CN112749010A - Edge calculation task allocation method for fusion recommendation system - Google Patents

Edge calculation task allocation method for fusion recommendation system Download PDF

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CN112749010A
CN112749010A CN202011617690.7A CN202011617690A CN112749010A CN 112749010 A CN112749010 A CN 112749010A CN 202011617690 A CN202011617690 A CN 202011617690A CN 112749010 A CN112749010 A CN 112749010A
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CN112749010B (en
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王磊磊
邓晓衡
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Central South University
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Abstract

The invention provides an edge computing task allocation method for a fusion recommendation system, which comprises the following steps: step 1, a cloud module, an edge server module and a mobile terminal are mutually connected to construct a cloud-edge-terminal fusion recommendation system; step 2, a task sender sends a task request to a cloud-edge-end fusion recommendation system; and 3, the cloud-edge-end fusion recommendation system receives a task request sent by a task sender, and the edge server module builds an edge server database according to the position information and the computing capacity of a plurality of edge servers which are deployed on the road side facilities and start to execute tasks according to the requirements. The invention combines the cached edge server with the recommended edge server, has high recommended hit rate, proves that the problem of the recommended hit rate is a monotonic sub-model function and NP-hard problem, improves the utilization rate of computer resources and reduces time consumption.

Description

Edge calculation task allocation method for fusion recommendation system
Technical Field
The invention relates to the technical field of computer task allocation, in particular to an edge computing task allocation method fusing a recommendation system.
Background
According to Cisco's report, mobile data traffic will grow 7-fold in the next 5 years, reaching 49 octets (EBs) per month by 2021 (1EB-106TB), while the global number of IoT devices will grow from the current 80 to 120 billion. This will make it difficult for its access network to acquire the computational resources of the mobile edge server.
In addition, in the 5G network era, large-scale task processing needs, such as multimedia special effect tasks, big data processing tasks, and the like, are beginning to be presented. Under these task scenarios, the most common user requirement is a real-time requirement, that is, the task is required to be quickly responded and executed, and the execution result can be quickly returned to the user. In order to deal with the rapid development of the mobile internet and the internet of things, 5G needs to meet the new business requirements of ultra-low time delay, ultra-low power consumption, ultra-high reliability and ultra-high density connection. Therefore, minimizing task completion time is the most common goal in task offloading problems.
The offloading problem in current mobile edge computation mainly includes: and page unloading, namely edge caching, wherein the page provider caches the commonly used pages on the edge cloud so as to reduce the time delay and energy consumption when the user requests the pages. Caching strategies considering page distribution and user mobility have been considered in related research. Task offloading, the problem is to decide when, where, how many tasks should be offloaded from the mobile device to be executed on the edge to reduce computation latency and save energy. The research mainly focuses on considering the unloading decision problem in a multi-user environment and a multi-server environment. The page unloading mainly concerns the storage capacity of the edge cloud, and the computing capacity is considered asynchronously. In the related research of task offloading, it is assumed that the edge cloud has enough software and hardware resources to support task computing as a normal assumption, which is contrary to the limitation of the edge cloud resources and the inability to support all types of tasks.
Many scholars have done relevant research to the task off-loading problem of MECs. An efficient offloading scheme is proposed by minimizing the total network energy consumption under consideration of the fronthaul and backhaul link capacity constraints and the maximum latency constraints of the users. Under the condition of balancing energy consumption and time delay, an energy-aware calculation unloading scheme is provided, and residual energy of a battery of the intelligent device is introduced into the definition of weighting factors of energy consumption and delay, so that the total consumption of the system is effectively reduced. In view of the trade-off between latency and reliability of task offloading, splitting the task of the user equipment into subtasks and offloading to nearby edge nodes in turn has been investigated. The above documents do not provide a reasonable allocation of limited radio and computational resources. Under the multi-user MEC system, an online task unloading algorithm is provided with the aim of minimizing the average energy consumption of users and MEC servers. Considering the minimization of the total energy consumption of the system, the joint optimization problem of the offloading decision, the allocation of radio resources and computing resources is studied. However, the existing task scheduling algorithms only unload and allocate tasks by optimizing energy consumption and delay, and never select an edge server to process tasks from the tasks themselves.
