CN114047971A - Edge computing resource allocation method and device - Google Patents
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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 unloaded of each user equipment, processable task information of each edge server, bid information of each user equipment unloaded to different edge servers for processing aiming at a single task, ask information of each edge server for processing the single task and distance information between each user equipment and each edge server; determining matching tasks and transaction prices corresponding to each pair of successfully matched user equipment and edge server combinations based on task information to be unloaded, processable task information, bid information, price information, distance information and a double-shot resource transaction model; and completing the computing resource allocation of each edge server based on the matching task and the bargaining price corresponding to each pair of the combination of the user equipment and the edge server with successful matching. Therefore, the 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, a new computing mode, namely edge computing, is generated, the edge computing is performed near a data source, the data can be loaded, stored, cached and processed, and the like, 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 with each other to allocate resources to mobile devices (which may also be 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 to be solved in the industry.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an edge computing resource allocation method and device.
In a first aspect, the present invention provides a method for allocating edge computing resources, including:
acquiring task information to be unloaded of each user equipment, processable task information of each edge server, bid information of each user equipment unloaded to different edge servers for processing aiming at a single task, ask information of each edge server for processing the single task and distance information between each user equipment and each edge server;
matching an edge server for the task to be unloaded of each user device based on the task information to be unloaded, the processable task information, the bid information, the ask price information, the distance information and the double auction resource transaction model, and determining a matching task and a bargaining price corresponding to each pair of user device and edge server combination which are successfully matched;
completing the calculation resource allocation of each edge server based on the matching task and the bargaining price corresponding to each pair of combination of the user equipment and the edge server which are successfully matched;
the double-auction resource transaction model is an optimization model matched with the edge server for the tasks to be unloaded of each user device, wherein the double-auction resource transaction model aims at maximizing the number of successfully matched user devices and edge server combinations, and takes gains obtained by unloading a single task to the edge server by the user devices for processing, gains obtained by processing the single task by the edge server and a transaction price as constraint conditions;
the revenue obtained by the user device offloading the single task to the edge server for processing is determined according to the bid information of the user device to the edge server, the price paid by the user device at the deal price, and the distance cost between the user device and the edge server.
Optionally, the benefit obtained by the user equipment offloading the single task to the edge server for processing is determined by the following formula:
wherein the content of the first and second substances,
representing the benefit obtained by the user device i offloading the task r to the edge server for processing, ci,jA bid representing the offloading of a user device i to an edge server j for processing for a single task,
representing the price to be paid by the user equipment i in the transaction price on the assumption that the task r to be unloaded of the user equipment i is successfully matched with the edge server j, tau represents the cost of unit distance, ei,jRepresenting the distance between the user device i and the edge server j.
Optionally, the matching an edge server for the to-be-offloaded task of each user equipment based on the to-be-offloaded task information, the processable task information, the bid information, the ask price information, the distance information, and the double-auction resource transaction model, and determining a matching task and a deal price corresponding to each pair of successfully-matched user equipment and edge server combinations, includes:
arranging the bids of the user equipment to different edge servers according to the bid information in a descending order to obtain a bid set;
according to the task information to be unloaded, the processable task information and the asking price information, starting from the first bid in the bid set, sequentially judging 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 unloaded of the target user equipment corresponding to the target bid;
if the target bid price is larger than the ask price of the target edge server, determining the combination of the target user equipment and the target edge server as a candidate matching combination, determining the intersection task as a matching task corresponding to the candidate matching combination, and determining the highest competitive bid price of other user equipment on the target edge server for the intersection task according to other bid prices after the target bid price in the bid price 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 user equipment and edge server combination which are successfully matched, determining the intersection task as a matching task corresponding to the pair of user equipment and edge server combination which are successfully matched, and determining the highest competitive bid as a transaction price corresponding to the pair of user equipment and edge server combination which are successfully matched; wherein the maximum distance cost is determined according to a maximum distance value in the distance information and a cost per distance.
Optionally, each of the user equipment and each of the edge servers are nodes on a block chain.
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 unloaded of each user equipment, task information processable by each edge server, bid information which is unloaded to different edge servers by each user equipment for processing a single task, ask information for processing the single task by each edge server and distance information between each user equipment and each edge server;
the matching module is used for matching an edge server for the task to be unloaded of each user device based on the task information to be unloaded, the processable task information, the bid information, the ask price information, the distance information and the double-shot resource transaction model, and determining a matching task and a bargaining price corresponding to each pair of successfully matched user devices and edge server combinations;
the distribution module is used for completing the calculation resource distribution of each edge server based on the matching task and the bargaining price corresponding to each pair of combination of the user equipment and the edge server which is successfully matched;
the double-auction resource transaction model is an optimization model matched with the edge server for the tasks to be unloaded of each user device, wherein the double-auction resource transaction model aims at maximizing the number of successfully matched user devices and edge server combinations, and takes gains obtained by unloading a single task to the edge server by the user devices for processing, gains obtained by processing the single task by the edge server and a transaction price as constraint conditions;
the revenue obtained by the user device offloading the single task to the edge server for processing is determined according to the bid information of the user device to the edge server, the price paid by the user device at the deal price, and the distance cost between the user device and the edge server.
