Multi-interface-based self-adaptive cloud service test method

A test method and self-adaptive technology, applied in the direction of digital transmission system, electrical components, transmission system, etc., can solve the problems of inability to handle tasks and low processing efficiency, and achieve the goal of solving large tasks, improving efficiency, and satisfying individualization effect of demand

Inactive Publication Date: 2013-12-25

NANJING UNIV OF POSTS & TELECOMM

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First of all, for a large number of tasks, due to their different attributes, the resource requests for the cloud platform are different. If the tasks are not processed, but these test ...

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Abstract

The invention relates to a multi-interface-based self-adaptive cloud service test method. A software test provided by a cloud platform is in a novel test mode which is different from that of common software tests. The test method is mainly characterized in that an online environment is provided on the cloud platform to concurrently and simultaneously process massive task sets and return test results to a client through a network. The method has the main purposes that in the whole process, classified user tasks are mainly uploaded to save the clustering time of the cloud platform according to client requirements and to divide clustering tasks at a small granularity grade and reasonably dispatch the tasks, so that the cloud platform can reasonably distribute resources to balance loads so as to realize the target of improving the cloud test efficiency to the maximum extent.

Application Domain

Data switching networks

Technology Topic

Self adaptiveReal-time computing +6

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[0090] The present invention is an adaptive cloud service testing method based on multiple interfaces. The first is the development of the multiple interface upload interface, the second is the division of the interface task set, and finally the cluster node dynamically executes the test task. Specific steps are as follows:

[0091] Step 1) Development of multi-interface upload interface, the specific process is as follows figure 2 Shown:

[0092] Step 1.1) For a specific cloud platform, obtain information about all virtual machines that the current cloud service platform can provide;

[0093] Step 1.2) Filter the cloud platform information. Here, the cloud platform resources are combined into multiple resources from the type of operating system (OS) of the virtual machine provided by the cloud platform, the type of Internet (WEB) server and the type of database according to the needs of the test. Assuming that there are n1, n2, and n3 types of OS type, WEB server type, and database type, after the combination, n1×n2×n3 types of resources will be formed; in addition, there are also cloud platforms that do not include the above three types. Types of virtual machine resources, treat these resources as fuzzy type resources;

[0094] Step 1.3) Map all types of resources to the corresponding interface of the client. Here, the corresponding interface is generated according to the resource type generated in step 1.2). For fuzzy resources, it is used as a fuzzy interface. You can choose this interface when you need to configure the environment for the task. Users only need to submit the test requirements to the cloud test platform;

[0095] Step 1.4) Count the usage of resources under each interface. If the preference value of a certain type of resource reaches a certain value, send the information of this type of resource to the cloud control center, and the cloud control center will determine whether there are unclassified current cloud platforms If there are virtual machine resources, go to step 1.5), otherwise, end the increase of dynamic interfaces; if it does not reach a certain value, keep statistics on resource usage.

[0096] Step 1.5) The cloud control center obtains corresponding resource information from fuzzy resources according to the received information and instantiates a new interface;

[0097] Step 2) The dynamic division of each interface task set, see the specific process image 3 Said:

[0098] Step 2.1) Obtain the task set list from each interface, the tasks of each interface are obtained from the client, so that the tasks uploaded by each interface can find the corresponding resources to process;

[0099] Step 2.2) Calculate the size of each task in the task list;

[0100] Step 2.3) Count the size of each task, calculate the sum of the size of all tasks, and then calculate the average of all tasks according to the number of tasks avg :

[0101]

[0102] among them, L Indicates the number of tasks; l[i] Represents the first i The size of each task;

[0103] Step 2.4) Take the average of all tasks avg As a threshold for judging the size of the task threshold , which is threshold=avg;

[0104] Step 2.5) Count the number of tasks whose test tasks are greater than the threshold and less than the threshold in the task concentration, denoted as Ma and Mi respectively;

[0105] Step 2.6) When Mi is greater than Ma, the maximum and minimum (Max-min) algorithm is used for priority division, otherwise, the minimum and minimum (Min-min) algorithm is used for division; the switching of this algorithm can make the task execution more reasonable. This can save time and improve the efficiency of testing;

[0106] Step 2.7) Calculate the load of each cluster in the resource pool under the current task interface, and calculate the average load of all clusters avgc :

[0107]

[0108] among them, n Indicates the number of clusters; usedc[i] Represents the first i The load value of each cluster;

[0109] Step 2.8) Filter out all clusters whose load is less than the average value and arrange them in ascending order according to the size of the load, and then schedule the tasks that have been prioritized in step 2.6) in descending order to the arranged clusters and schedule them reasonably;

[0110] Step 2.9) Calculate the average load of the cluster at regular t, that is, calculate the average load of the current cluster every time t. If the average load is greater than the maximum load of the cluster, end the process, otherwise, go to step 2.7);

[0111] Step 3) Cluster nodes dynamically execute test tasks, the specific process is as follows Figure 4 Said:

[0112] Step 3.1) Obtain the task set of the cluster in step 2);

[0113] Step 3.2) First judge whether the task set is empty, if it is empty, end the execution of the task, otherwise go to step 3.3);

[0114] Step 3.3) Traverse the idle resources of each node in the resource pool under the cluster, and count the unused resources of each node, that is, the idle value of the node;

[0115] Step 3.4) Calculate the average idle value of all node resources under the cluster avgr :

[0116]

[0117] among them, r Indicates the number of resources; res[i] Represents the first i +1 idle value of resources; avgr Indicates the average value of idle resources of all nodes;

[0118] Step 3.5) Select all nodes whose resource idle value is greater than the average idle value;

[0119] Step 3.6) Count the priority of each task in the task cluster and schedule it to the node selected in step 3.5) in descending order;

[0120] Step 3.7) Until the idle condition of each node reaches the average value, go to step 3.3). If it does not reach the average value, it will continue to schedule tasks to the corresponding node, that is, return to step 3.6);

[0121] Step 4) Collect the test results and return to the client.

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Multi-interface-based self-adaptive cloud service test method

AI-Extracted Technical Summary

Problems solved by technology

Abstract

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Examples

Example Embodiment

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Description & Claims & Application Information

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