CN111045827A - Time-validity task scheduling method based on resource sharing in cloud and fog environment - Google Patents

Time-validity task scheduling method based on resource sharing in cloud and fog environment Download PDF

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CN111045827A
CN111045827A CN201911303114.2A CN201911303114A CN111045827A CN 111045827 A CN111045827 A CN 111045827A CN 201911303114 A CN201911303114 A CN 201911303114A CN 111045827 A CN111045827 A CN 111045827A
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范贵生
虞慧群
孙怀英
杨康
刘冬梅
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East China University of Science and Technology
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Abstract

The invention relates to a time effectiveness task scheduling method based on resource sharing in a cloud and fog environment, which establishes a resource sharing mechanism based on a contract through sealed bidding double-sided auction under a specific time slice, and allocates resources according to the resource sharing mechanism, and specifically comprises the following steps: step S1: the fog cluster participates in resource auction at a rented resource price and a rented resource price; step S2: determining an optimal bidding strategy according to the resources of other fog clusters and the renting-in and renting-out prices; step S3: participating in auction according to the optimal bidding strategy, and establishing a contract with the corresponding fog cluster according to an auction result to form a resource sharing mechanism; step S4: selecting a fog key node under a resource sharing mechanism; step S5: and the fog key nodes finish the division of fog functional domains by a spectral clustering method, and carry out task scheduling among different fog functional domains. Compared with the prior art, the method has the advantages of fully utilizing fog layer resources, reducing cloud work load, reducing task response time and the like.

Description

Time-validity task scheduling method based on resource sharing in cloud and fog environment
Technical Field
The invention relates to the field of computer edge computing, in particular to a time effectiveness task scheduling method based on resource sharing in a cloud and mist environment.
Background
Edge computing (fog computing) is generated under the conditions of the rapid development and wide application of the interconnection of everything, the continuous expansion of data scale and the continuous increase of the computing requirement of data processing. The edge calculation makes everything more intelligent and supports the construction of a robust edge application ecology. The edge computing is a supplement and extension to cloud computing, and provides a better computing platform for mobile computing, the Internet of things and the like. The edge computing needs the strong computing capability and the support of mass storage of a cloud computing center, and the cloud computing also needs the processing of mass data and private data by edge equipment in the edge computing, so that the requirements of real-time performance, privacy protection and energy consumption reduction are met.
Based on the characteristics that cloud computing is farther away from a user and fog/edge computing is closer to the user, many existing technologies relate to how to migrate application requests of an internet layer onto a fog or cloud to be executed, so as to minimize the time of an application task or minimize the energy consumption of the application request during execution or consider optimization of the two indexes at the same time. Some techniques relate to the influence of the mobility (switching of geographical locations) of the user on the completion of the application request, for example, the user requests to download a video resource, and if the user is moving continuously, the performance of the video download will be greatly influenced. However, the existing technology is directed to scheduling of fog layer resources in a certain area/range, and does not consider the sharing of fog layer resources in different areas (fog layer resources in a cell or city can be referred to as a fog cluster). While fog calculations may ensure faster task response times with the advantage of closer physical distance and network distance, their resources are relatively limited. Therefore, how to fully utilize the resources of the fog layer and simultaneously reduce the response time of the task is a key problem.
Disclosure of Invention
The invention aims to overcome the defects of insufficient utilization of fog layer resources and long task response time in the prior art and provide a time effectiveness task scheduling method based on resource sharing in a cloud and fog environment.