Disclosure of Invention
The invention provides an edge computing task allocation method for a fusion recommendation system, and aims to solve the problem that an edge server is not selected to process tasks from the tasks of the traditional task allocation method.
In order to achieve the above object, an embodiment of the present invention provides an edge computing task allocation method for a fusion recommendation system, including:
step 1, a cloud module, an edge server module and a mobile terminal are mutually connected to construct a cloud-edge-terminal fusion recommendation system;
step 2, a task sender sends a task request to a cloud-edge-end fusion recommendation system;
step 3, the cloud-edge-end fusion recommendation system receives a task request sent by a task sender, and an edge server module builds an edge server database according to the position information and the computing capacity of a plurality of edge servers which are deployed on road side facilities and start to execute tasks according to requirements;
step 4, screening edge server information capable of processing the current task from an edge server database by the cloud-edge-end fusion recommendation system;
step 5, filtering the screened edge server information through a filtering algorithm according to the position information of the current task sender;
step 6, clustering the filtered edge server information according to the computing capability of the edge server to obtain the position information and the computing capability information of the edge server capable of executing the task request and starting the edge server capable of executing the task request;
step 7, recommending the edge server table to the cloud module by the edge server module according to the edge server table established in the edge server module according to the position information and the computing capacity information of the edge server capable of completing task execution;
step 8, the cloud-edge-end fusion recommendation system recommends the edge server module to the edge server table of the cloud end module and combines the edge server which is cached in the cloud end module but does not participate in the task execution of the previous stage;
step 9, screening the edge servers which are recommended to the cloud end module for the edge server module and cached in the cloud end module by the cloud-edge-end fusion recommendation system, and constructing a final recommendation edge server information table according to the screened edge servers;
step 10, the cloud-edge-end fusion recommendation system analyzes task characteristics of the task request, selects an edge server capable of executing the current task request in the final recommendation edge server information table, and recommends the edge server to a task sender, and when the task sender receives the edge server recommended by the cloud-edge-end fusion recommendation system, the edge server executes the current task request of the task sender;
and 11, optimizing the recommendation hit rate of the cloud-edge-end fusion recommendation system to the task sender by designing a recommendation optimization algorithm to obtain the optimal recommendation hit rate.
Wherein, the step 3 specifically comprises:
step 31, extracting and summarizing the deployment position of each edge server, and screening the deployment position information of the edge server;
step 32, summarizing an edge server deployment area, and determining an edge server deployment position;
step 33, extracting the position of the task sender, and screening edge server deployment positions meeting the requirement in the communication range of the task sender to obtain an edge server deployment position set in the communication range of the task sender;
step 34, analyzing the computing power of each edge server according to the edge server deployment position set;
step 35, sequencing and summarizing the position information of each edge server in the communication range of the task sender, determining the deployment position of each edge server, and establishing a deployment position database of the edge servers;
and step 36, summarizing the computing power of each edge server in the communication range of the task sender, and establishing an edge server database participating in task processing.
Wherein, the step 5 specifically comprises:
and step 51, filtering the edge server information within a certain communication range of the current task sender through a filtering algorithm according to the position information of the current task sender transmitted by the edge server module.
Wherein, the step 6 specifically comprises:
step 61, separating the edge server information needing to be recommended from the edge server information after secondary filtering;
and step 62, clustering the edge servers with sufficient computing power when the separated edge servers have excessive information, obtaining the position information of the edge servers with sufficient computing power, and starting.
Wherein, the step 7 specifically comprises:
step 71, establishing an edge server table according to the obtained position information of the edge server with sufficient computing capacity;
step 72, the edge server module recommends the edge server table to the cloud module.
Wherein, the step 8 specifically comprises:
step 81, recording an edge server table of an edge server module recommendation cloud end module by the cloud-edge-end fusion recommendation system;
and step 82, combining the edge server recommended by the edge server module with the edge server cached by the cloud end module.
Wherein, the step 9 and the step 10 specifically include:
the method comprises the steps that edge servers which are cached in a cloud module and recommended for an edge server module are screened out to construct a final recommended edge server information table, a cloud-edge-end fusion recommendation system adds edge servers which can process current task requests in the final recommended edge server information table to the tail of the edge server table by means of breadth-first search, and the cloud-edge-end fusion recommendation system recommends m edge servers which can complete current task requests to a task sender.
