Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Dhrystone: a synthetic systems programming benchmark
Communications of the ACM
Load Balancing of DNS-Based Distributed Web Server Systems with Page Caching
ICPADS '04 Proceedings of the Parallel and Distributed Systems, Tenth International Conference
Diagnosing performance overheads in the xen virtual machine environment
Proceedings of the 1st ACM/USENIX international conference on Virtual execution environments
Theory, Volume 1, Queueing Systems
Theory, Volume 1, Queueing Systems
Fast transparent migration for virtual machines
ATEC '05 Proceedings of the annual conference on USENIX Annual Technical Conference
Live migration of virtual machines
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
Autonomous learning for efficient resource utilization of dynamic VM migration
Proceedings of the 22nd annual international conference on Supercomputing
Experimental study of virtual machine migration in support of reservation of cluster resources
VTDC '07 Proceedings of the 2nd international workshop on Virtualization technology in distributed computing
On Strategies for Dynamic Resource Management in Virtualized Server Environments
MASCOTS '07 Proceedings of the 2007 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
Virtual machine migration in self-managing virtualized server environments
ICACT'09 Proceedings of the 11th international conference on Advanced Communication Technology - Volume 3
Detecting application load imbalance on high end massively parallel systems
Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
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Virtualization has been shown to be an attractive path to increase overall system resource utilization. The use of live virtual machine (VM) migration has enabled more effective sharing of system resources across multiple physical servers, resulting in an increase in overall performance. Live VM migration can be used to load balance virtualized clusters. To drive live migration, we need to be able to measure the current load imbalance. Further, we also need to accurately predict the resulting load imbalance produced by any migration. In this paper we present a new metric that captures the load of the physical servers and is a function of the resident VMs. This metric will be used to measure load imbalance and construct a load-balancing VM migration framework. The algorithm for balancing the load of virtualized enterprise servers follows a greedy approach, inductively predicting which VM migration will yield the greatest improvement of the imbalance metric in a particular step. We compare our algorithm to the leading commercially available load balancing solution - VMware's Distributed Resource Scheduler (DRS). Our results show that when we are able to accurately measure system imbalance, we can also predict future system state. We find that we can outperform DRS and improve performance up to 5%. Our results show that our approach does not impose additional performance impact and is comparable to the virtual machine monitor overhead.