Exploring portfolio scheduling for long-term execution of scientific workloads in IaaS clouds
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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Recent Infrastructure as a Service (IaaS) solutions, such as Amazon's EC2 cloud, provide virtualized on-demand computing resources on a pay-per-use model. From the user point of view, the cloud provides an inexhaustible supply of resources, which can be dynamically claimed and released. In the context of independent tasks, the main pricing model of EC2 promises two exciting features that drastically change the problem of resource provisioning and job scheduling. We call them \emph{free elasticity} and \emph{free CPU power}. Indeed, the price of CPU cycles is constant whatever the type of CPU and the amount of resources leased. Consequently, as soon as a user is able to keep its resources busy, the cost of one computation is the same using a lot of powerful resources or few slow ones. In this article, we study if these features can be exploited to execute bags of tasks, and what efforts are required to reach this goal. Efforts might be put on implementation, with complex provisioning and scheduling strategies, and in terms of performance, with the acceptance of execution delays. Using real workloads, we show that: (1) Most of the users can benefit from free elasticity with few efforts, (2) Free CPU power is difficult to achieve, (3) Using adapted provisioning and scheduling strategies can improve the results for a significant number of users, And (4) the outcomes of these efforts is difficult to predict.