Wherein, the step 11 specifically comprises:
because the task sender is in a mobile state, the edge server is in a flexible state, tasks needing to be processed between the task sender and the edge server change along with time, and the edge server selected as the edge server for processing the task request is in the communication range of the task sender under the current network state:
qij＝prob{ED j in the range of TS i}∈{0,1} (1)
wherein q isijIndicates the communication range of the task sender, i indicates the task sender, j indicates the edge server, and TS indicates the task sender.
Wherein the step 11 further comprises:
when the task request recommendation of the task sender is hit, the recommendation hit rate of the task request is expressed as a function of an integer variable, as follows:
Figure BDA0002875305850000051
where n represents a task request, n is equal to K,
Figure BDA0002875305850000052
the probability that the task sender sends the task request n is satisfied when the task k is unreasonable and the task sender i sends the task request n
Figure BDA0002875305850000053
M denotes the number of edge servers, K denotes the number of tasks, xnjIndicating the number of edge servers used to process task n within the communication range of the task sender.
Wherein the step 11 further comprises:
the problem of optimizing the recommendation strategy is represented as:
Figure BDA0002875305850000054
where N denotes the task sender, pikRepresenting the probability, x, that a task sender i requests to process a task knjIndicating the number of edge servers used to process task n within the task sender's communication range, and C indicating the maximum number of edge servers needed to process the task.
The scheme of the invention has the following beneficial effects:
according to the edge computing task allocation method of the fusion recommendation system, the cloud-edge-end fusion recommendation system is adopted to perform task characteristic analysis on a task request of a task sender and select a proper edge server, so that a task is allocated to the proper edge server to be executed; the cached edge server is combined with the recommended edge server, the recommendation hit rate is high, the recommendation hit rate problem is proved to be an NP-hard problem of a monotonic sub-model function, the utilization rate of computer resources is improved, and time consumption is reduced.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic structural view of the present invention;
FIG. 3 is a schematic diagram of a preferred process of the present invention;
FIG. 4 is a schematic diagram of the recommendation rate of the present invention under different BFS parameters;
FIG. 5 is a schematic diagram of the recommendation rates of the present invention in different optimized recommendation modes;
FIG. 6 is a schematic time consumption diagram of the present invention in different vehicles;
FIG. 7 is a diagram illustrating the time consumption of the present invention at different task sizes.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
The invention provides an edge computing task allocation method fusing a recommendation system, aiming at the problem that the existing task allocation method does not select an edge server to process tasks from the task itself.
As shown in fig. 1 to 7, an embodiment of the present invention provides an edge computing task allocation method for a fusion recommendation system, including: step 1, a cloud module, an edge server module and a mobile terminal are mutually connected to construct a cloud-edge-terminal fusion recommendation system; step 2, a task sender sends a task request to a cloud-edge-end fusion recommendation system; step 3, the cloud-edge-end fusion recommendation system receives a task request sent by a task sender, and an edge server module builds an edge server database according to the position information and the computing capacity of a plurality of edge servers which are deployed on road side facilities and start to execute tasks according to requirements; step 4, screening edge server information capable of processing the current task from an edge server database by the cloud-edge-end fusion recommendation system; step 5, filtering the screened edge server information through a filtering algorithm according to the position information of the current task sender; step 6, clustering the filtered edge server information according to the computing capability of the edge server to obtain the position information and the computing capability information of the edge server capable of executing the task request and starting the edge server capable of executing the task request; step 7, recommending the edge server table to the cloud module by the edge server module according to the edge server table established in the edge server module according to the position information and the computing capacity information of the edge server capable of completing task execution; step 8, the cloud-edge-end fusion recommendation system recommends the edge server module to the edge server table of the cloud end module and combines the edge server which is cached in the cloud end module but does not participate in the task execution of the previous stage; step 9, screening the edge servers which are recommended to the cloud end module for the edge server module and cached in the cloud end module by the cloud-edge-end fusion recommendation system, and constructing a final recommendation edge server information table according to the screened edge servers; step 10, the cloud-edge-end fusion recommendation system analyzes task characteristics of the task request, selects an edge server capable of executing the current task request in the final recommendation edge server information table, and recommends the edge server to a task sender, and when the task sender receives the edge server recommended by the cloud-edge-end fusion recommendation system, the edge server executes the current task request of the task sender; and 11, optimizing the recommendation hit rate of the cloud-edge-end fusion recommendation system to the task sender by designing a recommendation optimization algorithm to obtain the optimal recommendation hit rate.
