Enhancing Elasticity of SaaS Applications using Queuing Theory

Elasticity is one of key features of cloud computing. Elasticity allows Software as a Service (SaaS) applications’ provider to reduce cost of running applications. In large SaaS applications that are developed using service-oriented architecture model, each service is deployed in a separated virtual machine and may use one or more services to complete its task. Although, scaling service independently from its required services propagates scaling problem to other services, most of current elasticity approaches do not consider functional dependencies between services, which increases the probability of violating service level agreement. In this paper, architecture of SaaS application is modeled as multi-class M/M/m processor sharing queuing model with deadline to take into account functional dependencies between services during estimating required scaling resources. Experimental results show effectiveness of the proposed model in estimating required resources during scaling virtual resources. Keywords—auto-scaling; cloud computing; cloud resource scaling; queuing theory; resource provisioning; virtualized resources