The purpose of the invention can be realized by the following technical scheme:
a time-validity task scheduling method based on resource sharing in a cloud and fog environment is characterized in that a resource sharing mechanism based on a contract between fog clusters is established in a sealed bidding double-sided auction mode under a specific time slice, and resource allocation constructed based on a functional domain is carried out according to the resource sharing mechanism, and the method specifically comprises the following steps:
step S1: the fog cluster participates in the fog cluster resource auction at a rented resource price and a rented resource price according to local resources of the fog cluster;
step S2: the fog cluster determines an optimal bidding strategy according to the resources of other fog clusters and the prices of the rented resources and the rented resources;
step S3: the fog cluster participates in auction according to the optimal bidding strategy, and contracts are established with other corresponding fog clusters according to auction results to form the resource sharing mechanism;
step S4: selecting a fog key node under the resource sharing mechanism formed in the step S3;
step S5: and dynamically and periodically clustering the rest fog key nodes by the fog key nodes through a spectral clustering method to complete the division of the fog functional domains, and scheduling tasks among different fog functional domains.
Revenue obtained by a fog cluster executing a task on a resource leased in the contract when local resources are insufficient
Figure BDA0002322359350000021
The method specifically comprises the following steps:
Figure BDA0002322359350000022
wherein Res is a function of the resource usage amount corresponding to the load estimation,
Figure BDA0002322359350000023
is the final renting resource price of the contract, tau is the time slice, k is the resource type, i is the fog cluster, rho i is the price of the unit resource used, lambdai(τ) is the workload of fog cluster i in time slice τ, Ci(τ) is the amount of resources in time slice τ for fog cluster i, VkThe amount of k-type resources contained in time slice τ for fog cluster i.
The fog cluster distributes partial load to resources on other fog clusters to obtain the benefit when the running cost of local resources is larger
Figure BDA0002322359350000024
The method specifically comprises the following steps:
Figure BDA0002322359350000025
wherein the content of the first and second substances,
Figure BDA0002322359350000026
represents the overhead of running and managing resources of type k in fog cluster i.
The optimal bidding strategy for renting the fog cluster specifically comprises the following steps:
Figure BDA0002322359350000027
wherein the content of the first and second substances,
Figure BDA0002322359350000028
an optimal bidding strategy is entered for the lease.
The fog cluster rents resources to other fog clusters to obtain income when local resources are remained
Figure BDA0002322359350000029
The method specifically comprises the following steps:
Figure BDA0002322359350000031
wherein the content of the first and second substances,
Figure BDA0002322359350000032
the resource price is leased for the final contract.
Leasing optimal bidding strategy for the fog cluster
Figure BDA0002322359350000033
The method specifically comprises the following steps:
Figure BDA0002322359350000034
where Null denotes an empty bid, Mi(τ) is a fog node list, DkRepresenting resources of type k.
The contract information comprises a mist cluster for renting resources, a renting price, a corresponding time slice and a mist node list covered in the contract.
The process of selecting the fog key nodes comprises the steps of calculating the middle centrality and the calculation performance of each fog node in the fog cluster and the communication delay of each fog node reaching the terminal node of the Internet of things, and selecting the fog key nodes by performing non-dominated sorting according to the calculation results.
The calculation formula of the middle centrality is specifically as follows:
Figure BDA0002322359350000035
wherein g (n) represents the median centrality of vertex n, σsdRepresenting the total number of shortest paths, σ, from vertex s to vertex dsd(n) represents the number of paths through vertex n in the shortest path from vertex s to vertex d.
The grouping criteria of the spectral clustering method are based on the available computational and memory resources of the available mist devices.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, through establishing a resource sharing mechanism based on a contract among the fog clusters, the fog functional domain division and the resource allocation are carried out under the condition of the fog cluster resource sharing, so that the effective task scheduling is realized, the fog layer resources are fully utilized, more tasks can be executed at the fog end, the pressure of a core network between a user and the cloud end is reduced, the work load of the cloud end is reduced, and meanwhile, the response time of the tasks is reduced.
2. The invention selects the fog key nodes by a centrality method, the higher the centrality value of a certain vertex is, the higher the centrality value of the certain vertex is, the more the vertex can reach other vertexes by the shortest possible delay, and the higher the centrality value of the certain vertex is, the more the vertex is on the paths of the shortest route, the efficiency and the accuracy of selecting the fog key nodes are improved.