In the edge calculation task allocation method of the fusion recommendation system according to the above embodiment of the present invention, assuming that the edge server information records are regarded as a series of data points, each edge server ID-calculation capability-location data point is: s ═ d (computing power, position); all edge server information can be written as: q ═ S1,S2,…,Sn) (ii) a Since in practice, edge server deployment locations are fixed and only started when needed, it is necessary to roughly define edge servers within a certain communication range based on the location of the task sender and filter out edge server location information. The method for acquiring the edge server position information in the specified range comprises the following steps: extracting the deployment position of the edge server, summarizing the deployment area of the edge server and analyzing the computing capacity of the edge server. 1. Extracting the deployment positions of the edge servers comprises extracting and summarizing the deployment positions of the edge servers and screening the deployment position information of the edge servers. 2. And summarizing the edge server deployment area comprises determining, screening and merging edge server deployment positions and summarizing the edge server deployment area. 3. Analyzing edge server computing power includes sorting edge server deployment areas, screening edge server deployment areas, analyzing edge server computing power. Each deployment location indicates that the edge server has a behavior of some particular significance at a certain location. Through extraction and induction of deployment positions, an edge server deployment position set in a communication range of a task sender is obtained, the computing capacity condition of each edge server is further analyzed, and finally, the edge servers are deployed in a pairAnd sequencing and summarizing the edge servers, determining the positions of the edge servers, establishing an edge server position database, summarizing the computing capacity of the edge servers in the fixed position area, and establishing the edge server database.
Wherein, the step 3 specifically comprises: step 31, extracting and summarizing the deployment position of each edge server, and screening the deployment position information of the edge server; step 32, summarizing an edge server deployment area, and determining an edge server deployment position; step 33, extracting the position of the task sender, and screening edge server deployment positions meeting the requirement in the communication range of the task sender to obtain an edge server deployment position set in the communication range of the task sender; step 34, analyzing the computing power of each edge server according to the edge server deployment position set; step 35, sequencing and summarizing the position information of each edge server in the communication range of the task sender, determining the deployment position of each edge server, and establishing a deployment position database of the edge servers; and step 36, summarizing the computing power of each edge server in the communication range of the task sender, and establishing an edge server database participating in task processing.
Wherein, the step 5 specifically comprises: and step 51, filtering the edge server information within a certain communication range of the current task sender through a filtering algorithm according to the position information of the current task sender transmitted by the edge server module.
Wherein, the step 6 specifically comprises: step 61, separating the edge server information needing to be recommended from the edge server information after secondary filtering; and step 62, clustering the edge servers with sufficient computing power when the separated edge servers have excessive information, obtaining the position information of the edge servers with sufficient computing power, and starting.
Wherein, the step 7 specifically comprises: step 71, establishing an edge server table according to the obtained position information of the edge server with sufficient computing capacity; step 72, the edge server module recommends the edge server table to the cloud module.
Wherein, the step 8 specifically comprises: step 81, recording an edge server table of an edge server module recommendation cloud end module by the cloud-edge-end fusion recommendation system; and step 82, combining the edge server recommended by the edge server module with the edge server cached by the cloud end module.
Wherein, the step 9 and the step 10 specifically include: the method comprises the steps that edge servers which are cached in a cloud module and recommended for an edge server module are screened out to construct a final recommended edge server information table, a cloud-edge-end fusion recommendation system adds edge servers which can process current task requests in the final recommended edge server information table to the tail of the edge server table by means of breadth-first search, and the cloud-edge-end fusion recommendation system recommends m edge servers which can complete current task requests to a task sender.
In the edge computing task allocation method of the fusion recommendation system according to the embodiment of the present invention, the edge server information table includes recommended edge server information and edge server information cached by the cloud, and the cloud-edge-end fusion recommendation system selects a suitable edge server from the edge server information table based on a content relationship of the task request and recommends the selected edge server to the task sender.
