SaaS multitenant performance testing over social networks

Abstract

Recent years, cloud computing is description for facilitating suitable on-demand network access. In cloud, computing multi-tenancy plays a significant role on software as a service (SaaS). Structure of SaaS multi-tenant cloud aware applications initiates several new challenges the central one being a tenant. In cloud testing, tenant applications need to be tested in addition to performance testing be used. At the same time as numerous performance-testing techniques exist; most of them produce only fixed progressions of test configurations. This paper focuses on the challenges for Multi-tenancy testing in SaaS and analyses the configuration dynamically in which SaaS testing differs from testing conventional applications. The paper proposes performance testing and fittest function of each tenant. For fitness function, GASE algorithm is used which combines a genetic algorithm and a symbolic execution. This proposed algorithm uses the above performance testing values for obtaining the best of each tenant, in the form of fitness generations. A real experimentation is proposed using a multiple tenants on open stack cloud computing environment over social networks.