Optionally, the benefit obtained by the user equipment offloading the single task to the edge server for processing is determined by the following formula:
wherein the content of the first and second substances,
representing the benefit obtained by the user device i offloading the task r to the edge server for processing, ci,jA bid representing the offloading of a user device i to an edge server j for processing for a single task,
representing the price to be paid by the user equipment i in the transaction price on the assumption that the task r to be unloaded of the user equipment i is successfully matched with the edge server j, tau represents the cost of unit distance, ei,jRepresenting the distance between the user device i and the edge server j.
Optionally, the matching module is configured to:
arranging the bids of the user equipment to different edge servers according to the bid information in a descending order to obtain a bid set;
according to the task information to be unloaded, the processable task information and the asking price information, starting from the first bid in the bid set, sequentially judging 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 unloaded of the target user equipment corresponding to the target bid;
if the target bid price is larger than the ask price of the target edge server, determining the combination of the target user equipment and the target edge server as a candidate matching combination, determining the intersection task as a matching task corresponding to the candidate matching combination, and determining the highest competitive bid price of other user equipment on the target edge server for the intersection task according to other bid prices after the target bid price in the bid price 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 user equipment and edge server combination which are successfully matched, determining the intersection task as a matching task corresponding to the pair of user equipment and edge server combination which are successfully matched, and determining the highest competitive bid as a transaction price corresponding to the pair of user equipment and edge server combination which are successfully matched; wherein the maximum distance cost is determined according to a maximum distance value in the distance information and a cost per distance.
In a third aspect, the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the processor implements the steps of the edge computing resource allocation method according to the first aspect.
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 as described above in the first aspect.
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 one of the above.
According to the edge computing resource allocation method and device provided by the invention, the edge server is matched for the task to be unloaded of each user equipment through the double-shot 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 server, the task allocation requirement is completed as much as possible, and the efficiency of the system is improved.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a flow chart of an edge computing resource allocation method according to the present invention;
FIG. 2 is a schematic diagram of a block chain-based resource transaction architecture of the Internet of things provided by the invention;
FIG. 3 is a time consuming comparison of the double auction algorithm and the random selection algorithm provided by the present invention;
FIG. 4 is a graph comparing the number of tasks completed by the double auction algorithm and the random selection algorithm provided by the present invention;
FIG. 5 is a schematic structural diagram of an edge computing resource allocation apparatus provided in the present invention;
fig. 6 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of an edge computing resource allocation method provided in the present invention, as shown in fig. 1, the method includes the following steps:
specifically, in a scenario of performing edge computing resource allocation, two parts, namely an edge server and a user equipment, are mainly included. 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 unload tasks required to be processed to the edge server for processing.
In the embodiment of the invention, the resource transaction is based on a classic auction mechanism, but the resource transaction is different from the classic auction in that a buyer and a seller are considered, namely, a double auction mechanism is adopted in the invention, the classic auction model is favorable for the seller, the benefit of the buyer is ignored, and the double auction mechanism is considered in balance, so that a scheme which is more satisfied by both parties is selected. The double auction is a process of trading commodities, in which sellers submit their asking prices, buyers submit their bidding prices to auction sellers, and auction sellers determine successful buyers and sellers, bargained commodities and prices according to certain strategies. The invention takes the edge server as a seller, the user equipment as a buyer, the computing resources of the edge server are commodities, and a trusted third party (for example, the edge server can be used) serves as a seller to distribute the computing resources of the edge.
In the process of allocating the edge computing resources, an auction side needs to acquire task information to be unloaded of each user equipment, task information processable by each edge server, bid information for each user equipment to unload a single task to different edge servers for processing, ask information for each edge server to process the single task, and distance information between each user equipment and each edge server.
Wherein, for different task types, the bids of the same user device i to the same edge server j are the same. The ask for edge server j is the same for different task types.
Considering the time delay influence generated by the distance between the user equipment and the edge server, the invention also adds the distance factor when distributing the edge computing resource, and the auction side needs to obtain the distance information between each user equipment and each edge server.
specifically, after acquiring task information to be offloaded of each user device, task information processable by each edge server, bid information for offloading each user device to different edge servers for processing a single task, ask information for processing a single task by each edge server, and distance information between each user device and each edge server, respectively, the auctioneer may match the edge servers for the tasks to be offloaded of each user device based on the information and the double-auction resource transaction model, and determine a matching task and a bargaining price corresponding to each pair of user devices and edge server combinations that are successfully matched.
