Multicloud Deployment of Computing Clusters for Loosely Coupled MTC Applications

Cloud computing is gaining acceptance in many IT organizations, as an elastic, flexible, and variable-cost way to deploy their service platforms using outsourced resources. Unlike traditional utilities where a single provider scheme is a common practice, the ubiquitous access to cloud resources easily enables the simultaneous use of different clouds. In this paper, we explore this scenario to deploy a computing cluster on the top of a multicloud infrastructure, for solving loosely coupled Many-Task Computing (MTC) applications. In this way, the cluster nodes can be provisioned with resources from different clouds to improve the cost effectiveness of the deployment, or to implement high-availability strategies. We prove the viability of this kind of solutions by evaluating the scalability, performance, and cost of different configurations of a Sun Grid Engine cluster, deployed on a multicloud infrastructure spanning a local data center and three different cloud sites: Amazon EC2 Europe, Amazon EC2 US, and ElasticHosts. Although the testbed deployed in this work is limited to a reduced number of computing resources (due to hardware and budget limitations), we have complemented our analysis with a simulated infrastructure model, which includes a larger number of resources, and runs larger problem sizes. Data obtained by simulation show that performance and cost results can be extrapolated to large-scale problems and cluster infrastructures.

1 Introduction

Many-TaskComputing (MTC) paradigm [1] embraces different types of high-performance applications involving many different tasks, and requiring large number of computational resources over short periods of time. These tasks can be of very different nature, with sizes from small to large, loosely coupled or tightly coupled, or compute-intensive or data-intensive.