Wherein, the step 11 specifically comprises: because the task sender is in a mobile state, the edge server is in a flexible state, tasks needing to be processed between the task sender and the edge server change along with time, and the edge server selected as the edge server for processing the task request is in the communication range of the task sender under the current network state:
qij＝prob{ED j in the range of TS i}∈{0,1} (1)
wherein q isijIndicates the communication range of the task sender, i indicates the task sender, j indicates the edge server, and TS indicates the task sender.
Wherein the step 11 further comprises: when the task request recommendation of the task sender is hit, the recommendation hit rate of the task request is expressed as a function of an integer variable, as follows:
Figure BDA0002875305850000091
where n represents a task request, n is equal to K,
Figure BDA0002875305850000092
the probability that the task sender sends the task request n is satisfied when the task k is unreasonable and the task sender i sends the task request n
Figure BDA0002875305850000093
M denotes the number of edge servers, K denotes the number of tasks, xnjIndicating the number of edge servers used to process task n within the communication range of the task sender.
Wherein the step 11 further comprises: the problem of optimizing the recommendation strategy is represented as:
Figure BDA0002875305850000094
where N denotes the task sender, pikRepresenting the probability, x, that a task sender i requests to process a task knjIndicating the number of edge servers used to process task n within the task sender's communication range, and C indicating the maximum number of edge servers needed to process the task.
In the edge computing task allocation method for the fusion recommendation system according to the embodiment of the present invention, a task sender sends a task request to the cloud-edge-end fusion recommendation system, and searches for edge server information that can complete a task in the following BFS manner, first, the edge server information related to the task content is requested from the cloud-edge-end fusion recommendation system, and the requested edge server information is added to the list M in the order returned from the cloud-edge-end fusion recommendation system. Edge server information that is related to the edge server information in the list M and that is capable of performing task processing is further recommended for the edge server information in the list M, and they are added to the end of the list M. And adding other related edge server information by analogy until the retrieval depth is reached, searching the edge server information simultaneously contained in the cloud cache in the list M through the TORS algorithm, and adding the edge server information into the output list G until all the edge server information in the list M is browsed and the output list G contains N pieces of edge server information, so as to be on first (line 5-10). After the above steps are completed, if the number of edge servers in the output list G is less than N, then N- | G | pieces of edge server information are added to the output list G (line 11-16).
The edge calculation task allocation method for the fusion recommendation system according to the above embodiment of the present invention proves the following for the optimization problem of formula (3):
Figure BDA0002875305850000101
the objective function of the optimization problem is the NP-hard problem, using
Figure BDA0002875305850000102
The set of edge servers associated with the current processing task k is represented as follows:
Figure BDA0002875305850000103
wherein t represents an edge server different from the edge server j for processing the current task k, and M represents the number of edge servers;
the edge server subset is represented as
Figure BDA0002875305850000104
Assume that only edge server j is recommended to process task (x)j＝1and
Figure BDA0002875305850000105
) So the recommendation rate will be equal to
Figure BDA0002875305850000106
(
Figure BDA0002875305850000107
Is the probability that the edge server t processes task i). When more than one edge server j is recommended to process the task, S' represents all the edge servers covered by the current task and the union set
Figure BDA0002875305850000108
So the recommendation rate is equal to
Figure BDA0002875305850000109
The objective function thus corresponds to:
Figure BDA00028753058500001010
wherein,
Figure BDA00028753058500001011
representing the probability of an edge server t processing task i, qitIndicating that the edge server t is in the sender communication range of task i.
The above corresponds to the maximum coverage problem of the weighted elements, where the elements correspond to edge servers, the weights correspond to probability values, and the number of subsets selected must be less than the maximum number of edge servers needed to process the task, and the union of the covered elements is S'. This is a known NP-hard problem. Thus, there are many edge server problems recommended within the coverage of each task, an NP-hard problem.