The matching task refers to a task that the user equipment can successfully unload to the edge server in each pair of combination of the user equipment and the edge server, wherein the matching task is successful. The user equipment may offload its different tasks to be offloaded to different edge servers, respectively.
The transaction price may include the price of the item that the user device needs to pay at the time of the transaction, and the price of the item that is available to the edge server.
The double-auction resource transaction model in the embodiment of the invention takes the maximization of the number of successfully matched user equipment and edge server combinations as a target, takes the income obtained by unloading a single task to the edge server by the user equipment for processing, the income obtained by processing the single task by the edge server and the bargaining price as constraint conditions, and matches an optimization model of the edge server for the task to be unloaded of each user equipment; the profit obtained by the user equipment unloading the single task to the edge server for processing is determined according to the bid information of the user equipment on the edge server, the price paid by the user equipment at the transaction price 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 can be determined by the following formula:
wherein the content of the first and second substances,
representing the benefit obtained by the user device i offloading the task r to the edge server for processing, ci,jA bid representing the offloading of a user device i to an edge server j for processing for a single task,
representing the price to be paid by the user equipment i in the transaction price on the assumption that the task r to be unloaded of the user equipment i is successfully matched with the edge server j, tau represents the cost of unit distance, ei,jRepresenting the distance between the user device i and the edge server j.
And 102, completing the calculation resource allocation of each edge server based on the matching task and the bargaining price corresponding to each pair of the combination of the user equipment and the edge server with successful matching.
Specifically, after determining the matching task and the bargaining price corresponding to each pair of successfully matched user equipment and edge server combinations, 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 reward that can be obtained by the edge servers, and accordingly, the computing resource allocation of each edge server is completed.
According to the edge computing resource allocation method provided by the invention, the edge server is matched for the task to be unloaded of each user equipment through the double-beat 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 server, the task allocation requirement is completed as much as possible, and the efficiency of the system is improved.
Optionally, matching an edge server for the task to be offloaded of each 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 a matching task and a bargaining price corresponding to each pair of user equipment and edge server combinations that are successfully matched, includes:
according to the bid information, arranging bids of the user equipment on different edge servers in a descending order to obtain a bid set;
according to the task information to be unloaded, the processable task information and the asking price information, starting from the first bid in the bid set, sequentially judging 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 unloaded of the target user equipment corresponding to the target bid;
if the bidding price exists and the target bidding price is greater than the asking price of the target edge server, determining the combination of the target user equipment and the target edge server as a candidate matching combination, determining the intersection task as a matching task corresponding to the candidate matching combination, and determining the highest competitive bidding price of other user equipment on the target edge server for the intersection task according to other bidding prices after the target bidding price in the bidding price 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 user equipment and edge server combination which are successfully matched, determining the intersection task as a matching task corresponding to the pair of user equipment and edge server combination which are successfully matched, and determining the highest competitive bid as a transaction price corresponding to the pair of user equipment and edge server combination which are successfully matched; wherein the maximum distance cost is determined according to the maximum distance value in the distance information and the cost per distance.
Specifically, in the embodiment of the present invention, after the auction side obtains task information to be offloaded of each user device, task information processable by each edge server, bid information offloaded by each user device to different edge servers for a single task to be processed, ask information processed by each edge server for a single task, and distance information between each user device and each edge server, the bids of each user device on different edge servers may be arranged in a descending order according to the bid information to obtain a bid set.
Then, starting from the first bid in the bid set, sequentially judging whether intersection tasks exist between the remaining processable task sets of the target edge server corresponding to the target bid and the remaining task sets to be unloaded of the target user equipment corresponding to the target bid.
For example, if the first bid, i.e., the highest bid, in the set of bids is the bid of user device i on edge server j, and the tasks to be offloaded of the user device 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, and 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 set of processable tasks remaining after eliminating the intersection task of the first bid, and the set of remaining tasks to be offloaded for user device i is r2, and in this way, whether intersection tasks exist between the residual processable task sets of the target edge server corresponding to the target bid and the residual task sets to be unloaded of the target user equipment corresponding to the target bid is sequentially judged.
Taking the first bid as an example, if the first bid, that is, the bid of the user device i on the edge server j is greater than the ask price of the edge server j, the combination of the user device 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 bid of the other user devices on the edge server j for the intersection tasks r1 and r3 may be determined according to other bids 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, their bid on edge server j is their competing bid for task r1, and for user device i, the highest of these competing bids is the highest competing bid for other user devices for task r 1.
Then, considering the distance cost of the user equipment, the auctioning party 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 pair of user equipment and edge server combination which are successfully matched, determine the intersection task as a matching task corresponding to the pair of user equipment and edge server combination which are successfully matched, and determine the highest competitive bid as a transaction price corresponding to the pair of user equipment and edge server combination which are successfully matched. It should be noted that, the competing bids and the determination that the target bid is greater than or equal to the sum of the highest competing bid and the maximum distance cost are all for a single task, and if there are multiple intersecting tasks, it may happen that only one or more of the intersecting tasks are finally determined as the matching task.
