CN111045827B - Task scheduling method based on time effectiveness of resource sharing in cloud and fog environment - Google Patents
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Abstract
The invention relates to a task scheduling method based on time effectiveness of resource sharing in cloud and fog environment, which establishes a contract-based resource sharing mechanism 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 by 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 contracts with the corresponding fog clusters according to the auction result to form a resource sharing mechanism; step S4: selecting a fog key node under a resource sharing mechanism; step S5: the fog key nodes complete the division of fog functional domains by a spectral clustering method, and task scheduling is carried out among different fog functional domains. Compared with the prior art, the cloud computing system has the advantages of fully utilizing fog resources, reducing cloud workload, reducing response time of tasks and the like.
Description
Technical Field
The invention relates to the field of computer edge computing, in particular to a task scheduling method based on time effectiveness of resource sharing in a cloud and fog environment.
Background
Edge calculations (fog calculations) are generated with the rapid development and widespread use of everything interconnects, the ever-increasing data size, and the ever-increasing computational demands of data processing. The edge calculation makes everything more intelligent and supports building of a healthy edge application ecology. The edge calculation is a supplement and extension to cloud calculation, and provides a better calculation platform for mobile calculation, the Internet of things and the like. The edge computing requires the support of the powerful computing capacity and mass storage of the cloud computing center, and the cloud computing also requires the processing of the mass data and the privacy data by the edge equipment in the edge computing, so that the requirements of instantaneity, privacy protection and energy consumption reduction are met.
Based on the feature that cloud computing is farther from the user and fog/edge computing is closer to the user, many of the existing techniques are related to how to migrate application requests of the internet of things layer to fog or cloud for execution, so as to minimize the time of application tasks, or minimize the exhaustion of application requests when executing, or consider optimization of both indexes at the same time. Some technologies also relate to the effect of user mobility (switching of geographic locations) on the completion of application requests, such as the user requesting to download video resources, and if the user is moving continuously, the performance of video downloading can be greatly affected. However, the existing technology is directed to the scheduling of the fog resources in a certain area/range, and does not consider the situation of sharing among the fog resources in different areas (the fog resources in a cell or a city can be called as a fog cluster). While fog calculation can ensure faster task response times with the advantage of closer physical distance as well as network distance, its resources are relatively limited. Therefore, how to fully utilize the resources of the fog layer, while reducing the response time of the task is a key issue.
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 provides a task scheduling method based on time effectiveness of resource sharing in a cloud and fog environment.
The aim of the invention can be achieved by the following technical scheme:
a task scheduling method based on time effectiveness of resource sharing in cloud and fog environment establishes a contract-based resource sharing mechanism among fog clusters in a sealed bidding double-sided auction mode under a specific time slice, and performs resource allocation based on functional domain construction according to the resource sharing mechanism, specifically comprising the following steps:
step S1: the fog cluster participates in the fog cluster resource auction according to local resources of the fog cluster by a renting resource price and a renting resource price;
step S2: the fog clusters determine an optimal bidding strategy according to the resources of other fog clusters and the price of rented resources;
step S3: the fog clusters participate in auction according to the optimal bidding strategy, and contracts are established with other corresponding fog clusters according to auction results, so that the resource sharing mechanism is formed;
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 through a spectral clustering method to finish the division of fog functional domains, and task scheduling is carried out among different fog functional domains.
Mist clusters benefit from performing tasks on resources leased in the contract when local resources are insufficientThe method comprises the following steps:
where Res is a function of estimating its corresponding resource usage from the load,for the final lease price of the contract, τ is the time slice, k is the resource type, i is the fog cluster, ρi is the price of using unit resource, λ i (tau) is the workload of the mist cluster i in the time slice tau,C i (tau) is the amount of resources in the time slice tau, V of the fog cluster i k Is the amount of k-type resources contained in the time slice τ for the fog cluster i.
The mist clusters obtain benefits when the running cost of local resources is larger and partial load is distributed to the resources on other mist clustersThe method comprises the following steps:
wherein,representing the overhead of running and managing resources of type k in the fog cluster i.
The optimal renting bidding strategy of the fog cluster specifically comprises the following steps:
wherein,and (5) renting an optimal bidding strategy.
When the local resource is remained, the fog clusters rent the resource to other fog clusters to obtain the benefitThe method comprises the following steps:
wherein,the resource price is leased for the final of the contract.
Lease optimal bidding strategy of fog clusterThe method comprises the following steps:
where Null represents an empty bid, M i (τ) is a list of foggy nodes, D k Representing a resource of type k.
The information of the contract comprises fog clusters of leased resources, fog clusters of leased-in resources, leased-in prices, corresponding time slices and fog node lists covered in the contract.
