20 | 2018-09-28 | 东南大学 | Mobile intelligent perception and its resource allocation based on two way auction and incentive mechanism method |
| CN109714797A (en) * | 2019-02-18 | 2019-05-03 | 南京邮电大学 | A kind of mobile edge network resource allocation methods based on Game Theory |
| CN109819047A (en) * | 2019-02-26 | 2019-05-28 | 吉林大学 | A kind of mobile edge calculations resource allocation methods based on incentive mechanism |
| CN110276670A (en) * | 2019-05-16 | 2019-09-24 | 广东工业大学 | A kind of computational resource allocation method of task based access control migration |
| CN110417872A (en) * | 2019-07-08 | 2019-11-05 | 深圳供电局有限公司 | A kind of edge network resource allocation methods towards mobile block chain |
| CN111026547A (en) * | 2019-11-28 | 2020-04-17 | 云南大学 | Edge computing server resource allocation method based on auction mechanism |
| CN111274037A (en) * | 2020-01-21 | 2020-06-12 | 中南大学 | Method and system for unloading edge computing task |
| CN113553165A (en) * | 2020-04-23 | 2021-10-26 | 东北大学秦皇岛分校 | Game theory-based mobile edge computing task unloading and resource scheduling method |
| CN111835827A (en) * | 2020-06-11 | 2020-10-27 | 北京邮电大学 | Internet of things edge computing task unloading method and system |
| CN111901400A (en) * | 2020-07-13 | 2020-11-06 | 兰州理工大学 | Edge computing network task unloading method equipped with cache auxiliary device |
| CN111949409A (en) * | 2020-08-20 | 2020-11-17 | 全球能源互联网研究院有限公司 | Method and system for unloading calculation tasks in electric wireless heterogeneous network |
| CN112559187A (en) * | 2020-12-22 | 2021-03-26 | 杭州电子科技大学 | Method and system for dynamically allocating tasks to mobile edge computing server |
| CN113225377A (en) * | 2021-03-30 | 2021-08-06 | 北京中电飞华通信有限公司 | Internet of things edge task unloading method and device |
| CN113377516A (en) * | 2021-06-22 | 2021-09-10 | 华南理工大学 | Centralized scheduling method and system for unloading vehicle tasks facing edge computing |
| CN113515378A (en) * | 2021-06-28 | 2021-10-19 | 国网河北省电力有限公司雄安新区供电公司 | Method and device for migration and calculation resource allocation of 5G edge calculation task |
| CN113543055A (en) * | 2021-06-30 | 2021-10-22 | 云南大学 | Resource allocation method in vehicle edge calculation based on bidirectional auction mechanism |
| CN113535408A (en) * | 2021-08-02 | 2021-10-22 | 东北大学 | Auction-type optimization method for edge side computing resources |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114047971A (en) | 2022-02-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114047971B (en) | Edge computing resource allocation method and device | |
| Sun et al. | Joint resource allocation and incentive design for blockchain-based mobile edge computing | |
| Zhang et al. | Dynamic resource provisioning in cloud computing: A randomized auction approach | |
| Li et al. | Reinforcement-learning-and belief-learning-based double auction mechanism for edge computing resource allocation | |
| US6718312B1 (en) | Method and system for combinatorial auctions with bid composition restrictions | |
| US20140289076A1 (en) | Belief propagation for generalized matching | |
| US20090006115A1 (en) | Establishing and updating reputation scores in online participatory systems | |
| US20220164791A1 (en) | Method for distributing collectables ownership based on blockchain networks and online transaction server using the same | |
| US20030093357A1 (en) | Method and system for automated bid advice for auctions | |
| CN109064146A (en) | A kind of digital cash method of commerce, equipment, system, terminal and client wallet | |
| CN108537576A (en) | Community's advertisement placement method, device, terminal device and storage medium | |
| KR20020026880A (en) | Systems and methods for electronic trading that provide incentives and linked auctions | |
| CN106408361A (en) | Method and apparatus for matching buyers with sellers in a marketplace to facilitate trade | |
| US20180315118A1 (en) | Real estate trade system and operating method thereof | |
| Bolton et al. | Does laboratory trading mirror behavior in real world markets? Fair bargaining and competitive bidding on eBay | |
| WO2010085800A1 (en) | Method and system for conducting a participation award based auction | |
| JP2011510373A (en) | Distributed ranking and message matching | |
| Yue et al. | A double auction-based approach for multi-user resource allocation in mobile edge computing | |
| CN107705097A (en) | Order management method, device, electronic equipment and computer-readable recording medium | |
| US20160092979A1 (en) | System and method to optimize discount setting utilizing flexible pricing and continuous product time value decay | |
| Zhang et al. | Incentive mechanism with task bundling for mobile crowd sensing | |
| Xu et al. | Hierarchical combinatorial auction in computing resource allocation for mobile blockchain | |
| CA3037400C (en) | Method and device for disseminating product information | |
| CN108596763A (en) | A kind of method of commerce and device based on block chain | |
| Li et al. | Resource allocation for mobile blockchain: A hierarchical combinatorial auction approach |
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 |