The objective function is a monotonic sub-model function and is constrained by the cardinality: objective function
Figure BDA00028753058500001016
Equivalent to a set function
Figure BDA00028753058500001017
Where K × M is the edge server recommendation set (K, j), K represents the task, j represents the edge server, S { K ∈ K, i ∈ M: xkj1} as follows:
Figure BDA00028753058500001012
wherein p isikRepresenting the probability that task sender i requests to process task k,
Figure BDA00028753058500001013
the probability that the task sender sends the task request n is satisfied when the task k is unreasonable and the task sender i sends the task request n
Figure BDA00028753058500001014
qijIndicating the communication range of the task sender, i indicating the task sender, and j indicating the edge server.
An ensemble function is characterized as a set of submodules and only if for each
Figure BDA00028753058500001015
And ε ∈ V \ B is considered:
[f(A∪{ε})-f(A)]-[f(E∪{ε})-f(E)]≥0 (7)
wherein A and E represent sets, and all A and E sets are satisfied
Figure BDA0002875305850000111
ε represents the elements belonging to V that do not belong to E;
according to the formula
Figure BDA0002875305850000112
Firstly, calculating:
Figure BDA0002875305850000113
wherein l, ε represents an element not belonging to A, pikRepresenting the probability that task sender i requests to process task k,
Figure BDA0002875305850000114
represents anyWhen the task k is unreasonable, the probability that the task sender sends the task request n meets the requirement
Figure BDA0002875305850000115
qijAnd q isimIndicating the communication range of the task sender, i indicating the task sender, j, m indicating the edge server.
Figure BDA0002875305850000116
The value of equation (9) is always greater than 0, i.e. sub-modularity is demonstrated;
Figure BDA0002875305850000117
since equation (10) is always non-negative, monotonicity is demonstrated;
to prove that the constraint is a pseudo-matrix, consider that the set v-K × M (i.e., all possible tuples { task, edge server }) and the set of subsets thereof do not violate the maximum number of edge servers needed to process the task, as follows:
Figure BDA0002875305850000118
where S denotes a subset of edge servers, 2νRepresents the power set of the set v, C represents the maximum number of edge servers needed to process the task, and M represents the number of edge servers.
First, for all sets a and E,
Figure BDA0002875305850000121
it is considered that if
Figure BDA0002875305850000122
Set E recommends a number of edge servers that does not exceed the maximum number of edge servers required for task processing, and then, forIn that
Figure BDA0002875305850000123
Because each edge server in set a must be the same as or less than the edge servers in set E, the number of edge servers problem is not violated. Secondly, for all sets A, E ∈ Γ, edge servers that meet the recommendation condition, | E ∈ Γ>If the number of edge servers in set E is not equal to the maximum, the set E will violate the edge server number constraint, i.e., the set E will not be able to determine the number of edge servers in set E
Figure BDA0002875305850000124
This means that set a can continue to recommend at least one more edge server and can select that edge server from set E;
for any monotonic sub-model function f, consider that:
F(x)≥(1-1/e)f*(x) (12)
wherein f is*(x) And expressing the optimal value of the monotone sub-model function.
To maximize the monotonic sub-model function constrained by the pseudo-matrix, Khuller S proposes a random algorithm that gives a (1-1/e) approximation, which is divided into two parts. In the first part, the combinatorial problem is replaced with a sequential problem and an approximate solution to the sequential problem is found. In the second section, fractional solutions to successive problems are rounded using a technique called Pipage rounding. Although this algorithm provides better performance guarantees, when the edge server recommendation set size in question is equal to K × M, it is computationally too large to implement. Of course, minimizing the algorithm complexity or best approximation algorithms are already beyond the range of processing required. A method for efficiently processing a large task in a cloud edge collaborative scene through a quick and effective Greedy algorithm.
In the edge calculation task allocation method of the fusion recommendation system according to the embodiment of the present invention, fig. 4 shows that through two situations of direct recommendation and recommendation + caching, as the parameters B and D increase, the recommendation rate of recommendation + caching is higher than that of direct recommendation. The recommendation rate of the TORS algorithm, Greedy algorithm and Top recommendation algorithm performance under different BFS parameters are shown in fig. 5. For the same case, Greedy is always better than TORS, the recommendation rate performance of Top recommendation algorithm is 2 times higher than Greedy, and fig. 5 clearly shows the benefit of combining recommendation and caching.