The determination of the highest competitive bid as the bargaining price corresponding to the pair of the user equipment and the edge server combination which are successfully matched means that in the pair of the user equipment and the edge server combination which are successfully matched, the price which the user equipment needs to pay is the highest competitive bid, and the actual bid of the user equipment is not required.
The maximum distance cost may be determined according to a maximum distance value in the distance information and a cost per distance, and may be calculated by multiplying a maximum value in a distance set of each user equipment and each edge server by a cost per distance, for example.
Optionally, each user equipment and each edge server are nodes on the blockchain.
Specifically, the blockchain technology is a shared distributed account book, data on the account book are all public and cannot be tampered, cryptography in the blockchain technology can enable the data to be incapable of being modified once the data is linked up, a common recognition mechanism and the like can avoid traditional centralized ideas, nodes on the blockchain can check the data of the whole network at any time and any place without the help of other people 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 block chain, and the resource allocation process can be executed in the form of an intelligent contract in the block chain, so that a distributed, non-resistance, safe and fair resource allocation consensus mechanism is realized.
The above-mentioned edge computing resource allocation method is exemplified by specific embodiments below. In the embodiment, an internet of things edge computing resource transaction process based on a block chain is introduced, the internet of things edge computing resource transaction process is simulated according to a double auction algorithm, and intelligent contracts are compiled through an Ethernet workshop tool in the block chain technology to simulate resource transaction work.
Fig. 2 is a schematic diagram of a general architecture of resource transaction of the internet of things based on a block chain, as shown in fig. 2, the scenario includes two parts, namely an edge server and a user equipment. The devices are distributed dispersedly and have certain time delay characteristics. At the time of pre-auction, each user device publishes its own exact task requirements and distance factors between device nodes, and for the set of vendors represented by the edge server, what needs to be published is the application deployed therein that accomplishes the exact task.
Then, an auction is performed, in which a buyer (i.e. user equipment) and a seller (i.e. edge server) respectively make a bid and ask, and the same task may have demands of multiple buyers and the same task may have multiple sellers deployed, so that different tasks, different user equipment and edge server, have different bids and asks.
A double auction refers to both buyers and sellers submitting respective bids to an auctioneer, and the auctioning seller then determines an auction price according to certain policies to facilitate the transaction. In order to avoid a potential malicious attacker, the edge server selected as the verifier is used as the auctioneer, determines the set of winners (i.e. the user equipment and the edge server which are successfully matched) and trades the selected price of each buyer and each seller of the set of winners. Given the transaction information, key information is determined including a winning buyer set, a winning seller set, a distance matrix, a task matrix, a set of payments (i.e., payments by user devices in the transaction price), a set of payments (i.e., collections by edge servers in the transaction price), etc.
In order to make a pricing strategy, it is necessary toDetermining benefits z of buyers and sellersi,j. Suppose that the buyer i pays for a single application (corresponding to task r) provided by the seller j as
Buyer i bids on seller j as ci,j. Considering the influence of time delay, adding a distance factor, wherein tau is the cost per distance, ei,jIs the distance between buyer i and seller j, the service benefit that buyer i obtains by loading task r to seller j
The definition is as follows:
suppose mjIs the cost of seller j to process a single task, the amount of money that seller j receives in providing buyer i with a single application (corresponding to task r) is
Revenue obtained by seller j through providing service to buyer i
The definition is as follows:
in order to ensure that neither party of the transaction loses money and the whole transaction process is adverse, namely, the payment party is not allowed to subsidize the transaction. In this embodiment, the double auction resource transaction model expression is as follows:
where x denotes the number of buyers, c denotes the number of sellers, si,jRepresenting a combination of buyer i and seller j, SwSet of winners, XwIndicating a winning set of buyers, YwIndicating a winning set of sellers, si,rA successful match to the seller for the task r of buyer i is indicated by 1.
Equation (3) represents that the model is targeted at maximizing the number of winner combinations; equation (4) ensures that any participant in the auction will not lose costs; equation (5) ensures that the price paid by the buyer must be greater than or equal to the revenue the seller receives each time the seller successfully completes the task; equation (6) ensures that at the end of the auction, the buyer receives more benefit than the seller. The present embodiment uses the blockchain driven auction theory to solve the resource allocation problem, the blockchain technology uses the encryption authentication technology and the consensus mechanism to avoid the agent risk, and the auction theory can complete the loading and resource allocation tasks in a short time without causing excessive system loss.
