On providing on-the-fly resizing of the elasticity grain when executing HPC applications in the cloud

Abstract

Today, we observe that cloud infrastructures are gaining more and more space to execute high-performance computing (HPC) applications. Unlike clusters and grids, the cloud offers elasticity, which refers to the ability to increase or reduce the number of resources (and consequently, processes) to support, as closely as possible, the needs at a particular moment of the execution. To the best of our knowledge, current initiatives explore the elasticity for HPC applications by always handling the same number of resources at each scaling-in or scaling-out operation. This fixed elasticity grain commonly results in a stair-shaped behaviour, where successive elasticity operations take place to address the load curve. In this context, this article presents GrainElastic, an elasticity model for executing HPC applications with the capacity to adapt the elasticity grain to the requirements of each elasticity operation. Its contribution involves a mathematical formalism that uses historical execution traces and the ARIMA time-series model to predict the required number of resources (in our case, VMs) to address a reconfiguration point. Based on the proposed model, we developed a prototype that was compared with two other scenarios: 1) non-elastic application; 2) elastic middleware with a fixed grain. The results presented gains of up to 30% using GrainElastic, which demonstrates the relevance of adapting the elasticity grain to enhance system reactivity and performance.