Railway information system performance test method and system based on cloud platform

Abstract

The invention discloses a method and a system for testing the performance of a railway information system based on a cloud platform, which are applied to the field of cloud platform software testing, and comprise the following steps of: obtaining task processing time of a virtual processor based on a railway information system performance test task scheduling scheme of a cloud platform task scheduling model, and combining calculation parameters of the virtual processor, setting a fitness function; performing crossover and mutation on chromosomes by using an improved genetic algorithm to obtain a railway information system performance test task scheduling scheme under the optimal fitness value; and performing a railway information system performance test based on the railway information system performance test task scheduling scheme under the optimal fitness value, and dynamically adjusting the weight of each virtual processor by using the information entropy weight adjustment model according to the completion time proportion of the task on each virtual processor in the task execution process. According to the invention, the utilization rate and the execution efficiency of railway information system performance test resources in the cloud environment are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of cloud platform software testing, and in particular to a railway information system performance testing method and system based on a cloud platform. Background Art

[0002] In recent years, with the increasing scale and complexity of railway information system construction, evaluating system pressure bottlenecks through performance testing has become an indispensable measure to ensure software quality. Traditional performance testing solutions are limited by factors such as geography and hardware resources, and face problems such as high testing costs, low testing efficiency, and chaotic process quality management. Combining cloud computing technology with performance testing has brought new solutions to the above problems. Relying on the scalability and high flexibility of cloud computing, performance testing is directly provided to users as a service, realizing an efficient testing mode that transcends geographical restrictions and concentrates test resources. Despite this, performance testing in high-concurrency scenarios in cloud environments still has problems such as insufficient resource utilization and low execution efficiency.

[0003] Therefore, how to provide a railway information system performance testing method and system based on a cloud platform to effectively improve the resource utilization and execution efficiency of railway information system performance testing in a cloud environment is an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0004] In view of this, the present invention proposes a railway information system performance testing method and system based on a cloud platform.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A railway information system performance testing method based on a cloud platform, comprising:

[0007] Step 1: Based on the task scheduling scheme of the railway information system performance test of the cloud platform task scheduling model, the task processing time of the virtual processor is obtained, and the fitness function is set in combination with the calculation parameters of the virtual processor;

[0008] Step 2: Using the improved genetic algorithm to perform crossover and mutation on chromosomes, obtain the railway information system performance test task scheduling plan under the optimal fitness value;

[0009] Step 3: Perform railway information system performance test based on the railway information system performance test task scheduling plan under the optimal fitness value, and during the task execution process, dynamically adjust the weight of each virtual processor according to the completion time ratio of the task on each virtual processor using the information entropy weight adjustment model.

[0010] Optionally, in step 1, the cloud platform task scheduling model is as follows:

[0011] P=(T,E,S p ,ST,R,C);

[0012] Among them, P is the cloud platform task scheduling model; T is the set of task nodes; E is the set of directed edge matrices between task nodes; S p The resource matrix provided by the virtual processor; S T is the resource matrix required for the test task; R is the execution time matrix of the same test task on different virtual processors; C is the interval time matrix required for test task switching.

[0013] Optionally, in step 1, a fitness function is set based on the task processing time of the virtual processor and in combination with the computing parameters of the virtual processor, specifically:

[0014] Based on the task processing time and calculation parameters of the virtual processor, the load balance of the virtual processor is obtained; the calculation parameters include: the number of cores, the size of memory and the size of hard disk;

[0015] Set the fitness function based on task processing time and load balance.

[0016] Optionally, based on the task processing time of the virtual processor and the computing parameters of the virtual processor, the load balancing degree of the virtual processor is obtained, specifically:

[0017] Based on the task processing time and computing parameters of the virtual processor, the resource size and actual load provided by the virtual processor are obtained;

[0018] The load balance of the virtual processor is obtained according to the resource size and actual load provided by the virtual processor.

