SODA: A Service-On-Demand Architecture for Application Service Hosting Utility Platforms
HPDC '03 Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing
A Case For Grid Computing On Virtual Machines
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
VSched: Mixing Batch And Interactive Virtual Machines Using Periodic Real-time Scheduling
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Towards virtual networks for virtual machine grid computing
VM'04 Proceedings of the 3rd conference on Virtual Machine Research And Technology Symposium - Volume 3
Increasing application performance in virtual environments through run-time inference and adaptation
HPDC '05 Proceedings of the High Performance Distributed Computing, 2005. HPDC-14. Proceedings. 14th IEEE International Symposium
Automatic dynamic run-time optical network reservations
HPDC '05 Proceedings of the High Performance Distributed Computing, 2005. HPDC-14. Proceedings. 14th IEEE International Symposium
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
Building an automated and self-configurable emulation testbed for grid applications
Software—Practice & Experience
Topology-aware VM migration in bandwidth oversubscribed datacenter networks
ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part II
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A virtual execution environment consisting of virtual machines (VMs) interconnected with virtual networks provides opportunities to dynamically optimize, at run-time, the performance of existing, unmodified distributed applications without any user or programmer intervention. Along with resource monitoring and inference and application-independent adaptation mechanisms, efficient adaptation algorithms are key to the success of such an effort. In previous work we have described our measurement and inference framework, explained our adaptation mechanisms, and proposed simple heuristics as adaptation algorithms. Though we were successful in improving performance as compared to the case with no adaptation, none of our algorithms were characterized by theoretically proven bounds. In this paper, we formalize the adaptation problem, show that it is NP-hard and propose research directions for coming up with an efficient solution.