3. The method uses a spectral clustering method when the fog functional domain is divided, finds the similarity between different data points by using the graph similarity, and is suitable for traversing the network graph in the fog environment.
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FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of a fog-end contract auction mechanism of the present invention;
FIG. 3 is a schematic diagram of resource sharing in a cloud environment according to the present invention
FIG. 4 is a schematic flow chart illustrating a resource sharing mechanism for a fog cluster according to the present invention;
FIG. 5 is a schematic flow chart of selecting a key fog node according to the present invention;
FIG. 6 is a flow chart of the mist domain partition according to the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
As shown in fig. 1, a method for scheduling a task based on time availability of resource sharing in a cloud and fog environment, which establishes a resource sharing mechanism based on a contract between fog clusters in a sealed bidding double-sided auction manner under a specific time slice, and allocates resources based on a functional domain construction according to the resource sharing mechanism, specifically includes the following steps:
step S1: the fog cluster participates in the fog cluster resource auction at a rented resource price and a rented resource price according to local resources of the fog cluster;
step S2: determining an optimal bidding strategy by the fog cluster according to the resources of other fog clusters, the prices of rented resources and the prices of rented resources;
step S3: the fog cluster participates in auction according to the optimal bidding strategy, and contracts are established with other corresponding fog clusters according to auction results to form a resource sharing mechanism;
step S4: selecting a fog key node under the resource sharing mechanism formed in the step S3;
step S5: and the fog key nodes dynamically and periodically cluster the rest fog nodes by a spectral clustering method to complete the division of fog functional domains and perform task scheduling among different fog functional domains.
As shown in fig. 2 and 3, the system model for contract-based sharing of fog cluster resources in a cloud and fog environment comprises 3 logical layers. The first logic layer is a cloud-end layer, is mainly used for carrying out global resource scheduling and intelligent decision, serves as a third-party auction dealer when a resource sharing mechanism is established, is responsible for selecting key fog nodes in each fog cluster, and can also execute part of user requests; the second logic layer is a fog end layer and comprises different fog cluster resources corresponding to different areas, each fog cluster is composed of a plurality of fog nodes which are connected with each other, the resources in the fog clusters are mainly responsible for intelligent sensing, intelligent calculation, data analysis, real-time control, process optimization and construction of different functional domains, the resources can be shared among the fog clusters so as to achieve full utilization of the resources and minimization of task response time, the fog nodes in the fog clusters can receive and process task requests sent by terminal equipment from the bottom layer, and a plurality of key fog nodes can exist in each fog cluster. The cloud layer and the fog layer need data cooperation, service management cooperation, intelligent cooperation and application management cooperation so as to achieve performance optimization; the bottom layer is a terminal device layer, and the terminal device on the layer can send a task processing request to the upper fog layer or the cloud layer and receive device control information sent by the fog layer or the cloud layer.
As shown in FIG. 4, the specific workflow of the contract-based resource sharing mechanism among the fog clusters includes inputting a resource type k, a time slice t, the number n of the fog clusters, and a purchase price B of each fog cluster for the resource of the type kq(t) and selling price Sq(t) outputting the buyer set Bs and the seller set Ss of the type k resources and the corresponding lease price pr. Contract establishment for resources of a certain type k for a certain time slice includes buying a price Bq(t) and selling price Sq(t) respectively carrying out descending and ascending sorting to obtain arrays B and S, initializing the arrays to obtain B (0) and S (0) and a counter v of the arrays, and if S (0)<B (0), traversing the arrays B and S, finding the closest rent price and rent price to calculate the rent, otherwise, directly calculating the rent, wherein the rent m is [ B (v +1) + S (v +1)]2; and if the rent m is not more than B (v) and not less than S (v), forming the fog cluster which is bid for the corresponding price into a seller and a buyer, and establishing a contract with the rent m, otherwise, forming the fog cluster which is bid for the corresponding price into the seller and the buyer, and establishing a contract with the rent S (v). And continuously iterating the four steps from 1 to T in the time slice, and determining the auction result of each type k resource.
