CN104333569A - Cloud task scheduling algorithm based on user satisfaction - Google Patents

Abstract

A cloud task scheduling algorithm based on user satisfaction, which mainly includes parameter normalization of virtual machines and cloud tasks, calculation of Euclidean distance, resource selection, update of Euclidean distance, calculation of user satisfaction of tasks, and calculation of a single cloud task There are several stages such as the cost of resource usage and the total cost of the system after all tasks are executed. The algorithm of the present invention allocates tasks to the most suitable resources from the user's point of view, better meets the user's requirements for CPU, completion time, bandwidth and other aspects, and at the same time effectively reduces the user's cost of using resources. In cloud computing, what users care about is whether the cost paid and the quality of service received are reasonably matched, so that the needs of users can be satisfied to a high degree. Compared with the prior art, the present invention provides an effective strategy to allow users to obtain better service quality.

Description

Cloud task scheduling algorithm based on user satisfaction

technical field

The invention relates to a cloud task scheduling algorithm.

Background technique

Cloud computing is the comprehensive development of parallel computing, distributed computing and grid computing. The system can obtain computing power, storage space and information services as needed. However, while implementing these services, a problem needs to be considered, that is, different users have different requirements for the use of cloud computing resources, such as CPU, memory, completion time, bandwidth, usage fees, etc., how to adopt an effective strategy Allow users to obtain better service quality. The task scheduling algorithm of cloud computing is one of the ways to solve the above problems.

The traditional task scheduling algorithm focuses on the efficiency of the server. For example, the task scheduling method with the goal of optimal completion time, although it has good completion efficiency, may lead to high resource usage with strong computing power, which makes the system load unbalanced; load The balance algorithm can provide an effective method to expand the bandwidth of network devices and servers, increase throughput, strengthen network data processing capabilities, and improve network flexibility and availability. However, traditional task scheduling algorithms ignore the quality of service requirements of user tasks , cannot allocate resources on demand very well.

Contents of the invention

In the research process of cloud task scheduling technology, many classic scheduling algorithms have been formed. They mostly start from the perspective of cloud resource providers, considering optimal completion time, minimum energy consumption, node load balancing, resource availability and reliability, system Utilization rate and other parameters, while the algorithm proposed by the present invention focuses on parameters such as task completion time, cost, matching degree of cost and service quality, user satisfaction with resource use from the perspective of users, and also considers system load balancing.

Cloud computing uses virtualization technology to encapsulate the underlying physical resources in the form of virtual machines, allowing virtual machines to perform user tasks. The scheduling problem is to map the user's tasks with resources based on a certain optimization goal. Cloud computing simplifies the matching of tasks and resources, and makes the resources required by the tasks reflected in the form of a virtual machine. Therefore, the search for resources is transformed into Search for a virtual machine.

In order to realize the scheduling algorithm, the present invention firstly describes cloud tasks, virtual machines and task classification:

●The virtual machine is represented by a seven-tuple:

vm _i ＝<id _i ,peNum _i ,ram _i ,bw _i ,C _cpu/num ,C _mem/MB ,C _bw/Mbps > (1)

The seven-tuple represents the ID of the virtual machine, the number of CPUs, the memory, the bandwidth, and the unit price of the CPU, memory, and bandwidth.

●Cloud tasks are represented by octets:

t _i ＝<id _i ,type _i ,len _i ，exppe _i ,expram _i ,expbw _i ,s _i ,cost _i > (2)

The octet represents the ID, type, task size, expected number of CPUs, expected memory, expected bandwidth, user satisfaction of the task, and cost of executing the task, respectively, of the cloud task.

Cloud task type: the present invention mainly considers the following QoS parameters:

a) Completion time: For cloud tasks with real-time requirements, they need to be completed in as little time as possible, corresponding to the two resources of CPU and execution speed.

b) Bandwidth: When cloud tasks have high communication bandwidth requirements, such as multimedia streaming requirements, bandwidth requirements need to be prioritized.

c) Memory: When cloud tasks have high memory requirements, priority should be given to memory requirements.

