Cloud computing is a solution for addressing challenges such as licensing, distribution, configuration, and operation of enterprise applications associated with the traditional IT infrastructure, software sales and deployment models. Migrating from a traditional model to the Cloud model reduces the maintenance complexity and cost for enterprise customers, and provides on-going revenue for Software as a Service (SaaS) providers. Clients and SaaS providers need to establish a Service Level Agreement (SLA) to define the Quality of Service (QoS). The main objectives of SaaS providers are to minimize cost and to improve Customer Satisfaction Level (CSL). In this paper, we propose customer driven SLA-based resource provisioning algorithms to minimize cost by minimizing resource and penalty cost and improve CSL by minimizing SLA violations. The proposed provisioning algorithms consider customer profiles and providers’ quality parameters (e.g. response time) to handle dynamic customer requests and infrastructure level heterogeneity for enterprise systems. We also take into account customer-side parameters (such as the proportion of upgrade requests), and infrastructure level parameters (such as the service initiation time) to compare algorithms. Simulation results show that our algorithms reduce the total cost up to 54% and the number of SLA violations up to 45%, compared with the previously proposed best algorithm.

Item Type:	Refereed Article
Keywords:	cloud computing, service level agreement, resource allocation, scheduling, software as a service, customer-driven, key performance indicator, resource provisioning
Research Division:	Information and Computing Sciences
Research Group:	Distributed Computing
Research Field:	Distributed and Grid Systems
Objective Division:	Information and Communication Services
Objective Group:	Computer Software and Services
Objective Field:	Computer Time Leasing, Sharing and Renting Services
Author:	Garg, SK (Dr Saurabh Garg)
ID Code:	93811
Year Published:	2014
Web of Science® Times Cited:	23
Deposited By:	Information and Communication Technology
Deposited On:	2014-08-19
Last Modified:	2017-11-18
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