[0019] Optionally, based on the task processing time and computing parameters of the virtual processor, the resource size and actual load provided by the virtual processor are obtained, specifically:

[0020] Resource sizes are as follows:

[0021]

[0022] Among them, S j The size of resources provided for the jth virtual processor; m j is the number of time periods that the jth virtual processor divides in the overall processing time; C j,x for The number of computing cores provided by the jth virtual processor in the time period; M j,y for The memory size provided by the jth virtual processor in the time period; D j,Z for The size of the hard disk provided by the jth virtual processor in the time period; C j 、M j , D j are the number of computing cores, memory size, and hard disk size provided by the jth virtual processor; T j is the overall processing time for the jth virtual processor to complete the test task;

[0023] The actual load is as follows:

[0024]

[0025] Among them, S' j is the actual load of the jth virtual processor; m j ' is the number of time periods that the jth virtual processor divides in the actual processing time; C j ' ,x for The number of computing cores occupied by the jth virtual processor during the time period; M' j,y for The memory size occupied by the jth virtual processor in the time period; D' j,z for The hard disk size provided by the jth virtual processor in the time period; C' j 、M j '、D j ' are the number of computing cores, memory size, and hard disk size occupied by the jth virtual processor; T j ' is the actual processing time of the jth virtual processor to complete the test task.

[0026] Optionally, the load balance of the virtual processor is obtained according to the resource size and actual load provided by the virtual processor, as follows:

[0027]

[0028] Among them, B j is the load balancing degree of the jth virtual processor; m j ' is the number of time periods divided by the jth virtual processor in the actual processing time; is the average remaining resource size of the virtual processor; S j The resource size provided for the jth virtual processor; S' j is the actual load of the jth virtual processor; n is the number of virtual processors.

[0029] Optionally, set the fitness function based on task processing time and load balancing, as follows:

[0030]

[0031] Among them, F(k) is the fitness function; α and β are the actual total processing time of the test task and the weight of the load balancing degree; T' k is the actual total processing time of the test task; B k The load balancing degree of the virtual processor.

[0032] Optionally, in step 2, an improved genetic algorithm is used to perform crossover and mutation on chromosomes to obtain a railway information system performance test task scheduling scheme under an optimal fitness value, specifically:

[0033] The Min-Min algorithm is used to generate an initial population of a preset proportion, and random numbers are used to generate the remaining proportion of chromosomes. The 1st to nth test tasks are assigned to random virtual processors according to the task numbers. The probability of a chromosome being selected is as follows:

[0034]

[0035] Among them, F(k) is the fitness function; G is the number of chromosomes in the population; is the sum of the fitness of G chromosomes;

[0036] Select two chromosomes according to the set crossover probability, and exchange the corresponding gene sequences of the two chromosomes to generate new chromosome fragments;

[0037] Randomly select two values, select chromosomes according to the mutation probability, take out the chromosome fragments numbered between the two values, sort them in reverse order, and then re-inject them into the same position of the chromosome to form a new chromosome injection population, completing the chromosome mutation operation;

[0038] After the selection, crossover and mutation are completed, it means that an iteration process is completed. Finally, according to the iteration end condition, the railway information system performance test task scheduling plan with the optimal fitness value is obtained.

[0039] Optionally, in step 3, during the task execution process, the weight of each virtual processor is dynamically adjusted using an information entropy weight adjustment model according to the completion time ratio of the task on each virtual processor, specifically:

[0040] In the task k When the execution starts, record the task t k In each virtual processor R l The estimated completion time on each virtual processor R is calculated and recorded l The task completion time ratio P k,l , where P k,l For task t k The estimated completion time on the virtual processor accounts for the task tk The fraction of the total estimated completion time across all virtual processors;

[0041] In the task k After the task is completed, each virtual processor P is updated according to the actual task completion time. l The task completion time ratio P k,l , and use the following formula to calculate and update the virtual processor R l The weight W e :

[0042]

[0043] Where n is the number of virtual processors;

[0044] In subsequent task scheduling decisions, the cloud computing resources with the highest weight are selected to execute new tasks.