The edge computing task allocation method of the fusion recommendation system according to the above embodiment of the present invention, and the total time cost of the system when the number of edge nodes increases is shown in fig. 6 and fig. 7. In general, the total cost of the three methods decreases as the number of edge nodes increases. In fig. 6, the Top recommendation algorithm is most preferable, the Greedy algorithm has a small difference, and both the TORS algorithm and the Greedy algorithm are stable. Meanwhile, the Top recommendation algorithm is higher than the Greedy algorithm Greedy optimal recommendation curve, and is Greedy for the unilateral nodes. But as more edge nodes are available, the faster they fall. This is because the execution time decreases as the number of edge nodes increases. Fig. 7 shows the impact of the offload task data size on the total cost of the system, where the number of edge nodes is set to 4, and as the offload task data size increases, the total cost of the three methods also increases. This is because the larger the amount of data, the greater the time and energy consumption of the offload. Compared with other methods, the TORS algorithm has a slow growth trend and a better effect. With the increase of the data volume, the increase speed of the Top recommendation algorithm curve is far faster than that of the TORS algorithm and Greedy algorithm, which indicates that the larger the data volume required for unloading, the larger the delay and energy consumption of the unloading calculation.
In the edge computing task allocation method of the fusion recommendation system according to the embodiment of the invention, the cloud-edge-end fusion recommendation system is adopted to perform task characteristic analysis on the task request of the task sender and select a proper edge server, so that the task is allocated to the proper edge server for execution. The cloud-edge-end fusion recommendation system mainly comprises three modules: the system comprises a cloud module, an edge server module and a mobile terminal (task sender). The edge server module mainly recommends an edge server in a flexible state (non-cache), and recommends an edge server meeting the condition to the cloud module according to whether the edge server is in a communication range with a task sender and the computing capacity is enough. And the cloud module combines the edge server information acquired from the edge server module with the edge server cached by the cloud and recommends the combination to the task sender. The cloud-edge-end fusion recommendation system is designed by combining the cached edge server and the recommended edge server, has higher recommendation hit rate, proves that the recommendation hit rate problem is a monotone sub-model function and NP-hard problem, improves the utilization rate of computer resources of the whole system, and reduces time consumption.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (10)

1. An edge computing task allocation method for a fusion recommendation system is characterized by comprising the following steps:
step 1, a cloud module, an edge server module and a mobile terminal are mutually connected to construct a cloud-edge-terminal fusion recommendation system;
step 2, a task sender sends a task request to a cloud-edge-end fusion recommendation system;
step 3, the cloud-edge-end fusion recommendation system receives a task request sent by a task sender, and an edge server module builds an edge server database according to the position information and the computing capacity of a plurality of edge servers which are deployed on road side facilities and start to execute tasks according to requirements;
step 4, screening edge server information capable of processing the current task from an edge server database by the cloud-edge-end fusion recommendation system;
step 5, filtering the screened edge server information through a filtering algorithm according to the position information of the current task sender;
step 6, clustering the filtered edge server information according to the computing capability of the edge server to obtain the position information and the computing capability information of the edge server capable of executing the task request and starting the edge server capable of executing the task request;
step 7, recommending the edge server table to the cloud module by the edge server module according to the edge server table established in the edge server module according to the position information and the computing capacity information of the edge server capable of completing task execution;
step 8, the cloud-edge-end fusion recommendation system recommends the edge server module to the edge server table of the cloud end module and combines the edge server which is cached in the cloud end module but does not participate in the task execution of the previous stage;
step 9, screening the edge servers which are recommended to the cloud end module for the edge server module and cached in the cloud end module by the cloud-edge-end fusion recommendation system, and constructing a final recommendation edge server information table according to the screened edge servers;
step 10, the cloud-edge-end fusion recommendation system analyzes task characteristics of the task request, selects an edge server capable of executing the current task request in the final recommendation edge server information table, and recommends the edge server to a task sender, and when the task sender receives the edge server recommended by the cloud-edge-end fusion recommendation system, the edge server executes the current task request of the task sender;
and 11, optimizing the recommendation hit rate of the cloud-edge-end fusion recommendation system to the task sender by designing a recommendation optimization algorithm to obtain the optimal recommendation hit rate.
