Computer Science – Distributed – Parallel – and Cluster Computing
Scientific paper
2010-03-05
Proceedings of 2nd Workshop on Many-Task Computing on Grids and Supercomputers, Co-located with ACM/IEEE SC 2009
Computer Science
Distributed, Parallel, and Cluster Computing
Scientific paper
In this paper, we intend to answer one key question to the success of cloud computing: in cloud, do many task computing (MTC) or high throughput computing (HTC) service providers, which offer the corresponding computing service to end users, benefit from the economies of scale? Our research contributions are three-fold: first, we propose an innovative usage model, called dynamic service provision (DSP) model, for MTC or HTC service providers. In the DSP model, the resource provider provides the service of creating and managing runtime environments for MTC or HTC service providers, and consolidates heterogeneous MTC or HTC workloads on the cloud platform; second, according to the DSP model, we design and implement DawningCloud, which provides automatic management for heterogeneous workloads; third, a comprehensive evaluation of DawningCloud has been performed in an emulatation experiment. We found that for typical workloads, in comparison with the previous two cloud solutions, DawningCloud saves the resource consumption maximally by 46.4% (HTC) and 74.9% (MTC) for the service providers, and saves the total resource consumption maximally by 29.7% for the resource provider. At the same time, comparing with the traditional solution that provides MTC or HTC services with dedicated systems, DawningCloud is more cost-effective. To this end, we conclude that for typical MTC and HTC workloads, on the cloud platform, MTC and HTC service providers and the resource provider can benefit from the economies of scale.
Liang Yi
Shi Weisong
Wang Lei
Yuan Lin
Zhan Jianfeng
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