[0045] The present invention also provides a railway information system performance testing system based on a cloud platform using a railway information system performance testing method based on a cloud platform, comprising:

[0046] Fitness function setting module: used for the railway information system performance test task scheduling scheme based on the cloud platform task scheduling model, obtaining the task processing time of the virtual processor, and setting the fitness function in combination with the calculation parameters of the virtual processor;

[0047] Optimal task scheduling scheme acquisition module: used to obtain the railway information system performance test task scheduling scheme under the optimal fitness value by using the improved genetic algorithm to cross and mutate the chromosomes;

[0048] Task execution module: It is used to perform railway information system performance test based on the railway information system performance test task scheduling plan under the optimal fitness value, and during the task execution process, according to the completion time ratio of the task on each virtual processor, the weight of each virtual processor is dynamically adjusted using the information entropy weight adjustment model.

[0049] It can be seen from the above technical solutions that, compared with the prior art, the present invention proposes a railway information system performance test method and system based on a cloud platform. The load balance of the virtual processor is obtained based on the task processing time and calculation parameters of the virtual processor, and the fitness function is set based on the task processing time and load balance. The improved genetic algorithm crosses and mutates the chromosomes to obtain the railway information system performance test task scheduling plan under the optimal fitness value, and during the task execution process, according to the completion time ratio of the task on each virtual processor, the information entropy weight adjustment model is used to dynamically adjust the weight of each virtual processor, which effectively improves the resource utilization and execution efficiency of the railway information system performance test in the cloud environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0051] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Embodiment 1:

[0054] Embodiment 1 of the present invention discloses a railway information system performance testing method based on a cloud platform, such as Figure 1 As shown, including:

[0055] Step 1: Based on the task scheduling scheme of the railway information system performance test of the cloud platform task scheduling model, the task processing time of the virtual processor is obtained, and the fitness function is set in combination with the computing parameters of the virtual processor.

[0056] The cloud platform task scheduling model is as follows:

[0057] P=(T,E,S p ,S T ,R,C);

[0058] Among them, P is the cloud platform task scheduling model; T is the set of task nodes; E is the set of directed edge matrices between task nodes; S p The resource matrix provided by the virtual processor; S T is the resource matrix required for the test task; R is the execution time matrix of the same test task on different virtual processors; C is the interval time matrix required for test task switching.

[0059] Based on the task processing time of the virtual processor and combined with the calculation parameters of the virtual processor, the fitness function is set as follows:

[0060] Based on the task processing time and calculation parameters of the virtual processor, the load balance of the virtual processor is obtained; the calculation parameters include: the number of cores, the size of memory and the size of hard disk;

[0061] Set the fitness function based on task processing time and load balance.

[0062] Based on the task processing time of the virtual processor and the computing parameters of the virtual processor, the load balancing degree of the virtual processor is obtained, specifically:

[0063] Based on the task processing time and computing parameters of the virtual processor, the resource size and actual load provided by the virtual processor are obtained;

[0064] The load balance of the virtual processor is obtained according to the resource size and actual load provided by the virtual processor.

[0065] Based on the task processing time and computing parameters of the virtual processor, the resource size and actual load provided by the virtual processor are obtained, specifically:

[0066] Resource sizes are as follows:

[0067]

[0068] Among them, S j The size of resources provided for the jth virtual processor; m j is the number of time periods that the jth virtual processor divides in the overall processing time; C j,x for The number of computing cores provided by the jth virtual processor in the time period; M j,y for The memory size provided by the jth virtual processor in the time period; D j,z for The size of the hard disk provided by the jth virtual processor in the time period; C j 、M j , D jare the number of computing cores, memory size, and hard disk size provided by the jth virtual processor; T j is the overall processing time for the jth virtual processor to complete the test task;

[0069] The actual load is as follows:

[0070]

[0071] Among them, S' j is the actual load of the jth virtual processor; m j ' is the number of time periods that the jth virtual processor divides in the actual processing time; C j ' ,x for The number of computing cores occupied by the jth virtual processor during the time period; M' j,y for The memory size occupied by the jth virtual processor in the time period; D' j,z for The hard disk size provided by the jth virtual processor in the time period; C' j 、M j '、D j ' are the number of computing cores, memory size, and hard disk size occupied by the jth virtual processor; T j ' is the actual processing time of the jth virtual processor to complete the test task.