In the resource sharing mechanism, each fog cluster can be used for buying at a price according to the bidding strategy thereof
Figure BDA0002322359350000051
And selling price
Figure BDA0002322359350000052
The third party considers the buying price B of all types of k resources under a specific time slicek(b1,b2,...,bN) And selling price Sk(s1,s2,...,sN) Final pricing, buyer and seller are decided. The result of the auction is noted as
Figure BDA0002322359350000053
And
Figure BDA0002322359350000054
if x bi1, denotes bi is a buyer of type k resources, if x sj1, denotes that sj is a vendor of type k resources. The final bid and ask prices for the auction of the goods are determined as
Figure BDA0002322359350000055
And
Figure BDA0002322359350000056
thereafter, a contract is established between the ultimate buyer and seller.
Benefits of fog clusters from performing tasks on resources leased in contracts when local resources are insufficient
Figure BDA0002322359350000057
The method specifically comprises the following steps:
Figure BDA0002322359350000058
wherein Res is a function of the resource usage amount corresponding to the load estimation,
Figure BDA0002322359350000059
is the final renting resource price of the contract, tau is the time slice, k is the resource type, i is the fog cluster, rho i is the price of the unit resource used, lambdai(τ) is the workload of fog cluster i in time slice τ, Ci(τ) is the amount of resources in time slice τ for fog cluster i, VkThe amount of k-type resources contained in time slice τ for fog cluster i.
Benefits from distributing partial load to resources on other fog clusters when the operating overhead of local resources is greater for fog clusters
Figure BDA0002322359350000061
The method specifically comprises the following steps:
Figure BDA0002322359350000062
wherein the content of the first and second substances,
Figure BDA0002322359350000063
represents the overhead of running and managing resources of type k in fog cluster i.
The optimal bidding strategy for renting the fog cluster specifically comprises the following steps:
Figure BDA0002322359350000064
wherein the content of the first and second substances,
Figure BDA0002322359350000065
an optimal bidding strategy is entered for the lease.
The fog cluster rents resources to other fog clusters to obtain income when local resources are remained
Figure BDA0002322359350000066
The method specifically comprises the following steps:
Figure BDA0002322359350000067
wherein the content of the first and second substances,
Figure BDA0002322359350000068
the resource price is leased for the final contract.
Optimal bidding leasing strategy for fog cluster
Figure BDA0002322359350000069
The method specifically comprises the following steps:
Figure BDA00023223593500000610
where Null denotes an empty bid, Mi(τ) is a fog node list, DkRepresenting resources of type k.
The contract information comprises a mist cluster for renting resources, a renting price, a corresponding time slice and a mist node list covered in the contract. After the contract is established, each fog cluster updates the fog node list covered by the cluster, namely, the fog nodes rented in the auction are removed, and the rented fog nodes are added.
When the fog key nodes are selected, the centrality of the fog computing nodes in each fog cluster is evaluated by using a centrality method in graph theory. In the field of graph theory, the intermediate centrality is used for measuring the centrality of a node in a graph, mainly relating to the number of shortest paths passing through the node. There is at least one shortest path between each pair of vertices in the connected unweighted graph, so that the number of edges that the route passes through can be minimal. Accordingly, for weighted graphs, the intermediate centrality then uses the weighted sum over the edges to find the shortest path. A higher centrality value for a vertex generally means that the vertex can reach other vertices with the shortest possible delay, and also means that the vertex is on the path of the much shortest route. And after the middle centrality evaluation is finished, non-dominated sorting is carried out on the fog nodes based on NSGA-II, the middle centrality, the calculation performance and the communication delay reaching the terminal node of each fog node are considered, and the fog key nodes are selected according to the result.
