Research on Cloud Test Resource Allocation Based on Improved Fuzzy Clustering PSO Algorithm

Kang, Weijie; Xiao, Jiyang; Kong, Xiaoruo

doi:10.1088/1742-6596/1176/2/022005

Cited by 1 publication

(2 citation statements)

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“…For the allocation of test resources in cloud environments, Kang et al [10] proposed a resource allocation method based on improved Particle Swarm Optimization (PSO) to allocate virtual machines for test tasks, which can improve the efficiency of cloud resource allocation. Lampe et al [11] presented a model for scheduling software tests on a Testingas-a-Service system.…”

Section: Related Workmentioning

confidence: 99%

“…The resource allocation that allocates client-side virtual machines for the simulation of workloads is essential to the effective execution of load testing tasks and the operation costs of cloud testing providers [9]. In the existing work, the techniques in [10]- [12] allocate resources for test tasks using an exclusive utilization mode of virtual machine resources, with one virtual machine occupied by at most a single test task. Commercial cloud testing services like Tencent WeTest [13] and Alibaba PTS [14] also adopt exclusive-mode virtual machine resource allocation in their test-script-driven load testing.…”

Section: Introductionmentioning

confidence: 99%

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Shared-Mode Resource Allocation for Cloud-Based Load Testing

2020

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Resource allocation is essential for cloud-based load testing. The existing techniques use coarse-grained resource allocation methods with an entire virtual machine occupied by a single test task for cloud-based load testing. The idle resources in a virtual machine are unable to be used by other load testing tasks. This may result in uneconomical use of test resources and increase test costs. To optimize the use of test resources, this paper presents a shared-mode resource allocation method for cloud-based load testing. The method shares client-side virtual machine resources among load testing tasks. It takes minimizing resource redundancy, test execution cost, and network communication cost as optimization objectives of resource allocation, with the assurance of enough test resources as a basic constraint. We introduce a multiobjective optimization algorithm to create an optimized resource allocation plan for load testing tasks within a time window. The experiments show that the proposed method can reduce resource demands for load testing and thereby save the test costs. INDEX TERMS cloud testing, load testing, test resource allocation, multi-objective optimization.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%