[0072] The load balance of the virtual processor is obtained according to the resource size and actual load provided by the virtual processor, as follows:

[0073]

[0074] Among them, B j is the load balancing degree of the jth virtual processor; m j ' is the number of time periods divided by the jth virtual processor in the actual processing time; is the average remaining resource size of the virtual processor; S j The resource size provided for the jth virtual processor; S' j is the actual load of the jth virtual processor; n is the number of virtual processors.

[0075] Based on the task processing time and load balance, set the fitness function as follows:

[0076]

[0077] Among them, F(k) is the fitness function; α and β are the actual total processing time of the test task and the weight of the load balancing degree; T' kis the actual total processing time of the test task; B k The load balancing degree of the virtual processor.

[0078] Step 2: Use the improved genetic algorithm to crossover and mutate chromosomes to obtain the railway information system performance test task scheduling plan with the optimal fitness value.

[0079] The improved genetic algorithm is used to cross and mutate chromosomes to obtain the railway information system performance test task scheduling plan under the optimal fitness value, specifically:

[0080] The Min-Min algorithm is used to generate an initial population of a preset proportion, and random numbers are used to generate the remaining proportion of chromosomes. The 1st to nth test tasks are assigned to random virtual processors according to the task numbers. The probability of a chromosome being selected is as follows:

[0081]

[0082] Among them, F(k) is the fitness function; G is the number of chromosomes in the population; is the sum of the fitness of G chromosomes;

[0083] Select two chromosomes according to the set crossover probability, and exchange the corresponding gene sequences of the two chromosomes to generate new chromosome fragments;

[0084] Randomly select two values, select chromosomes according to the mutation probability, take out the chromosome fragments numbered between the two values, sort them in reverse order, and then re-inject them into the same position of the chromosome to form a new chromosome injection population, completing the chromosome mutation operation;

[0085] After the selection, crossover and mutation are completed, it means that an iteration process is completed. Finally, according to the iteration end condition, the railway information system performance test task scheduling plan with the optimal fitness value is obtained.

[0086] Step 3: Perform railway information system performance test based on the railway information system performance test task scheduling plan under the optimal fitness value, and during the task execution process, dynamically adjust the weight of each virtual processor according to the completion time ratio of the task on each virtual processor using the information entropy weight adjustment model.

[0087] During the task execution process, the weight of each virtual processor is dynamically adjusted using the information entropy weight adjustment model according to the completion time ratio of the task on each virtual processor. Specifically:

[0088] In the task k When the execution starts, record the task t k In each virtual processor Rl The estimated completion time on each virtual processor R is calculated and recorded l The task completion time ratio P k,l , where P k,l For task t k The estimated completion time on the virtual processor accounts for the task t k The fraction of the total estimated completion time across all virtual processors;

[0089] In the task k After the task is completed, each virtual processor R is updated according to the actual task completion time. l The task completion time ratio P k,l , and use the following formula to calculate and update the virtual processor R l The weight W e :

[0090]

[0091] Where n is the number of virtual processors;

[0092] In subsequent task scheduling decisions, the cloud computing resources with the highest weight are selected to execute new tasks.

[0093] Embodiment 2:

[0094] Embodiment 2 of the present invention discloses a railway information system performance testing system based on a cloud platform using a railway information system performance testing method based on a cloud platform, comprising:

[0095] Fitness function setting module: used for the railway information system performance test task scheduling scheme based on the cloud platform task scheduling model, obtaining the task processing time of the virtual processor, and setting the fitness function in combination with the calculation parameters of the virtual processor;

[0096] Optimal task scheduling scheme acquisition module: used to obtain the railway information system performance test task scheduling scheme under the optimal fitness value by using the improved genetic algorithm to cross and mutate the chromosomes;

[0097] Task execution module: It is used to perform railway information system performance test based on the railway information system performance test task scheduling plan under the optimal fitness value, and during the task execution process, according to the completion time ratio of the task on each virtual processor, the weight of each virtual processor is dynamically adjusted using the information entropy weight adjustment model.