The following is the core algorithm pseudo code for resource allocation provided by this embodiment:
where C represents the buyer's bid collection (the elements in its collection are denoted by C subscripts) and Q represents what the seller is to havePrice set (elements in its set are denoted by q subscript), U denotes the set of tasks to be offloaded by the buyer (elements in its set are denoted by U subscript), T denotes the set of tasks processable by the seller (elements in its set are denoted by T subscript), E denotes a distance matrix (elements in its set are denoted by E subscript), P denotes a distance matrix (elements in its set are denoted by E subscript), andurepresenting a winning set of purchaser payments (the elements in which are set denoted by puSubscripted representation), PsRepresenting a winning seller's collection of collections (the elements in which are denoted by p)sDenoted by subscript).
The double-auction algorithm firstly carries out descending sort on the distance matrix to obtain E ', inquires the maximum value in the E' to obtain the maximum distance value
Then, the buyer bidding sets are sorted in a descending order to obtain C ', elements in the C' are traversed in the order, resources are distributed according to the newly sorted order to provide corresponding candidate buyers, the buyer tasks are loaded into the sellers, then other candidates with the tasks (namely competitors of the candidate buyers) are found, each buyer bids for the sellers, and one of the highest bids is used as a basis for the buyers to acquire the loading tasks. If all of the buyer's tasks have been completed, the subsequent bids associated with the buyer are deleted. The remaining elements operate in the same manner, taking into account the distance cost of the buyer, to arrive at a combination of winners and corresponding pricing combination.
The results of the simulation experiments provided in this example are described below.
The invention adopts java language to simulate the resource transaction process on an Eclipse platform. User devices that do not have a task to load (hereinafter referred to as users) will not submit bids and will not participate in the auction. Each edge server (hereinafter referred to as a server) is connected directly or indirectly through a wired link. The prices and configurations for buyers and sellers are randomly generated in a uniformly distributed fashion. It is assumed that all servers can only handle four types of tasks and that each server can handle some or all types of tasks.
Resource transaction simulation is carried out on two design algorithms by using java language, the default of task types which can be processed on Eclipse is 4, other data volume changes are in the number of servers and users, eight groups of data are set to enable the two algorithms to be compared, initialization data random generation is carried out on a bid collection, a price collection, a task collection, a deployment collection and the like, the number of users and the number of servers are firstly determined, the data volume of the eight groups of users and the servers is shown in table 1, the bid value range of each user to each server is [1,10], each group of data is collected by one folder, files with the number of the corresponding users are arranged under each folder, the nth row in the ith file represents the bid of the user i to the server v, corresponding data random initial generation is carried out on other variables in a similar mode, and all data are stored in the files.
For the buyer's task set and the seller's deployment set, if the task needs and the application program with the task is loaded, the value is 1, otherwise, the value is 0, the value is taken in the {0, 1} for the set with the service record in the same way, for the bid set, the user with the task needs has a bid value range within [1,10], and if not, the value is 0 without the bid.
Comparing a double auction mechanism algorithm with a random selection algorithm, wherein the core idea of the random selection algorithm is to randomly select a user and a server, if the selected server just meets the requirements of the user, the allocation is carried out, and the two algorithms carry out scheme allocation under the same data. The performance and efficiency of the algorithm are compared in the two aspects of the time consumption of the algorithm and the number of the tasks to be completed, the time consumption of the algorithm is shown in table 2, and the number of the tasks to be completed by the algorithm is shown in table 3.
TABLE 1 Experimental data set
TABLE 2 algorithm time consuming case
TABLE 3 number of tasks completed by the Algorithm
Fig. 3 is a time consumption comparison diagram of a double auction algorithm and a random selection algorithm provided by the present invention, fig. 4 is a comparison diagram of the number of tasks completed by the double auction algorithm and the random selection algorithm provided by the present invention, and in combination with fig. 3 and fig. 4, in terms of algorithm time consumption, as the number of users increases, the number of tasks increases, and the execution time becomes longer and longer. When the number of tasks is small, the time-consuming difference between the two algorithms is small. However, as the number of tasks increases, the algorithm time consumption becomes far from obvious. The time complexity of the dual auction algorithm can be analyzed as O (xc)2) The time complexity of the stochastic algorithm is O (x + c + xc)2) The time complexity of the former algorithm is lower than that of the latter algorithm, so the execution time of the double-auction algorithm is smaller than that of the random selection algorithm.
As can be seen from the simulation results, the double-auction algorithm provided by the invention is superior to the random selection algorithm in the aspects of performance and efficiency, and the system can be more efficient in practical application.
The following describes the edge computing resource allocation apparatus provided by the present invention, and the edge computing resource allocation apparatus described below and the edge computing resource allocation method described above may be referred to correspondingly.
