Exploiting performance and cost diversity in the cloud

Infrastructure-as-a-Service (IaaS) platforms, such as Amazon EC2, allow clients access to massive computational power in the form of virtual machines (VMs) known as instances. Amazon hosts three different instance purchasing options, each with its own service level agreement covering availability and pricing. In addition, Amazon offers access to a number of geographical regions, zones, and instance types from which to select. In this paper, we present a resource allocation and job scheduling framework (RAMC-DC), which utilizes Amazon's rich selection of service offerings-particularly within Spot and On-Demand instance purchasing options-aiming to cost efficiently execute deadline-constrained jobs. The framework is capable of ensuring quality of service in terms of cost, deadline compliance and service reliability. Such capacities are realized incorporating a set of novel strategies including execution time and cost approximation, bidding and resource allocation strategies. To the best of our knowledge, RAMC-DC most extensively exploits the service diversity of Amazon EC2, and offers a comprehensive cost efficiency solution that is able to deliver both the performance and reliability of On-Demand instances and the low costs of Spot instances. Experimental results obtained from extensive simulations using Amazon's Spot price traces show that our approach keeps deadline breaches and early-termination rates as low as 0.47% and 0.18%, respectively. This reliable performance is achieved with total costs between 13% and 20% of an equivalent approach using only On-Demand instances.