For different cloud task requirements, user satisfaction is measured according to different QoS parameters. For this reason, the present invention designs a weight vector, which represents the value recognition degree of the cloud platform for different resources, and uses the weight vector to adjust and select the virtual machine Resource performance ratio parameters, in order to better improve user satisfaction with resources. For example, for real-time or time-sensitive cloud tasks, it is hoped to complete the task with the minimum completion time, so resources with strong computing power are needed, so the CPU is given a greater weight. Let the weight vector of the i-th task be expressed as:

ei=[ei ₁ ,ei ₂ ,ei ₃ ] (3)

Among them, ei ₁ , ei ₂ , and ei ₃ correspond to the weights of CPU, memory, and bandwidth respectively, and

${\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}{\mathrm{<span\; class="notranslate">\; ei</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

The idea of the cloud task scheduling algorithm based on user satisfaction is as follows: For a given set of cloud tasks, select the cloud task with the highest priority in the system, normalize its parameters, and then normalize the normalized cloud task Calculate the Euclidean distance between the cloud task and all virtual machines in the system (all the virtual machines have been normalized in advance). When calculating the Euclidean distance, according to the type of cloud task and the degree of recognition of the value of each parameter by the system, different values are given. Different weights of performance parameters bind the current cloud task to the virtual machine with the smallest Euclidean distance value. In order to balance the load of the system and ensure the completion time of all tasks, when a virtual machine is bound to a cloud task, its Euclidean distance list is updated to reduce the possibility that the next cloud task will be assigned to the same virtual machine. After the current task is executed, calculate its user satisfaction and resource usage cost. After all tasks are executed, calculate the comprehensive satisfaction of all cloud tasks and the total cost of the system.

The technical solution to be protected in the present invention is characterized by:

A kind of cloud task scheduling algorithm based on user satisfaction, it is characterized in that, comprises the steps:

Step 1, resource parameter normalization.

Normalize the performance parameters of tasks and virtual machines to the [0,1] interval, let the set X _ij ={X _1j ,...,X _tj }j be the number of performance parameters, and be the number of similar performance parameters of virtual machines collection whose normalized value is:

GX _ij ＝(curX _ij -minX _ij )/(maxX _ij -minX _ij ) (5)

Among them, i is the number of tasks, j is the number of performance parameters, curX _ij is the current value of performance, minX _ij is the minimum value in the same performance parameter set, and maxX _ij is the maximum value in the same kind of performance parameter set.

Step 2, calculate the Euclidean distance.

After parameter normalization processing, the parameter vector of the virtual machine is X={X ₁ , X ₂ , X ₃ }, and the parameter vector of the cloud task is Y={Y ₁ , Y ₂ , Y ₃ }. Considering the three performance parameters of CPU, memory and bandwidth, the weight vector W={W ₁ ,W ₂ ,W ₃ } is obtained according to the type of cloud task. The formula for calculating the Euclidean distance is:

${\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\sqrt{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; *</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

Where X _j represents the normalized parameter value of the jth resource in the i-th virtual machine; Y _j represents the expected value of the task for the j-th resource; W _j represents the weight of the j-th resource.

Step 3, select resources.

Each task selects the virtual machine with the smallest Euclidean distance to execute the task, and uses the method of controlling idle virtual machines for load balancing. Each virtual machine maintains a Euclidean distance table. When a task is successfully assigned to a virtual machine for execution After that, the Euclidean distance table needs to be updated to increase the Euclidean distance between a certain type of resource and the virtual machine. The update formula is:

D _i ′＝D _i (1+1/n), n is the number of virtual machines (7)

Step 4, calculate user satisfaction.

After the task is completed, consider the completion of each task, including the completion time of the task and the user satisfaction of each task, as well as the overall satisfaction of all tasks. The user satisfaction of a single task is:

${\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}\mathrm{<span\; class="notranslate">\; ln</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; act</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; exp</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$

Among them, s _i is the user satisfaction of task i; W _j is the weight of the jth performance parameter; act _j is the actual consumption of the jth performance parameter by the task; exp _j is the user expectation of the jth performance of the cloud task value.

When 0≤|s _i |≤0.5, it is considered that the user is satisfied with the resource allocation of cloud task i; when 0.5<|s _i |≤1, it is considered that the user is satisfied with the resource allocation of cloud task i; when | When s _i | > 1, it is considered that the user is not satisfied with the resource allocation of cloud task i; when the value of |s _i | is large, it is considered that the user is very dissatisfied with the resource allocation of cloud task i.

The comprehensive user satisfaction of all cloud tasks is:

$\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 9</span>}<span\; class="notranslate">\; )</span>$

Where s _i is the user satisfaction of the i-th task; t is the number of all cloud tasks. In a cloud computing system, the smaller the value of S, the higher the satisfaction of all users of the system and the services provided by the cloud computing service provider.

Step 5, calculate the cost.