Fig. 5 is a schematic structural diagram of an edge computing resource allocation apparatus provided in the present invention, as shown in fig. 5, the apparatus includes:
an obtaining module 500, configured to obtain task information to be offloaded of each user equipment, task information processable by each edge server, bid information for offloading each user equipment to different edge servers for processing a single task, ask information for processing a single task by each edge server, and distance information between each user equipment and each edge server;
a matching module 510, configured to match an edge server for the to-be-offloaded task of each user equipment based on the to-be-offloaded task information, the processable task information, the bid information, the ask information, the distance information, and the double-auction resource transaction model, and determine a matching task and a bargain price corresponding to each pair of user equipment and edge server combinations that are successfully matched;
an allocation module 520, configured to complete the allocation of computing resources of each edge server based on the matching task and the deal price corresponding to each pair of combinations of the user equipment and the edge server with successful matching determined;
the double-beat resource transaction model is an optimization model matched with the edge server for the tasks to be unloaded of each user device, wherein the double-beat resource transaction model aims at maximizing the number of successfully matched user devices and edge server combinations, and takes gains obtained by unloading a single task to the edge server by the user devices for processing, gains obtained by processing the single task by the edge server and a bargaining price as constraint conditions;
the revenue obtained by the user device offloading the single task to the edge server for processing is determined based on the bid information of the user device to the edge server, the price paid by the user device at the deal price, and the distance cost between the user device and the edge server.
Optionally, the benefit obtained by the user equipment offloading the single task to the edge server for processing is determined by the following formula:
wherein the content of the first and second substances,
representing the benefit obtained by the user device i offloading the task r to the edge server for processing, ci,jA bid representing the offloading of a user device i to an edge server j for processing for a single task,
the method comprises the steps that the task r to be unloaded of the user equipment i is matched with the edge server j successfully, the price to be paid by the user equipment i is represented by a transaction price, and tau represents a sheetCost of bit distance, ei,jRepresenting the distance between the user device i and the edge server j.
Optionally, the matching module 510 is configured to: according to the bid information, arranging bids of the user equipment on different edge servers in a descending order to obtain a bid set; according to the task information to be unloaded, the processable task information and the asking price information, starting from the first bid in the bid set, sequentially judging 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 unloaded of the target user equipment corresponding to the target bid; if the bidding price exists and the target bidding price is greater than the asking price of the target edge server, determining the combination of the target user equipment and the target edge server as a candidate matching combination, determining the intersection task as a matching task corresponding to the candidate matching combination, and determining the highest competitive bidding price of other user equipment on the target edge server for the intersection task according to other bidding prices after the target bidding price in the bidding price 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 user equipment and edge server combination which are successfully matched, determining the intersection task as a matching task corresponding to the pair of user equipment and edge server combination which are successfully matched, and determining the highest competitive bid as a transaction price corresponding to the pair of user equipment and edge server combination which are successfully matched; wherein the maximum distance cost is determined according to the maximum distance value in the distance information and the cost per distance.
Optionally, each user equipment and each edge server are nodes on the blockchain.
It should be noted that, the apparatus provided in the present invention can implement all the method steps implemented by the method embodiments and achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as the method embodiments in this embodiment are omitted here.
Fig. 6 is a schematic structural diagram of an electronic device provided in the present invention, and as shown in fig. 6, the electronic device may include: a processor (processor)610, a communication Interface (Communications Interface)620, a memory (memory)630 and a communication bus 640, wherein the processor 610, the communication Interface 620 and the memory 630 communicate with each other via the communication bus 640. The processor 610 may call 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 unloaded of each user equipment, processable task information of each edge server, bid information of each user equipment unloaded to different edge servers for processing aiming at a single task, ask information of each edge server for processing the single task and distance information between each user equipment and each edge server; matching an edge server for the task to be unloaded of each user device based on the task information to be unloaded, the processable task information, the bid information, the price information, the distance information and the double-shot resource transaction model, and determining a matching task and a bargaining price corresponding to each pair of user device and edge server combination which are successfully matched; completing the calculation resource allocation of each edge server based on the matching task and the bargaining price corresponding to each pair of combination of the user equipment and the edge server which are successfully matched; the double-beat resource transaction model is an optimization model matched with the edge server for the tasks to be unloaded of each user device, wherein the double-beat resource transaction model aims at maximizing the number of successfully matched user devices and edge server combinations, and takes gains obtained by unloading a single task to the edge server by the user devices for processing, gains obtained by processing the single task by the edge server and a bargaining price as constraint conditions; the revenue obtained by the user device offloading the single task to the edge server for processing is determined based on the bid information of the user device to the edge server, the price paid by the user device at the deal price, and the distance cost between the user device and the edge server.
