A Framework for Dynamic Resource Provisioning and Adaptation in IaaS Clouds
- 1 November 2011
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 312-319
- https://doi.org/10.1109/cloudcom.2011.49
Abstract
Infrastructure-as-a-Service (IaaS) cloud computing provides the ability to dynamically acquire extra or release existing computing resources on-demand to adapt to dynamic application workloads. In this paper, we propose an extensible framework for on-demand cloud resource provisioning and adaptation. The core of the framework is a set of resource adaptation algorithms that are capable of making informed provisioning decisions to adapt to workload fluctuations. The framework is designed to manage multiple sets of resources acquired from different cloud providers, and to interact with different local resource managers. We have developed a fully functional web-service based prototype of this framework, and used it for performance evaluation of various resource adaptation algorithms under different realistic settings, e.g. when input data such as jobs' wall times are inaccurate. Extensive experiments have been conducted with both synthetic and real workload traces obtained from the Grid Workload Archives, more specifically the traces from the Large Hadron Collider Computing Grid. The results demonstrate the effectiveness and robustness of our proposed algorithms.Keywords
This publication has 6 references indexed in Scilit:
- Cost-Wait Trade-Offs in Client-Side Resource Provisioning with Elastic CloudsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Dynamically scaling applications in the cloudACM SIGCOMM Computer Communication Review, 2011
- Multicloud Deployment of Computing Clusters for Loosely Coupled MTC ApplicationsIEEE Transactions on Parallel and Distributed Systems, 2010
- Elastic Site: Using Clouds to Elastically Extend Site ResourcesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- Evaluating the cost-benefit of using cloud computing to extend the capacity of clustersPublished by Association for Computing Machinery (ACM) ,2009
- The Grid Workloads ArchiveFuture Generation Computer Systems, 2008