GENERATING DIFFERENT WORKLOAD TYPES FOR CLOUD SERVICE TESTING

• US 20190087301A1
• Filed: 09/15/2017
• Published: 03/21/2019
• Est. Priority Date: 09/15/2017
• Status: Active Grant

• Current Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Original Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Inventors: M, Siva Subramaniam, S, Thavamaniraja, Teeparti, Rajesh, Ganesan, Vinnarasu, Vasipalli, SaiKrishna Reddy, Vuppuluri, Brahmanand
• Granted Patent: US 10,838,840 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 11/3006 where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems multiprogramming arrangements G06F9/46; allocation of resources G06F9/50
• CPC Class Codes: G06F 11/3414 Workload generation, e.g. scripts, playback
• CPC Class Codes: G06F 11/3433 for load management allocation of a server based on load conditions G06F9/505; load rebalancing G06F9/5083; redistributing the load in a network by a load balancer H04L67/1029
• CPC Class Codes: G06F 11/3688 for test execution, e.g. scheduling of test suites
• CPC Class Codes: G06F 2009/45587 Isolation or security of virtual machine instances
• CPC Class Codes: G06F 2201/815 Virtual middleware or OS functionality using virtual machines to implement generic software techniques for error detection or fault masking G06F11/1484
• CPC Class Codes: G06F 9/455 Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
• CPC Class Codes: H04L 12/4633 Interconnection of networks using encapsulation techniques, e.g. tunneling
• CPC Class Codes: H04L 43/0817 by checking functioning
• CPC Class Codes: H04L 43/50 Testing arrangements
• CPC Class Codes: H04L 43/55 Testing of service level quality, e.g. simulating service usage
• CPC Class Codes: H04L 63/20 for managing network security; network security policies in general filtering policies H04L63/0227
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/131 Protocols for games, networked simulations or virtual reality

Abstract

Example implementations relate to testing a cloud service. An example implementation includes creating cloud service resources. The resources may include a system node and a cloud application programming interface (API) for accessing the system node. Workloads of different types may be generated on the deployed cloud service according to workload parameters. In an example, the different workload types may include data workloads on the system nodes to consume computational resources of a computational resource pool assignable by the cloud service. In an example, the different workload types may include application programming interface calls to interface with cloud APIs of the cloud service.

Claims

1. A system for testing a cloud service comprising:

a physical processor; and

a memory storing machine readable instructions that, when executed by the physical processor, causes the processor to;

create cloud service resources according to scaling parameters, wherein the resources include system nodes, and

generate workloads of different types on the created resources according to workload parameters, the different workload types including;

data workloads on the system nodes to consume computational resources of a computational resource pool assignable by the cloud service, and

application programming interface calls to stress cloud application programming interfaces (APIs), wherein the cloud APIs enable access to the system nodes.

2. The system of claim 1, wherein the memory stores machine readable instructions that, when executed by the physical processor, causes the processor to monitor metrics of the system when the cloud service is under test.

3. The system of claim 2, further comprising a data processing server to collect the metrics for transfer to data storage.

4. The system of claim 3, wherein the memory stores machine readable instructions that, when executed by the physical processor, causes the processor to:

retrieve the data from the data processing server, and

generate a scalability report regarding the monitored metrics.

5. The system of claim 1, wherein the workload parameters include a frequency or volume at which API calls are generated.

6. The system of claim 1, wherein the workload parameters include a number of API calls to be generated upon satisfaction of a criterion.

7. The system of claim 1, wherein the workload parameters include a period of time during which API calls are generated.

8. The system of claim 1, wherein the scaling parameters include a start time for triggering a scale test for creating system resources and a stop time for ending the scale test.

9. The system of claim 1, wherein the scaling parameters and workload parameters are generated in coordination to simulate a use case.

10. A method for testing a cloud service comprising:

deploying, by a processor, a cloud service having system resources including a system node;

generating, by the processor, workloads of different types on the deployed cloud service according to workload parameters, the different workload types including;

data workloads on the system nodes to consume computational resources of a computational resource pool assignable by the cloud service, and

application programming interface (API) calls to interface with a cloud API of the cloud service for accessing the system node.

11. The method of claim 10, further comprising monitoring, by the processor, metrics of the cloud service when the cloud service is under test.

12. The method of claim 11, further comprising collecting, by a data processing server, the monitored metrics for transfer to data storage.

13. The method of claim 12, further comprising:

retrieving, by the processor, the data from the data processing server; and

generating, by the processor, a scalability report regarding the monitored metrics.

14. The method of claim 10, wherein the cloud service is deployed according to scaling parameters, the scaling parameters including a concurrency limit specifying, a maximum number of API calls that can be exercised on the cloud API of the cloud service at a particular time, or a maximum number of users that can access the cloud service at a particular time.

15. A non-transitory machine-readable storage medium comprising instructions executable by a processor for testing a cloud service, the machine-readable storage medium comprising:

instructions to cause the processor to scale the cloud service according to scaling parameters, the scaling to include creating system resources including a system node; and

instructions to cause the processor to generate workloads of different types on the created resources according to workload parameters, the different workload types including;

data workloads on the system node to consume computational resources of a computational resource pool assignable by the cloud service, and

application programming interface (API) calls to interface with a cloud API for accessing the system node of the cloud service.

16. The non-transitory machine-readable storage medium of claim 15, wherein the workload parameters include disk utilization, computer processing unit (CPU) utilization, memory utilization, or network congestion.

17. The non-transitory machine-readable storage medium of claim 15, wherein the workload parameters include a frequency or volume at which API calls are generated.

18. The non-transitory machine-readable medium of claim 15, wherein the scaling parameters include a start time for triggering a scale test for creating system resources and a stop time for ending the scale test.

19. The non-transitory machine-readable medium of claim 15, wherein the scaling parameters include a concurrency limit, a number of nodes to be created, or an amount of resources to be dedicated to a created node.

20. The non-transitory machine-readable medium of claim 15, wherein the scaling parameters and workload parameters are generated in coordination to simulate a use case.