[0098] The embodiment of the present invention discloses a railway information system performance test method and system based on a cloud platform. The load balance of the virtual processor is obtained based on the task processing time and calculation parameters of the virtual processor, and the fitness function is set based on the task processing time and the load balance. The improved genetic algorithm is used to cross and mutate chromosomes to obtain a railway information system performance test task scheduling plan under the optimal fitness value, and during the task execution process, according to the completion time ratio of the task on each virtual processor, the weight of each virtual processor is dynamically adjusted using an information entropy weight adjustment model, which effectively improves the resource utilization and execution efficiency of the railway information system performance test in the cloud environment.

[0099] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0100] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A railway information system performance testing method based on a cloud platform, characterized in that: include: Step 1: Based on the task scheduling scheme of the railway information system performance test of the cloud platform task scheduling model, the task processing time of the virtual processor is obtained, and the fitness function is set in combination with the computing parameters of the virtual processor; Step 2: Using the improved genetic algorithm to perform crossover and mutation on chromosomes, obtain the railway information system performance test task scheduling plan under the optimal fitness value; Step 3: Perform railway information system performance test based on the railway information system performance test task scheduling plan under the optimal fitness value, and during the task execution process, dynamically adjust the weight of each virtual processor according to the completion time ratio of the task on each virtual processor using the information entropy weight adjustment model.

2. A railway information system performance testing method based on a cloud platform according to claim 1, characterized in that: In step 1, the cloud platform task scheduling model is as follows: P=(T,E,S p ,S T ,R,C); Among them, P is the cloud platform task scheduling model; T is the set of task nodes; E is the set of directed edge matrices between task nodes; S p The resource matrix provided by the virtual processor; S T is the resource matrix required for the test task; R is the execution time matrix of the same test task on different virtual processors; C is the interval time matrix required for test task switching.

3. A railway information system performance testing method based on a cloud platform according to claim 1, characterized in that: In step 1, based on the task processing time of the virtual processor and in combination with the computing parameters of the virtual processor, a fitness function is set, specifically: Obtaining a load balancing degree of the virtual processor based on the task processing time and computing parameters of the virtual processor; The computing parameters include: number of cores, memory size, and hard disk size; A fitness function is set based on the task processing time and the load balancing degree.

4. A railway information system performance testing method based on a cloud platform according to claim 3, characterized in that: Based on the task processing time of the virtual processor and the computing parameters of the virtual processor, the load balancing degree of the virtual processor is obtained, specifically: Based on the task processing time and computing parameters of the virtual processor, obtaining the resource size and actual load provided by the virtual processor; The load balance degree of the virtual processor is obtained according to the resource size and actual load provided by the virtual processor.

5. A railway information system performance testing method based on a cloud platform according to claim 4, characterized in that: Based on the task processing time and computing parameters of the virtual processor, the resource size and actual load provided by the virtual processor are obtained, specifically: The resource sizes are as follows: Among them, S j The resource size provided for the jth virtual processor; m j is the number of time periods divided by the jth virtual processor in the overall processing time; C j,x for The number of computing cores provided by the jth virtual processor in the time period; M j,y for The memory size provided by the jth virtual processor in the time period; D j,z for The hard disk size provided by the jth virtual processor in the time period; C j 、M j , D j are the number of computing cores, memory size, and hard disk size provided by the jth virtual processor respectively; T j is the overall processing time of the jth virtual processor to complete the test task; The actual load is as follows: Among them, S' j is the actual load of the jth virtual processor; m' j is the number of time periods divided by the jth virtual processor in the actual processing time; C' j,x for The number of computing cores occupied by the jth virtual processor in the time period; M' j,y for The memory size occupied by the jth virtual processor in the time period; D' j,z for The hard disk size provided by the jth virtual processor in the time period; C' j , M' j , D' j are the number of computing cores, memory size, and hard disk size occupied by the jth virtual processor; T' j is the actual processing time of the jth virtual processor to complete the test task.