In addition, the logic instructions in the memory 630 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute 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), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention further provides a computer program product, the computer program product including a computer program, the computer program being stored on a non-transitory computer readable storage medium, wherein when the computer program is executed by a processor, the computer is capable of executing the steps of any one of the edge computing resource allocation methods provided in the above embodiments, for example: acquiring task information to be unloaded of each user equipment, processable task information of each edge server, bid information of each user equipment unloaded to different edge servers for processing aiming at a single task, ask information of each edge server for processing the single task and distance information between each user equipment and each edge server; matching an edge server for the task to be unloaded of each user device based on the task information to be unloaded, the processable task information, the bid information, the price information, the distance information and the double-shot resource transaction model, and determining a matching task and a bargaining price corresponding to each pair of user device and edge server combination which are successfully matched; completing the calculation resource allocation of each edge server based on the matching task and the bargaining price corresponding to each pair of combination of the user equipment and the edge server which are successfully matched; the double-beat resource transaction model is an optimization model matched with the edge server for the tasks to be unloaded of each user device, wherein the double-beat resource transaction model aims at maximizing the number of successfully matched user devices and edge server combinations, and takes gains obtained by unloading a single task to the edge server by the user devices for processing, gains obtained by processing the single task by the edge server and a bargaining price as constraint conditions; the revenue obtained by the user device offloading the single task to the edge server for processing is determined based on the bid information of the user device to the edge server, the price paid by the user device at the deal price, and the distance cost between the user device and the edge server.
In yet another aspect, the present invention further provides a non-transitory computer readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to perform the steps of any one of the edge computing resource allocation methods provided in the above embodiments, for example: acquiring task information to be unloaded of each user equipment, processable task information of each edge server, bid information of each user equipment unloaded to different edge servers for processing aiming at a single task, ask information of each edge server for processing the single task and distance information between each user equipment and each edge server; matching an edge server for the task to be unloaded of each user device based on the task information to be unloaded, the processable task information, the bid information, the price information, the distance information and the double-shot resource transaction model, and determining a matching task and a bargaining price corresponding to each pair of user device and edge server combination which are successfully matched; completing the calculation resource allocation of each edge server based on the matching task and the bargaining price corresponding to each pair of combination of the user equipment and the edge server which are successfully matched; the double-beat resource transaction model is an optimization model matched with the edge server for the tasks to be unloaded of each user device, wherein the double-beat resource transaction model aims at maximizing the number of successfully matched user devices and edge server combinations, and takes gains obtained by unloading a single task to the edge server by the user devices for processing, gains obtained by processing the single task by the edge server and a bargaining price as constraint conditions; the revenue obtained by the user device offloading the single task to the edge server for processing is determined based on the bid information of the user device to the edge server, the price paid by the user device at the deal price, and the distance cost between the user device and the edge server.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An edge computing resource allocation method, comprising:
acquiring task information to be unloaded of each user equipment, processable task information of each edge server, bid information of each user equipment unloaded to different edge servers for processing aiming at a single task, ask information of each edge server for processing the single task and distance information between each user equipment and each edge server;
matching an edge server for the task to be unloaded of each user device based on the task information to be unloaded, the processable task information, the bid information, the ask price information, the distance information and the double auction resource transaction model, and determining a matching task and a bargaining price corresponding to each pair of user device and edge server combination which are successfully matched;
completing the calculation resource allocation of each edge server based on the matching task and the bargaining price corresponding to each pair of combination of the user equipment and the edge server which are successfully matched;
the double-auction resource transaction model is an optimization model matched with the edge server for the tasks to be unloaded of each user device, wherein the double-auction resource transaction model aims at maximizing the number of successfully matched user devices and edge server combinations, and takes gains obtained by unloading a single task to the edge server by the user devices for processing, gains obtained by processing the single task by the edge server and a transaction price as constraint conditions;
the revenue obtained by the user device offloading the single task to the edge server for processing is determined according to the bid information of the user device to the edge server, the price paid by the user device at the deal price, and the distance cost between the user device and the edge server.
2. The method of claim 1, wherein the revenue obtained by the user equipment offloading a single task to an edge server for processing is determined by the following formula:
wherein the content of the first and second substances,
indicating that user equipment i offloads task r to edge server for processingGain obtained, ci,jA bid representing the offloading of a user device i to an edge server j for processing for a single task,
representing the price to be paid by the user equipment i in the transaction price on the assumption that the task r to be unloaded of the user equipment i is successfully matched with the edge server j, tau represents the cost of unit distance, ei，jRepresenting the distance between the user device i and the edge server j.
3. The method for allocating edge computing resources according to claim 1, wherein the matching an edge server for the task to be offloaded of each user device 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, and determining a matching task and a bargain price corresponding to each pair of user device and edge server combinations that are successfully matched comprises:
arranging the bids of the user equipment to different edge servers according to the bid information in a descending order to obtain a bid set;
according to the task information to be unloaded, the processable task information and the asking price information, starting from the first bid in the bid set, sequentially judging 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 unloaded of the target user equipment corresponding to the target bid;
if the target bid price is larger than the ask price of the target edge server, determining the combination of the target user equipment and the target edge server as a candidate matching combination, determining the intersection task as a matching task corresponding to the candidate matching combination, and determining the highest competitive bid price of other user equipment on the target edge server for the intersection task according to other bid prices after the target bid price in the bid price 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 user equipment and edge server combination which are successfully matched, determining the intersection task as a matching task corresponding to the pair of user equipment and edge server combination which are successfully matched, and determining the highest competitive bid as a transaction price corresponding to the pair of user equipment and edge server combination which are successfully matched; wherein the maximum distance cost is determined according to a maximum distance value in the distance information and a cost per distance.