The process of selecting the fog key nodes comprises the steps of calculating the middle centrality, the calculation performance and the communication delay reaching the terminal nodes of the Internet of things of each fog node in the fog cluster, and selecting the fog key nodes according to non-dominant sorting of calculation results.
The calculation formula of the middle centrality is specifically as follows:
wherein g (n) represents the intermediate centrality of vertex n, σ sd Representing the total shortest path number, σ, from vertex s to vertex d sd (n) represents the number of paths passing through vertex n among the shortest paths from vertex s to vertex d.
The grouping criteria of the spectral clustering method are based on available computing 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, by establishing a contract-based resource sharing mechanism among the fog clusters, under the condition of fog cluster resource sharing, fog function domain division and resource allocation are carried out, so that effective task scheduling is realized, fog layer resources are fully utilized, more tasks can be executed at a fog end, the pressure of a core network between a user and a cloud end is reduced, the work load of the cloud end is reduced, and the response time of the tasks is reduced.
2. The invention selects the fog key nodes by the centrality method, and the higher the centrality value of a certain vertex is, the higher the centrality value of the certain vertex is usually, the vertex can reach other vertices by virtue of the shortest possible delay, and meanwhile, the vertex is on a plurality of paths of the shortest route, so that the efficiency and the accuracy of selecting the fog key nodes are improved.
3. The invention uses a spectral clustering method when the fog function domain is divided, and searches the similarity among different data points by using the similarity of the images, so that the method is suitable for traversing the network images in fog environment.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a schematic diagram of a foggy end contract auction mechanism of the present invention;
FIG. 3 is a schematic diagram of the cloud environment resource sharing of the present invention
FIG. 4 is a schematic flow chart of the mist cluster resource sharing mechanism of 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 partitioning of the present invention.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples. The present embodiment is implemented on the premise of the technical scheme of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following examples.
As shown in fig. 1, a task scheduling method based on time effectiveness of resource sharing in cloud and fog environment establishes a contract-based resource sharing mechanism among fog clusters in a sealed bidding double-sided auction manner under a specific time slice, and performs resource allocation based on functional domain construction according to the resource sharing mechanism, specifically comprising the following steps:
step S1: the fog cluster participates in the fog cluster resource auction according to local resources of the fog cluster by a renting resource price and a renting resource price;
step S2: the fog clusters determine an optimal bidding strategy according to the resources of other fog clusters, the price of rented resources and the price of rented resources;
step S3: the fog clusters participate in auction according to the optimal bidding strategy, and contracts are established with other corresponding fog clusters according to auction results, so that a resource sharing mechanism is formed;
step S4: selecting a fog key node under the resource sharing mechanism formed in the step S3;
step S5: the fog key nodes dynamically and periodically cluster the rest fog nodes through a spectral clustering method to finish the division of fog functional domains, and task scheduling is carried out among different fog functional domains.
As shown in fig. 2 and 3, the system model for contract-based cloud cluster resource sharing in a cloud environment contains 3 logical layers. The first logic layer is a cloud layer, mainly performs global resource scheduling and intelligent decision making, serves as a third-party auctioneer when a resource sharing mechanism is established, and is responsible for the selection of key fog nodes in each fog cluster and also performs part of user requests; the second logic layer is a fog end layer and comprises different fog cluster resources corresponding to a plurality of different areas, each fog cluster is composed of a plurality of mutually connected fog nodes, the resources in the fog clusters are mainly responsible for intelligent sensing, intelligent computing, 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 at the bottom layer, and a plurality of key fog nodes possibly exist in each fog cluster. The cloud layer and the fog layer need to perform data coordination, service management coordination, intelligent coordination and application management coordination so as to achieve optimization of performance; the bottommost layer is a terminal equipment layer, and terminal equipment of the layer can send a task processing request to an upper fog end layer or a cloud end layer and receive equipment control information sent by the fog end layer or the cloud end layer.
As shown in FIG. 4, a specific workflow of the inter-mist cluster contract-based resource sharing mechanism includes inputting a resource type k, a time slice t, and a mist cluster numbern and the buying price B of each fog cluster to type k resources q (t) and selling price S q (t) outputting the buyer set Bs and the seller set Ss of the type k resource and the corresponding lease price pr. The contract establishment procedure for a certain type k of resource under a certain time slice includes buying price B q (t) and selling price S q (t) performing descending and ascending orders respectively 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, searching the nearest rental price and the nearest rental price to calculate the rent, otherwise directly calculating the rent, wherein the rent m= [ B (v+1) +S (v+1)]2; if the lease m is not more than B (v) and not less than S (v), the fog cluster with the bid corresponding to the price is used as a seller and a buyer, a contract with the lease m is established, otherwise, the fog cluster with the bid corresponding to the price is used as the seller and the buyer, and the contract with the lease S (v) is established. The four steps above iterate continuously for time slices of 1 to T to determine the auction result for each type k resource.
