Performance Testing in A Multi Tenant Cloud Architecture using Genetic Algorithm
Vishnu Shankar.S1, Sajidha.S.A2, Nisha.V.M3, Sathis kumar.B4
1Vishnu Shankar, GE Power, Bangalore, India.
2Sajidha.S.A*, Vellore Institute of Technology, Chennai, India.
3Nisha. V.M*, Vellore Institute of Technology, Chennai, India.
4Sathis kumar. B,Vellore Institute of Technology, Chennai, India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2840-2846 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3364129219/2019©BEIESP | DOI: 10.35940/ijeat.B3364.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Recent researches in cloud discusses about the application response testing, performance testing, security testing and many more, but still there is a lack of researches addressing issues like resource utilization and user interactions in cloud SaaS testing. The load on the cloud, SaaS instance keeps varying dynamically with respect to time, it is difficult to find the exact load at a particular interval of time. One does not know where to look for the solution and where to start, this made SaaS instances non deterministic in nature. In order to find a solution for such non deterministic problems, we make use of Genetic Algorithm which is considered as a good solution for non-deterministic problems.We determine the optimized resources that a cloud instance, would need to manage the dynamic load at all times. Toaddress the resource utilization of a group of users in MultiTenant Architecture (MTA), we adopt Genetic Algorithm which uses a popular technique, called neighborhood search and instance ranking policy. The basic concept of this paper is to explore the neighbors of an existing solution, that is considered as the solutions which can be obtained with a specific operation on the base population. In addition to that,this paper discusses about the ranking of all the available population and select the most highly ranked one. Instance ranking policies are aimed at minimizing the number of nodes in use or maximize the resources available to each node in an instance.
Keywords: Software-as-a-Service (SaaS), Virtual Machines, Cloud Multi Tenant Architecture (MTA), Genetic Algorithm (GA), Non-determinism.