After each cloud task is executed, the cost of executing the task is calculated. Virtual machines are billed for resources by unit, and the total cost of task consumption cost _i is:

${\mathrm{<span\; class="notranslate">\; cos</span>}\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; cpu</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; num</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\frac{\mathrm{<span\; class="notranslate">\; meme</span>}}{\mathrm{<span\; class="notranslate">\; MB</span>}}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; bw</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; Mbps</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

Among them, P _i is the resource quantity, and C is the unit resource price.

After performing all cloud tasks, the total cost of the system is:

$\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}{\mathrm{<span\; class="notranslate">\; cos</span>}\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 11</span>}<span\; class="notranslate">\; )</span>$

Where cost _i is the cost of the i-th task; t is the number of all cloud tasks. In a cloud computing system, the smaller the value of C, the smaller the cost of the system to perform all cloud tasks.

The algorithm of the present invention allocates tasks to the most suitable resources from the user's point of view, better meets the user's requirements for CPU, completion time, bandwidth and other aspects, and at the same time effectively reduces the user's cost of using resources. In cloud computing, what users care about is whether the cost paid and the quality of service received are reasonably matched, so that the needs of users can be satisfied to a high degree. Compared with the prior art, the present invention provides an effective strategy to allow users to obtain better service quality.

Description of drawings

Figure 1. Flow chart of cloud task scheduling algorithm based on user satisfaction.

Fig. 2 Simulation results of cloud task scheduling algorithm based on user satisfaction.

Fig. 3 Simulation results of optimal completion time scheduling algorithm.

Figure 4 Comparison of task completion time.

Figure 5 Comparison of task user satisfaction.

Figure 6 Comparison of task execution costs.

Detailed ways

In cloud computing, users are not very concerned about the performance of the system. What they care about is whether the cost paid and the quality of service obtained are reasonably matched. In order to satisfy the needs of users to a higher degree, the present invention proposes A cloud task scheduling algorithm based on user satisfaction. From the user's point of view, the algorithm allocates tasks to the most appropriate resources to better meet the user's needs for CPU, completion time, bandwidth, etc., while effectively reducing the cost of using resources for users. Finally, the present invention uses the CloudSim platform to simulate the algorithm, and compares the algorithm with the currently more commonly used optimal completion time scheduling algorithm to verify the effectiveness of the algorithm in terms of user satisfaction and resource usage costs.

The present invention will be further described below in conjunction with the algorithm flow chart of the accompanying drawings.

Fig. 1 is the algorithm flowchart of the present invention. As shown in the figure, the algorithm mainly includes parameter normalization of virtual machines and cloud tasks, calculation of Euclidean distance, resource selection, update of Euclidean distance, calculation of user satisfaction of tasks, calculation of resource usage cost of a single cloud task, and execution of all tasks The total cost of the system after the completion of several stages.

Step 1, resource parameter normalization. In order to calculate the Euclidean distance more conveniently, the present invention normalizes the performance parameters of tasks and virtual machines to the interval [0,1], and sets X _ij ={X _1j ,...,X _tj }j as performance parameters The number of is a collection of similar performance parameters of the virtual machine, and its normalized value is:

GX _ij ＝(curX _ij -minX _ij )/(maxX _ij -minX _ij ) (5)

Among them, i is the number of tasks, j is the number of performance parameters, curX _ij is the current value of performance, minX _ij is the minimum value in the same performance parameter set, and maxX _ij is the maximum value in the same kind of performance parameter set.

Step 2, calculate the Euclidean distance. After parameter normalization processing, the parameter vector of the virtual machine is X={X ₁ , X ₂ , X ₃ }, and the parameter vector of the cloud task is Y={Y ₁ , Y ₂ , Y ₃ }. The present invention mainly considers three performance parameters of CPU, memory and bandwidth, and obtains a weight vector W={W ₁ , W ₂ , W ₃ } according to the type of cloud task. The formula for calculating the Euclidean distance is:

${\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\sqrt{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; *</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

Where X _j represents the normalized parameter value of the jth resource in the i-th virtual machine; Y _j represents the expected value of the task for the j-th resource; W _j represents the weight of the j-th resource.

The smaller the Euclidean distance between the task and the virtual machine, it means that the task chooses the virtual machine to obtain relatively better user satisfaction, and at the same time, it can better meet the value recognition needs of cloud computing providers for different resources.