In the resource sharing mechanism, each fog cluster can buy price according to the bidding strategy of the fog clusterSelling priceBidding, the third party considers the buying price B of all type k resources under a specific time slice k (b 1 ,b 2 ,...,b N ) And selling price S k (s 1 ,s 2 ,...,s N ) The final pricing, buyer and seller are determined. The result of the auction is marked->And->If x bi =1, indicating that bi is a buyer of type k resource, if x sj =1, meaning sj is one seller of type k resources. The final buying and selling prices of the auction for the commodity are determined as/>And->Thereafter, a contract is established between the final buyer and seller.
Mist clusters benefit from performing tasks on resources leased in contracts when local resources are scarceThe method comprises the following steps:
where Res is a function of estimating its corresponding resource usage from the load,for the final lease price of the contract, τ is the time slice, k is the resource type, i is the fog cluster, ρi is the price of using unit resource, λ i (τ) is the workload of the mist cluster i in the time slice τ, C i (tau) is the amount of resources in the time slice tau, V of the fog cluster i k Is the amount of k-type resources contained in the time slice τ for the fog cluster i.
Benefits obtained by distributing partial load to resources on other fog clusters when running overhead of local resources is largerThe method comprises the following steps:
wherein,indicating running sum pipe in fog cluster iAnd (3) processing the overhead of the resource with k type.
The optimal renting bidding strategy of the fog clusters is specifically as follows:
wherein,and (5) renting an optimal bidding strategy.
When local resources are left in the fog clusters, the fog clusters rent the resources to other fog clusters to obtain benefitsThe method comprises the following steps:
wherein,the resource price is leased for the final of the contract.
Lease optimal bidding strategy for fog clusterThe method comprises the following steps:
where Null represents an empty bid, M i (τ) is a list of foggy nodes, D k Representing a resource of type k.
The information of the contract includes fog clusters of leased resources, fog clusters of leased-in resources, leased-in prices, corresponding time slices, and fog node lists covered in the contract. After the contract is established, each fog cluster updates the fog node list covered by the fog cluster, namely, the fog nodes leased in the auction are removed, and the leased fog nodes are added.
And when the selection of the fog key nodes is carried out, evaluating the centrality of the fog calculation nodes in each fog cluster by using a centrality method in graph theory. In the field of research of graph theory, the middle centrality is used to measure the centrality of a node in a graph, mainly with respect to the number of shortest paths through the node. There is at least one shortest path between each pair of vertices in the connected graph such that the number of edges traversed by the route may be minimal. Correspondingly, for weighted graphs, the center of gravity uses the sum of weights on the edges to find the shortest path. A higher centrality value for a vertex generally indicates that the vertex can reach other vertices with the shortest possible delay, and also means that the vertex is on a much shortest-routed path. After the intermediate centrality evaluation is completed, non-dominant ordering is carried out on the fog nodes based on NSGA-II, the intermediate 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:
wherein g (n) represents the intermediate centrality of vertex n, σ sd Representing the total shortest path number, σ, from vertex s to vertex d sd (n) represents the number of paths passing through vertex n among the shortest paths 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 ,fn 2 ,…,fn n CPU and storage M on the fog nodes, connection topology Gr among the fog nodes, then traversing and calculating intermediate centrality values of all the fog nodes in the fog node set, non-dominant ordering the fog nodes based on NSGA-II, selecting fog key nodes by considering calculation performance of each fog node and communication delay reaching a terminal node, and outputting a fog key node set { fn of the current fog cluster x X is some value between 1 and n.
Each fog cluster has 1 or more key fog nodes, each key fog node can dynamically and periodically cluster the remaining fog nodes, namely, divide functional domains, and divide fog equipment nodes into different classes for the demands of different types of applications, such as computing or memory or network intensive applications. In order to effectively construct the functional domains in the fog clusters, a spectral clustering method in an unsupervised clustering technology is adopted. The spectral clustering method is suitable for searching the similarity between different data points by using the concept of the similarity of the graph, 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 kernels. After dimension reduction, spectral clustering groups the data points using a simple clustering method such as the k-means method. The clustering process is performed in a continuous self-adaptive manner, mainly to cope with environmental changes in time, such as removal or addition of fog devices in the fog cluster.
