Proceedings of the 2nd International Workshop on Software Engineering for Cloud Computing
Designing and Deploying a Scientific Computing Cloud Platform
GRID '12 Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing
Cost-Effective provisioning and scheduling of deadline-constrained applications in hybrid clouds
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Scheduling strategies for optimal service deployment across multiple clouds
Future Generation Computer Systems
A distributed framework for energy-efficient lightpaths in computational grids
Journal of High Speed Networks - Green Networking and Computing, Part 2 of 2
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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.