Step 3, select resources. Each task selects the virtual machine with the smallest Euclidean distance to perform the task, but considering the load balancing problem of the system, it is avoided to assign all tasks to a powerful virtual machine at the same time, and in order to meet the optimization of the task completion time as much as possible, The method of controlling idle virtual machines is used for load balancing, so each virtual machine maintains a Euclidean distance table. When a task is successfully assigned to a virtual machine for execution, the Euclidean distance table needs to be updated to add certain types of resources and The Euclidean distance between the virtual machines, the update formula is:

D _i ′＝D _i (1+1/n), n is the number of virtual machines (7)

The resource selection process is as follows:

1. For i=1 to m

2 Select VM by parameter of t _i to VM _i ; (all tasks have the same performance to form a vector)

3 For i＝1 to t

4 For j＝1 to 3

5 Compute GX _ij (parameter normalization processing);

6 For i＝1 to t

7 Calculate the Euclidean distance D _i between the task and the virtual machine;

8 Select min D _i ;

9 Bind t _i to VM which has the min D _i ;

10 End;

Step 4, calculate user satisfaction. After the task is completed, the completion of each task needs to be considered. Including task completion time and user satisfaction of each task, as well as the overall satisfaction of all tasks. The user satisfaction of a single task is:

${\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; 3</span>}}{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}\mathrm{<span\; class="notranslate">\; ln</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; act</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; exp</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$

Among them, s _i is the user satisfaction of task i; W _j is the weight of the jth performance parameter; act _j is the actual consumption of the jth performance parameter by the task; exp _j is the user expectation of the jth performance of the cloud task value.

When 0≤|s _i |≤0.5, it is considered that the user is satisfied with the resource allocation of cloud task i; when 0.5<|s _i |≤1, it is considered that the user is satisfied with the resource allocation of cloud task i; when | When s _i | > 1, it is considered that the user is not satisfied with the resource allocation of cloud task i; when the value of |s _i | is large, it is considered that the user is very dissatisfied with the resource allocation of cloud task i.

The comprehensive user satisfaction of all cloud tasks is:

$\mathrm{<span\; class="notranslate">\; S</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 9</span>}<span\; class="notranslate">\; )</span>$

Where s _i is the user satisfaction of the i-th task; t is the number of all cloud tasks. In a cloud computing system, the smaller the value of S, the higher the satisfaction of all users of the system and the services provided by the cloud computing service provider.

Step 5, calculate the cost. After each cloud task is executed, the cost of executing the task needs to be calculated. The virtual machine bills the resource according to the unit. Therefore, the total cost _i of task consumption is:

${\mathrm{<span\; class="notranslate">\; cos</span>}\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; cpu</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; num</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\frac{\mathrm{<span\; class="notranslate">\; meme</span>}}{\mathrm{<span\; class="notranslate">\; MB</span>}}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; bw</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; Mbps</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

Among them, P _i is the resource quantity, and C is the unit resource price.

After performing all cloud tasks, the total cost of the system is:

$\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}^{\mathrm{<span\; class="notranslate">\; t</span>}}{\mathrm{<span\; class="notranslate">\; cos</span>}\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 11</span>}<span\; class="notranslate">\; )</span>$

Where cost _i is the cost of the i-th task; t is the number of all cloud tasks. In a cloud computing system, the smaller the value of C, the smaller the cost of the system to perform all cloud tasks.

Step 6, algorithm simulation. The simulated cloud environment of the present invention is composed of 5 virtual machine nodes, resource agents are established on the nodes, and 10 simulated user tasks are established. The simulation mainly examines the following aspects: the completion time of all cloud tasks, the user satisfaction of a single cloud task, the overall satisfaction of all tasks, the execution cost of a single task, and the cost of executing all cloud tasks by the system. The unit price of each is 1 (you can set it yourself in actual use). Figure 2 shows the simulation results of the cloud task scheduling algorithm based on user satisfaction. Figure 3 is the simulation result of the optimal completion time scheduling algorithm.

It can be seen from Figure 4 that in the optimal completion time algorithm, task 3 is the last to be completed, and the total completion time is 334.62 ms. In the cloud task scheduling algorithm based on user satisfaction, task 7 is the last to be completed, and the total completion time is 334.62 ms. The time is 389.92ms. The scheduling algorithm of the present invention is not as good as the classic optimal time scheduling algorithm in terms of completion time, but it is also relatively close.

It can be seen from Figure 5 that the satisfaction of most tasks and the total user satisfaction in the cloud task scheduling algorithm based on user satisfaction are better than the optimal completion time scheduling algorithm. This result reflects the validity of the algorithm designed by the present invention.

It can be seen from Figure 6 that the execution cost of most tasks in the cloud task scheduling algorithm based on user satisfaction is lower than that of the optimal completion time scheduling algorithm, and the total system cost of the cloud task scheduling algorithm based on user satisfaction is 25587 units , the total system cost of the optimal completion time scheduling algorithm is 30432 units. This result reflects that the cloud task scheduling algorithm based on user satisfaction has advantages in cost saving.

