Autonomic Resource Management of Virtualized Computing SystemsWith the rapid growth of computational power on compute servers, and the fast maturing of x86 virtualization technologies, Virtual Machines (VM) have become increasingly important to supporting efficient and flexible resource provisioning. Modern virtual machine technologies (e.g. Xen, VMware) allow a single physical server to be carved into multiple virtual resource containers, each delivering a powerful, secure, and isolated execution environment for applications. In addition to providing access to resources, such environments can be customized to encapsulate the entire software and hardware platform needed by the applications and support their seamless deployments.
The challenges to resource management of virtualized computing systems are how to effectively allocate resources to the VMs to meet their hosted applications’ Quality of Service (QoS) goals and how to efficiently manage the shared resources among many concurrent VMs hosting applications with different resource and QoS needs. To address these challenges, we are studying autonomic techniques to automatically manage VM resources according to applications’ resource demands and optimize the resource allocation according to high-level user-specified objectives for large virtualized computing systems.
A hierachical architecture is designed for VM-based resource management. A virtual resource manager provides centralized allocation of distributed virtualized resources, and a per-host VM scheduler manages its local VMs. Intelligent controllers are integrated into this architecture at different levels to provide optimized resource management. Fuzzy-logic based machine learning methods are used to model resource demands for VMs running changing workloads, and resources are allocated dynamically based on a profit-driven model. For applications requiring QoS guarantees, advance resource reservation are also provided for VMs. VM migration can be employed to vacate workloads and dedicate resources for VMs, and a model is developed to estimate the migration overhead for efficient scheduling of reservations.
Fuzzy-logic based Resource Control Architecture
Publication
- Jing Xu, Ming Zhao, José A. B. Fortes, Robert Carpenter, Mazin Yousif, “Autonomic Resource Management in Virtualized Data Centers using Fuzzy-logic-based Approaches”, Cluster Computing, Vol. 11, No. 3, Pages: 213-227, September 2008.
- M. Zhao and R. J. Figueiredo, “Experimental Study of Virtual Machine Migration in Support of Reservation of Cluster Resources,” Proceedings of 2nd International Workshop on Virtualization Technology in Distributed Computing (held in conjunction with SC07) (VTDC 2007), November 2007.
- J. Xu, M. Zhao, J. A. B. Fortes, R. Carpenter, and M. Yousif, “On the Use of Fuzzy Modeling in Virtualized Data Center Management,” Proceedings of 4th International Conference on Autonomic Computing (ICAC 2007), June 2007.