Abstract
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 multi-objective 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.
Highlights
Many failures in online services are due to their inability to scale to meet user demands [1]
RQ2: Does the shared-mode resource allocation affect the effective execution of each load testing task?
1) RQ1: COMPARISON WITH THE EXCLUSIVE-MODE RESOURCE ALLOCATION For RQ1, we evaluate the economy of resource utilization from the aspects of the resource redundancy, the test execution cost, the network communication cost, and the number of allocated virtual and physical machines
Summary
Many failures in online services are due to their inability to scale to meet user demands [1]. The virtual machines in the cloud are used in a coarse-grained manner, and a virtual machine can simultaneously provide test services for only one single load testing task. The method takes minimizing resource redundancy, test execution cost, and network communication cost as the optimization objectives of resource allocation It regards ensuring enough virtual machine resources for test tasks as a basic constraint. With these objectives and constraints, the amount and costs of the used resources can possibly be reduced, and the load testing tasks can be effectively executed. Our experimental results show that for the tested cloud environments and load testing tasks, the shared-mode resource allocation method performs better than the exclusive-mode one in terms of the resource utilization efficiency.
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