6. A railway information system performance testing method based on a cloud platform according to claim 4, characterized in that: The load balance degree of the virtual processor is obtained according to the resource size and actual load provided by the virtual processor, as follows: Among them, B j is the load balancing degree of the jth virtual processor; m' j is the number of time periods into which the jth virtual processor divides its actual processing time; is the average remaining resource size of the virtual processor; j The resource size provided by the jth virtual processor; S' j is the actual load of the jth virtual processor; n is the number of the virtual processors.

7. A railway information system performance testing method based on a cloud platform according to claim 4, characterized in that: Based on the task processing time and the load balancing degree, a fitness function is set as follows: Where F(k) is the fitness function; α and β are the actual total processing time of the test task and the weight of the load balancing degree; T' k is the actual total processing time of the test task; B k is the load balancing degree of the virtual processor.

8. A railway information system performance testing method based on a cloud platform according to claim 1, characterized in that: In step 2, the improved genetic algorithm is used to cross and mutate chromosomes to obtain the railway information system performance test task scheduling plan under the optimal fitness value, specifically: The Min-Min algorithm is used to generate an initial population of a preset proportion, and random numbers are used to generate the remaining proportion of chromosomes. The 1st to nth test tasks are assigned to random virtual processors according to the task numbers. The probability of a chromosome being selected is as follows: Among them, F(k) is the fitness function; G is the number of chromosomes in the population; is the sum of the fitness of G chromosomes; Select two chromosomes according to the set crossover probability, and exchange the corresponding gene sequences of the two chromosomes to generate new chromosome fragments; Randomly select two values, select chromosomes according to the mutation probability, take out the chromosome fragments numbered between the two values, sort them in reverse order, and then re-inject them into the same position of the chromosome to form a new chromosome injection population, thus completing the chromosome mutation operation; After the selection, crossover and mutation are completed, it means that an iteration process is completed. Finally, according to the iteration end condition, the railway information system performance test task scheduling plan with the optimal fitness value is obtained.

9. A railway information system performance testing method based on a cloud platform according to claim 1, characterized in that: In step 3, during the task execution process, the weight of each virtual processor is dynamically adjusted using the information entropy weight adjustment model according to the completion time ratio of the task on each virtual processor, specifically: In the task k When the execution starts, record the task t k In each virtual processor R l The estimated completion time on each virtual processor R is calculated and recorded l The task completion time ratio P k,l , where P k,l For task t k The estimated completion time on the virtual processor accounts for the task t k The fraction of the total estimated completion time across all virtual processors; In the task k After the task is completed, each virtual processor R is updated according to the actual task completion time. l The task completion time ratio P k,l , and use the following formula to calculate and update the virtual processor R l The weight W e : Where n is the number of virtual processors; In subsequent task scheduling decisions, the cloud computing resources with the highest weight are selected to execute new tasks.

10. A railway information system performance test system based on a cloud platform using a railway information system performance test method based on a cloud platform according to any one of claims 1 to 9, characterized in that: include: Fitness function setting module: used for the railway information system performance test task scheduling scheme based on the cloud platform task scheduling model, obtaining the task processing time of the virtual processor, and setting the fitness function in combination with the calculation parameters of the virtual processor; Optimal task scheduling scheme acquisition module: used to obtain the railway information system performance test task scheduling scheme under the optimal fitness value by using the improved genetic algorithm to cross and mutate the chromosomes; Task execution module: It is used to perform railway information system performance test based on the railway information system performance test task scheduling plan under the optimal fitness value, and during the task execution process, according to the completion time ratio of the task on each virtual processor, the weight of each virtual processor is dynamically adjusted using the information entropy weight adjustment model.

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