Designing Flexible Resource Rental Models for Implementing HPC-as-a-Service in Cloud


Due to its ability of delivering virtually unlimited computing power as a metered service, the emerging cloud computing paradigm becomes an attractive option for scientific communities to experiment large-scale resource-demanding applications traditionally deployed in high performance computing (HPC) centers. Planning resource rental is difficult in cloud environment because various parties may have fundamentally different interests. To accommodate such diverse heterogeneity, designing flexible resource rental models for multiple parties in the cloud market becomes a challenging issue. In this study, we investigate the problem of designing flexible resource rental models for implementing HPC-as-a-Service in cloud market. First, from the perspective of resource customers, we present cost-effective resource rental planning models to better utilize on-demand and spot resources in cloud. Next, we approach the problem from the perspective of resource providers, and propose a novel service scheduling model for a multi-tenant cloud resource sharing platform. The remaining work to complete the PhD dissertation is then presented. The proposed research highlights flexibility for resource rental planning in the following two aspects. (1) Flexibility for resource customers ensures them to deploy elastic HPC applications taking most advantage of both fixed-price on-demand resources and dynamic spot resources. (2) Flexibility for resource providers accommodates differentiated service requirements from various customers and achieves efficient HPC service scheduling. We believe that the proposed work presented in this paper offers a novel means to address the need for flexible resource rental models in cloud, and sheds light on future research in the broad area of resource management in cloud computing.

DOI: 10.1109/IPDPSW.2012.324

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@article{Zhao2012DesigningFR, title={Designing Flexible Resource Rental Models for Implementing HPC-as-a-Service in Cloud}, author={Han Zhao and Xiaolin Li}, journal={2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum}, year={2012}, pages={2550-2553} }