Piyush Shivam
ABSTRACT
Utility computing delivers compute and storage resources to applications as an 'on-demand utility', much like electricity, from a distributed collection of computing resources. There is great interest in running database applications on utility resources (e.g., Oracle's Grid initiative) due to reduced infrastructure and management costs, higher resource utilization, and the ability to handle sudden load surges. Virtual Machine (VM) technology offers powerful mechanisms to manage a utility resource infrastructure. However, provisioning VMs for applications to meet system performance goals, e.g., to meet service level agreements (SLAs), is an open problem. We are building two systems at Duke - Shirako and NIMO - that collectively address this problem.
Shirako is a toolkit for leasing VMs to an application from a utility resource infrastructure. NIMO learns application performance models using novel techniques based on active learning, and uses these models to guide VM provisioning in Shirako. We will demonstrate: (a) how NIMO learns performance models in an online and automatic fashion using active learning; and (b) how NIMO uses these models to do automated and on-demand provisioning of VMs in Shirako for two classes of database applications - multi-tier web services and computational science workflows.
- D. Irwin, J. S. Chase, L. Grit, A. Yumerefendi, D. Becker, and K. G. Yocum. Sharing Networked Resources with Brokered Leases. In Proceedings of the USENIX Annual Technical Conference, Jun 2006. Google Scholar
Digital Library
- Network/Internet Computing Lab. http://www.cs.duke.edu/nicl.Google Scholar
- L. Ramakrishnan, L. Grit, A. Iamnitchi, D. Irwin, A. Yumerefendi, and J. Chase. Toward a Doctrine of Containment: Grid Hosting with Adaptive Resource Control. In Proceedings of ACM Conference on Supercomputing (SC), Nov 2006. Google ScholarDigital Library
- P. Shivam, S. Babu, and J. Chase. Active and Accelerated Learning of Cost Models for Optimizing Scientific Applications. In Proceedings of International Conference on Very Large Data Bases (VLDB), Sep 2006. Google ScholarDigital Library
- P. Shivam, S. Babu, and J. Chase. Active Sampling for Accelerated Learning of Performance Models. In Workshop on Tackling Computer Systems Problems with Machine Learning Techniques (SysML), Jun 2006.Google Scholar
Index Terms
- Automated and on-demand provisioning of virtual machines for database applications
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