Enhancing Elasticity of SaaS Applications using Queuing Theory

TL;DR

This paper proposes a queuing theory-based model to improve the elasticity of SaaS applications by considering service dependencies, leading to more accurate resource scaling and SLA compliance.

Contribution

It introduces a multi-class M/M/m queuing model that accounts for functional dependencies in SaaS elasticity, which is a novel approach.

Findings

Model accurately estimates required resources during scaling.

Incorporating dependencies improves SLA adherence.

Experimental results validate the model's effectiveness.

Abstract

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.