4. The method according to any of claims 1 to 3, wherein each of the user equipments and each of the edge servers are nodes on a blockchain.
5. 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 unloaded of each user equipment, task information processable by each edge server, bid information which is unloaded to different edge servers by each user equipment for processing a single task, ask information for processing the single task by each edge server and distance information between each user equipment and each edge server;
the matching module is used for matching an edge server for the task to be unloaded of each user device based on the task information to be unloaded, the processable task information, the bid information, the ask price information, the distance information and the double-shot resource transaction model, and determining a matching task and a bargaining price corresponding to each pair of successfully matched user devices and edge server combinations;
the distribution module is used for completing the calculation resource distribution of each edge server based on the matching task and the bargaining price corresponding to each pair of combination of the user equipment and the edge server which is successfully matched;
the double-auction resource transaction model is an optimization model matched with the edge server for the tasks to be unloaded of each user device, wherein the double-auction resource transaction model aims at maximizing the number of successfully matched user devices and edge server combinations, and takes gains obtained by unloading a single task to the edge server by the user devices for processing, gains obtained by processing the single task by the edge server and a transaction price as constraint conditions;
the revenue obtained by the user device offloading the single task to the edge server for processing is determined according to the bid information of the user device to the edge server, the price paid by the user device at the deal price, and the distance cost between the user device and the edge server.
6. The apparatus according to claim 5, wherein the profit gained by the user equipment in offloading a single task to the edge server for processing is determined by the following formula:
wherein the content of the first and second substances,
representing the benefit obtained by the user device i offloading the task r to the edge server for processing, ci,jA bid representing the offloading of a user device i to an edge server j for processing for a single task,
representing the price to be paid by the user equipment i in the transaction price on the assumption that the task r to be unloaded of the user equipment i is successfully matched with the edge server j, tau represents the cost of unit distance, ei,jRepresenting the distance between the user device i and the edge server j.
7. The edge computing resource allocation apparatus of claim 5, wherein the matching module is configured to:
arranging the bids of the user equipment to different edge servers according to the bid information in a descending order to obtain a bid set;
according to the task information to be unloaded, the processable task information and the asking price information, starting from the first bid in the bid set, sequentially judging 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 unloaded of the target user equipment corresponding to the target bid;
if the target bid price is larger than the ask price of the target edge server, determining the combination of the target user equipment and the target edge server as a candidate matching combination, determining the intersection task as a matching task corresponding to the candidate matching combination, and determining the highest competitive bid price of other user equipment on the target edge server for the intersection task according to other bid prices after the target bid price in the bid price 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 user equipment and edge server combination which are successfully matched, determining the intersection task as a matching task corresponding to the pair of user equipment and edge server combination which are successfully matched, and determining the highest competitive bid as a transaction price corresponding to the pair of user equipment and edge server combination which are successfully matched; wherein the maximum distance cost is determined according to a maximum distance value in the distance information and a cost per distance.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the edge computing resource allocation method according to any of claims 1 to 4.
9. 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 any one of claims 1 to 4.
10. A computer program product comprising a computer program, wherein the computer program when executed by a processor implements the steps of the edge computing resource allocation method according to any one of claims 1 to 4.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115022189A (en) * | 2022-05-31 | 2022-09-06 | 武汉大学 | Edge user distribution model construction method, device, equipment and readable storage medium |
| CN115328650A (en) * | 2022-08-11 | 2022-11-11 | 杭州电子科技大学 | Edge node distribution method based on intelligent contract maximization system profit |
| 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 |
| CN116886706A (en) * | 2023-09-07 | 2023-10-13 | 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 |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115022189A (en) * | 2022-05-31 | 2022-09-06 | 武汉大学 | Edge user distribution model construction method, device, equipment and readable storage medium |
| CN115022189B (en) * | 2022-05-31 | 2024-03-26 | 武汉大学 | Edge user allocation model construction method, device, equipment and readable storage medium |
| CN115328650A (en) * | 2022-08-11 | 2022-11-11 | 杭州电子科技大学 | Edge node distribution method based on intelligent contract maximization system profit |
| 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 |
| CN116886706A (en) * | 2023-09-07 | 2023-10-13 | 典基网络科技(上海)有限公司 | Application program placement method and device, electronic equipment and storage medium |
| 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 |
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