The calculation formula of the middle centrality is specifically as follows:
Figure BDA00023223593500000611
wherein g (n) represents the median centrality of vertex n, σsdRepresenting the total number of shortest paths, σ, from vertex s to vertex dsd(n) represents the number of paths through vertex n in the shortest path from vertex s to vertex d.
As shown in fig. 5, the specific process of selecting the key fog node includes inputting a fog node set P ═ { fn ═1,fn2,…,fnnCPU on the fog nodes, resource information such as storage M and the like, connection topology Gr among the fog nodes, traversing and calculating middle centrality values of all the fog nodes in the fog node set, carrying out non-dominated sorting on the fog nodes based on NSGA-II, and simultaneously considering the calculation performance of each fog node and reaching a terminal nodeThe communication of the points is delayed, the fog key nodes are selected, and the fog key node set { fn of the current fog cluster is outputxX is some value between 1 and n.
Each of the key fog nodes dynamically and periodically clusters the remaining fog nodes, i.e., divides the functional domain, and divides the fog device nodes into different classes for the requirements of different types of applications, such as computation or memory or network intensive applications. In order to effectively construct the functional domain in the fog cluster, a spectral clustering method in an unsupervised clustering technology is adopted. The spectral clustering method is suitable for finding the similarity between different data points by using the concept of graph similarity, and is suitable for traversing the network graph in the fog environment. The similarity measure commonly used in spectral clustering is based on euclidean distance and gaussian kernel. After the dimensionality reduction, spectral clustering groups the data points using a simple clustering method, such as the k-means method. The clustering process is carried out in a continuous self-adaptive mode, and mainly aims to timely respond to the change of the environment, such as the removal or addition of fog equipment in a fog cluster.
As shown in fig. 6, the specific process of the functional fog domain division includes the following steps:
step S501: inputting the number N of nodes in the fog cluster, computing capacity C and storage M and other resources corresponding to each fog node, a neighbor classification kNN type, an adopted Gaussian function G and an updating step length s;
step S502: calculating the similarity between the fog nodes according to the resource information of the fog nodes, and obtaining a degree matrix D and a similar matrix W related to the fog nodes;
step S503: calculating a Laplace matrix L (D-W) according to the degree matrix D and the similarity matrix W, and standardizing L;
step S504: calculating the eigenvectors of the first k minimum eigenvalues in the L, forming an m-k dimensional matrix by the k eigenvectors, and converting the m-k dimensional matrix into a matrix Q according to standardization;
step S505: taking each row of m rows in the matrix Q as a k-dimensional sample, and carrying out k-means clustering;
step S506: outputting each type of fog section in the current fog clusterSet of points S1k。
Clustering requires grouping logically similar fog devices, so that a key fog node in each fog cluster can execute an effective clustering method, such as spectral clustering, considering factors such as available computing and memory resources of available fog devices. If a mist control node is aimed at creating a computational optimization domain, the corresponding spectral clustering will identify all similar mist devices that have sufficient processing resources. Each key fog node creates its own functional domain class, and a fog node with sufficient resources may belong to multiple functional domain classes.
In addition, it should be noted that the specific embodiments described in the present specification may have different names, and the above descriptions in the present specification are only illustrations of the structures of the present invention. Minor or simple variations in the structure, features and principles of the present invention are included within the scope of the present invention. Various modifications or additions may be made to the described embodiments or methods may be similarly employed by those skilled in the art without departing from the scope of the invention as defined in the appending claims.

Claims (10)

1. A task scheduling method based on time effectiveness of resource sharing in a cloud and fog environment is characterized in that a resource sharing mechanism based on contracts among fog clusters is established in a sealed bidding bilateral auction mode under a specific time slice, and resource allocation constructed based on a functional domain is carried out according to the resource sharing mechanism, and the method specifically comprises the following steps:
step S1: the fog cluster participates in the fog cluster resource auction at a rented resource price and a rented resource price according to local resources of the fog cluster;
step S2: the fog cluster determines an optimal bidding strategy according to the resources of other fog clusters and the prices of the rented resources and the rented resources;
step S3: the fog cluster participates in auction according to the optimal bidding strategy, and contracts are established with other corresponding fog clusters according to auction results to form the resource sharing mechanism;
step S4: selecting a fog key node under the resource sharing mechanism formed in the step S3;
step S5: and dynamically and periodically clustering the rest fog key nodes by the fog key nodes through a spectral clustering method to complete the division of the fog functional domains, and scheduling tasks among different fog functional domains.