2. The method for distributing the edge calculation tasks of the fusion recommendation system according to claim 1, wherein the step 3 specifically comprises:
step 31, extracting and summarizing the deployment position of each edge server, and screening the deployment position information of the edge server;
step 32, summarizing an edge server deployment area, and determining an edge server deployment position;
step 33, extracting the position of the task sender, and screening edge server deployment positions meeting the requirement in the communication range of the task sender to obtain an edge server deployment position set in the communication range of the task sender;
step 34, analyzing the computing power of each edge server according to the edge server deployment position set;
step 35, sequencing and summarizing the position information of each edge server in the communication range of the task sender, determining the deployment position of each edge server, and establishing a deployment position database of the edge servers;
and step 36, summarizing the computing power of each edge server in the communication range of the task sender, and establishing an edge server database participating in task processing.
3. The method for distributing the edge calculation tasks of the fusion recommendation system according to claim 1, wherein the step 5 specifically comprises:
and step 51, filtering the edge server information within a certain communication range of the current task sender through a filtering algorithm according to the position information of the current task sender transmitted by the edge server module.
4. The method for distributing the edge calculation tasks of the fusion recommendation system according to claim 1, wherein the step 6 specifically comprises:
step 61, separating the edge server information needing to be recommended from the edge server information after secondary filtering;
and step 62, clustering the edge servers with sufficient computing power when the separated edge servers have excessive information, obtaining the position information of the edge servers with sufficient computing power, and starting.
5. The method for distributing the edge calculation tasks of the fusion recommendation system according to claim 1, wherein the step 7 specifically comprises:
step 71, establishing an edge server table according to the obtained position information of the edge server with sufficient computing capacity;
step 72, the edge server module recommends the edge server table to the cloud module.
6. The method for distributing the edge calculation tasks of the fusion recommendation system according to claim 1, wherein the step 8 specifically comprises:
step 81, recording an edge server table of an edge server module recommendation cloud end module by the cloud-edge-end fusion recommendation system;
and step 82, combining the edge server recommended by the edge server module with the edge server cached by the cloud end module.
7. The method for distributing the edge calculation tasks of the fusion recommendation system according to claim 1, wherein the steps 9 and 10 specifically include:
the method comprises the steps that edge servers which are cached in a cloud module and recommended for an edge server module are screened out to construct a final recommended edge server information table, a cloud-edge-end fusion recommendation system adds edge servers which can process current task requests in the final recommended edge server information table to the tail of the edge server table by means of breadth-first search, and the cloud-edge-end fusion recommendation system recommends m edge servers which can complete current task requests to a task sender.
8. The method for distributing the edge calculation tasks of the fusion recommendation system according to claim 1, wherein the step 11 specifically comprises:
because the task sender is in a mobile state, the edge server is in a flexible state, tasks needing to be processed between the task sender and the edge server change along with time, and the edge server selected as the edge server for processing the task request is in the communication range of the task sender under the current network state:
qij＝prob{ED j in the range of TSi}∈{0,1} (1)
wherein q isijIndicating the communication range of the task sender, i indicating the task sender, j indicating the edgeEdge server, TS, represents task sender.
9. The method for distributing the edge computing task of the fusion recommendation system according to claim 8, wherein the step 11 further comprises:
when the task request recommendation of the task sender is hit, the recommendation hit rate of the task request is expressed as a function of an integer variable, as follows:
Figure FDA0002875305840000031
where n represents a task request, n is equal to K,
Figure FDA0002875305840000032
the probability that the task sender sends the task request n is satisfied when the task k is unreasonable and the task sender i sends the task request n
Figure FDA0002875305840000033
M denotes the number of edge servers, K denotes the number of tasks, xnjIndicating the number of edge servers used to process task n within the communication range of the task sender.
10. The method for distributing the edge computing task of the fusion recommendation system according to claim 9, wherein the step 11 further comprises:
the problem of optimizing the recommendation strategy is represented as:
Figure FDA0002875305840000041
where N denotes the task sender, pikRepresenting the probability, x, that a task sender i requests to process a task knjIndicating the number of edge servers used to process task n within the communication range of the task sender, C indicating the maximum number of edge servers required to process the taskTo achieve the purpose.
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