Through the analysis of the above simulation results, compared with the optimal completion time task scheduling algorithm, the cloud task scheduling algorithm based on user satisfaction can better meet the needs of different users and improve user satisfaction while ensuring a good task completion time. Satisfaction, but also can effectively save implementation costs.

Innovation of the present invention

1) Design a cloud task scheduling algorithm from the perspective of user satisfaction with cloud computing resources.

2) The scheduling algorithm can better satisfy users with different needs through a dynamic task allocation strategy under the condition of ensuring the completion time of the task, and can obtain good user satisfaction and overall system satisfaction, which can effectively save System implementation costs.

Claims (1)

1., based on a cloud task scheduling algorithm for user satisfaction, it is characterized in that, comprise the steps:

Step 1, resource parameters normalization

The performance parameter of task and virtual machine is all normalized to [0,1] interval, order set X _ij={ X _1j..., X _tjj is the number of performance parameter, be the set of the similar performance parameter of virtual machine, its normalized value is:

GX _ij＝(curX _ij-minX _ij)/(maxX _ij-minX _ij) (5)

Wherein, i is the number of task, and j is the number of performance parameter, curX _ijfor performance currency, minX _ijfor the minimum value in similar performance parameter sets, maxX _ijfor the maximum in similar performance parameter sets.

Step 2, calculates Euclidean distance

Be X={X at the parameter vector of virtual machine after parameter normalization process ₁, X ₂, X ₃, the parameter vector of cloud task is Y={Y ₁, Y ₂, Y ₃; Consider CPU, internal memory and bandwidth three performance parameters, obtain weight vectors W={W according to the type of cloud task ₁, W ₂, W ₃, then the computing formula of Euclidean distance is:

$D_i=\sqrt{{\mathrm{\&Sigma;}}_{j=1}^3{W}_j*{(X_j-Y_j)}^2}---\left(6\right)$

Wherein X _jrepresent the normalized parameter value of a jth resource in i-th virtual machine; Y _jexpression task is to the expected value of jth kind resource; W _jrepresent the weight of jth kind resource;

Step 3, selects resource

Each task choosing and the minimum virtual machines performing tasks of its Euclidean distance, the method controlling idle virtual machine is adopted to carry out load balance, each virtual machine safeguards an Euclidean distance table, after certain task is successfully assigned to certain virtual machine execution, need to upgrade Euclidean distance table, increase the Euclidean distance between certain class resource and this virtual machine, more new formula is:

D _i'=D _i(1+1/n), n is the number (7) of virtual machine

Step 4, calculates user satisfaction

After task completes, consider the performance of each task, comprise the deadline of task and the user satisfaction of each task, and the comprehensive satisfaction of all tasks; The user satisfaction of individual task is:

$s_i={\mathrm{\&Sigma;}}_{j=1}^3{W}_j\ln ({\mathrm{act}}_j/{\exp }_j)---\left(8\right)$

Wherein, s _ifor the user satisfaction of task i; W _jfor the weight of jth item performance parameter; act _jfor task is to the actual consumption of jth item performance parameter; exp _jfor cloud task is to user's expected value of jth item performance;

When 0≤| s _i| when≤0.5, then think that the Resourse Distribute of user to cloud task i is felt quite pleased; Work as 0.5<|s _i| when≤1, then think that the Resourse Distribute of user to cloud task i is satisfied; When | s _i| during >1, then think that user is unsatisfied with the Resourse Distribute of cloud task i; When | s _i| value very large time, then think that user is very dissatisfied to the Resourse Distribute of cloud task i;

User's comprehensive satisfaction of all cloud tasks is:

$S={\mathrm{\&Sigma;}}_{i=1}^t\left|s_i\right|---\left(9\right)$

Wherein s _iit is the user satisfaction of i-th task; T is the number of all cloud tasks;

Step 5, assesses the cost

After executing each cloud task, calculate spent cost of executing the task; Virtual machine according to unit to resource charging, task consumption full payment cost _ifor:

${\cos t}_i=C_{\mathrm{cpu}/\mathrm{num}}*P_1+C_{\frac{\mathrm{mem}}{\mathrm{MB}}}*P_2+C_{\mathrm{bw}/\mathrm{Mbps}}*P_3---\left(10\right)$

Wherein, P _ifor resource quantity, C is unit resource price;

After performing all cloud tasks, the total cost of system is:

$C={\mathrm{\&Sigma;}}_{i=1}^t{\cos t}_i---\left(11\right)$

Wherein cost _iit is the cost of i-th task; T is the number of all cloud tasks.