2. The method of claim 1, wherein the cloud cluster gains from executing tasks on resources leased in the contract when local resources are insufficient
Figure FDA0002322359340000011
The method specifically comprises the following steps:
Figure FDA0002322359340000012
wherein Res is a function of the resource usage amount corresponding to the load estimation,
Figure FDA0002322359340000013
is the final renting resource price of the contract, tau is the time slice, k is the resource type, i is the fog cluster, rhoiFor the price of using a unit resource, lambdai(τ) is the workload of fog cluster i in time slice τ, Ci(τ) is the amount of resources in time slice τ for fog cluster i, VkThe amount of k-type resources contained in time slice τ for fog cluster i.
3. The method for scheduling task based on time effectiveness of resource sharing in cloud and fog environment as claimed in claim 2, wherein the fog cluster obtains the benefit of distributing partial load to the resource on other fog clusters when the running cost of the local resource is larger
Figure FDA0002322359340000014
The method specifically comprises the following steps:
Figure FDA0002322359340000015
wherein the content of the first and second substances,
Figure FDA0002322359340000016
represents the overhead of running and managing resources of type k in fog cluster i.
4. The method for task scheduling based on time effectiveness of resource sharing in cloud and mist environment according to claim 3, wherein the optimal bidding strategy for renting the mist cluster specifically comprises:
Figure FDA0002322359340000021
wherein the content of the first and second substances,
Figure FDA0002322359340000022
an optimal bidding strategy is entered for the lease.
5. The method for scheduling tasks based on time effectiveness of resource sharing in cloud and fog environment as claimed in claim 1, wherein the fog cluster rents resources to other fog clusters to obtain the income when the local resources are remained
Figure FDA0002322359340000023
The method specifically comprises the following steps:
Figure FDA0002322359340000024
wherein the content of the first and second substances,
Figure FDA0002322359340000025
funding for final rental of contractsThe source price.
6. The method for scheduling task based on time effectiveness of resource sharing in cloud and fog environment as claimed in claim 5, wherein the lease optimal bidding strategy of the fog cluster
Figure FDA0002322359340000026
The method specifically comprises the following steps:
Figure FDA0002322359340000027
where Null denotes an empty bid, Mi(τ) is a fog node list, DkRepresenting resources of type k.
7. The method according to claim 1, wherein the information of the contract comprises a fog cluster of rented resources, a rented price, a corresponding time slice, and a fog node list covered in the contract.
8. The method for scheduling tasks based on the time effectiveness of resource sharing in the cloud and fog environment as claimed in claim 1, wherein the process of selecting the fog key nodes includes calculating the middle centrality, the calculation performance and the communication delay reaching the terminal node of the internet of things of each fog node in the fog cluster, and selecting the fog key nodes by performing non-dominated sorting according to the calculation result.
9. The method for scheduling task based on time availability of resource sharing in cloud and fog environment as claimed in claim 8, wherein the calculation formula of the intermediate centrality is specifically:
Figure FDA0002322359340000028
wherein g (n) represents the median centrality of vertex n, σsdRepresenting the total number of shortest paths, σ, from vertex s to vertex dsd(n) represents the number of paths through vertex n in the shortest path from vertex s to vertex d.
10. The method for scheduling task based on time effectiveness of resource sharing in cloud and fog environment as claimed in claim 1, wherein the grouping criteria of the spectral clustering method is based on available computing and memory resources of available fog devices.
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