Tradeoffs Between Profit and Customer Satisfaction for Service Provisioning in the Cloud

  • Authors:

  • Junliang Chen;Chen Wang;Bing Bing Zhou;Lei Sun;Young Choon Lee;Albert Y. Zomaya

  • Affiliations:

  • The University of Sydney & National ICT Australia Limited, Sydney, Australia;CSIRO ICT Center, Sydney, Australia;The University of Sydney, Sydney, Australia;China University of Mining and Technology, Xuzhou, China;The University of Sydney, Sydney, Australia;The University of Sydney, Sydney, Australia

  • Venue:
  • Proceedings of the 20th international symposium on High performance distributed computing
  • Year:
  • 2011

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Abstract

The recent cloud computing paradigm represents a trend of moving business applications to platforms run by parties located in different administrative domains. A cloud platform is often highly scalable and cost-effective through its pay-as-you-go pricing model. However, being shared by a large number of users, the running of applications in the platform faces higher performance uncertainty compared to a dedicated platform. Existing Service Level Agreements (SLAs) cannot sufficiently address the performance variation issue. In this paper, we use utility theory leveraged from economics and develop a new utility model for measuring customer satisfaction in the cloud. Based on the utility model, we design a mechanism to support utility-based SLAs in order to balance the performance of applications and the cost of running them. We consider an infrastructure-as-a-service type cloud platform (e.g., Amazon EC2), where a business service provider leases virtual machine (VM) instances with spot prices from the cloud and gains revenue by serving its customers. Particularly, we investigate the interaction of service profit and customer satisfaction. In addition, we present two scheduling algorithms that can effectively bid for different types of VM instances to make tradeoffs between profit and customer satisfaction. We conduct extensive simulations based on the performance data of different types of Amazon EC2 instances and their price history. Our experimental results demonstrate that the algorithms perform well across the metrics of profit, customer satisfaction and instance utilization.