SLA-based admission control for a Software-as-a-Service provider in Cloud computing environments

• Published in: Journal of computer and system sciences Vol. 78; no. 5; pp. 1280 - 1299
• Main Authors: Wu, Linlin, Kumar Garg, Saurabh, Buyya, Rajkumar
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2012
• Subjects: Admission control; Algorithms; Cloud computing; Computer simulation; Cost engineering; Infrastructure; Internet; Mathematical models; Scalability of application services; Service Level Agreement (SLA); Software as a Service; Cloud computing; Service Level Agreement (SLA); Software as a Service; Admission control; Scalability of application services
• ISSN: 0022-0000; 1090-2724
• DOI: 10.1016/j.jcss.2011.12.014

Software as a Service (SaaS) provides access to applications to end users over the Internet without upfront investment in infrastructure and software. To serve their customers, SaaS providers utilise resources of internal data centres or rent resources from a public Infrastructure as a Service (IaaS) provider. In-house hosting can increase administration and maintenance costs whereas renting from an IaaS provider can impact the service quality due to its variable performance. To overcome these limitations, we propose innovative admission control and scheduling algorithms for SaaS providers to effectively utilise public Cloud resources to maximize profit by minimizing cost and improving customer satisfaction level. Furthermore, we conduct an extensive evaluation study to analyse which solution suits best in which scenario to maximize SaaS providerʼs profit. Simulation results show that our proposed algorithms provide substantial improvement (up to 40% cost saving) over reference ones across all ranges of variation in QoS parameters. ► The paper proposes a system and mathematical models for SaaS providers to satisfy customers by leasing Cloud resources from multiple IaaS providers. ► It proposes three innovative admission control and scheduling algorithms for profit maximization by minimizing cost and maximizing customer satisfaction level. ► It demonstrates effectiveness of the proposed models and algorithms through an extended evaluation study by varying customer and provider side parameters to analyze which solution suits best in which scenario to maximize SaaS providerʼs profit using actual IaaS data from Amazon and GoGrid.