As shown in fig. 6, the specific procedure of the fog function region division includes the following steps:
step S501: inputting the number N of nodes in the fog cluster, the corresponding computing power C and storage M of each fog node and other resources, neighbor classification kNN types, the adopted Gaussian function G and the updating step length s;
step S502: calculating the similarity between fog nodes according to the resource information of the fog nodes, and obtaining a similarity matrix D and a similarity 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 normalizing L;
step S504: calculating the feature vectors of the first k minimum feature values in L, forming the k feature vectors into a matrix of m-k dimensions, and converting the matrix into a matrix Q according to standardization;
step S505: taking each of m rows in the matrix Q as a k-dimensional sample, and carrying out k-means clustering;
step S506: outputting a set S1 of each type of fog node in the current fog cluster k 。
The clustering process needs to group logically similar fog devices, so that key fog nodes in each fog cluster can execute an effective clustering method, such as spectral clustering, and considered factors are available calculation and memory resources of available fog devices. If a fog control node aims at creating a computational optimization domain, then the corresponding spectral clusters will identify all similar fog devices that have sufficient processing resources. Each key foggy node creates its own functional domain class, and a foggy node with sufficient resources can belong to multiple functional domain classes.
Furthermore, the particular embodiments described herein may vary from one embodiment to another, and the above description is merely illustrative of the structure of the present invention. All such small variations and simple variations in construction, features and principles of the inventive concept are intended to be included within the scope of the present invention. Various modifications or additions to the described embodiments or similar methods may be made by those skilled in the art without departing from the structure of the invention or exceeding the scope of the invention as defined in the accompanying claims.
Claims (3)
1. A task scheduling method based on time effectiveness of resource sharing in cloud and fog environment is characterized in that a contract-based resource sharing mechanism between fog clusters is established in a sealed bidding double-side auction mode under a specific time slice, and resource allocation based on functional domain construction 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 according to local resources of the fog cluster by a renting resource price and a renting resource price;
step S2: the fog clusters determine an optimal bidding strategy according to the resources of other fog clusters and the price of rented resources;
step S3: the fog clusters participate in auction according to the optimal bidding strategy, and contracts are established with other corresponding fog clusters according to auction results, so that the resource sharing mechanism is formed;
step S4: selecting a fog key node under the resource sharing mechanism formed in the step S3;
step S5: the fog key nodes dynamically and periodically cluster the rest fog nodes through a spectral clustering method to finish the division of fog functional domains, and task scheduling is carried out among different fog functional domains;
the mist cluster obtains benefits by executing tasks on resources leased in the contract when the local resources are insufficientThe method comprises the following steps:
where Res is a function of estimating its corresponding resource usage from the load,for the final lease resource price of the contract, τ is the time slice, k is the resource type, i is the fog cluster, ρ i Lambda for the price of using unit resource i (τ) is the workload of the mist cluster i in the time slice τ, C i (tau) is the amount of resources in the time slice tau, V of the fog cluster i k The amount of k-type resources contained in the time slice τ for the fog cluster i;
the mist clusters obtain benefits when the running cost of local resources is larger and partial load is distributed to the resources on other mist clustersThe method comprises the following steps:
wherein,representing the overhead of running and managing resources of type k in the fog cluster i;
the optimal renting bidding strategy of the fog cluster specifically comprises the following steps:
wherein,optimal bidding strategies are leased in;
when the local resource is remained, the fog clusters rent the resource to other fog clusters to obtain the benefitThe method comprises the following steps:
wherein,lease a resource price for the final of the contract;
lease optimal bidding strategy of fog clusterThe method comprises the following steps:
where Null represents an empty bid, M i (τ) is a list of foggy nodes, D k Representing a resource of type k;
the process of selecting the fog key nodes comprises the steps of calculating the middle centrality, the calculation performance and the communication delay reaching the terminal nodes of the Internet of things of each fog node in the fog cluster, and selecting the fog key nodes according to non-dominant sorting of calculation results;
the calculation formula of the middle centrality is specifically as follows:
wherein g (n) represents the intermediate centrality of vertex n, σ sd Representing the total shortest path number, σ, from vertex s to vertex d sd (n) represents the number of paths passing through vertex n among the shortest paths from vertex s to vertex d;
1 or more key fog nodes exist in each fog cluster, each key fog node can dynamically and periodically cluster the rest fog nodes to divide functional domains, and the fog equipment nodes are divided into different classes for the requirements of different types of applications, wherein the types of the applications comprise computing, memory or network intensive applications; the functional domain is divided by adopting a spectral clustering method in an unsupervised clustering technology.
2. The method for scheduling tasks based on time availability of resource sharing in cloud environment according to claim 1, wherein the information of the contract comprises a cloud cluster of leased resources, a lease price, a corresponding time slice, and a list of cloud nodes covered in the contract.
3. The task scheduling method based on time effectiveness of resource sharing in cloud environment of claim 1, wherein the grouping criteria of the spectral clustering method is based on available computing and memory resources of available cloud devices.
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