Queueing Systems: Theory and Applications - Polling models
Assigning a single server to inhomogeneous queues with switching costs
Theoretical Computer Science
Decision Theory: An Introduction to Dynamic Programming and Sequential Decisions
Decision Theory: An Introduction to Dynamic Programming and Sequential Decisions
Structural results for the control of queueing systems using event-based dynamic programming
Queueing Systems: Theory and Applications
Perfect simulation of index based routing queueing networks
ACM SIGMETRICS Performance Evaluation Review
A progressive multi-layer resource reconfiguration framework for time-shared grid systems
Future Generation Computer Systems
A mean field approach for optimization in particle systems and applications
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
Grid brokering for batch allocation using indexes
NET-COOP'07 Proceedings of the 1st EuroFGI international conference on Network control and optimization
Optimal dynamic server allocation in systems with on/off sources
EPEW'07 Proceedings of the 4th European performance engineering conference on Formal methods and stochastic models for performance evaluation
A mean field approach for optimization in discrete time
Discrete Event Dynamic Systems
Server allocation in grid systems with on/off sources
ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
A queuing network model for minimizing the total makespan of computational grids
Computers and Electrical Engineering
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We examine a system where the servers in a cluster may be switched dynamically and preemptively from one kind of work to another. The demand consists of M job types joining separate queues, with different arrival and service characteristics, and also different relative importance represented by appropriate holding costs. The switching of a server from queue i to queue j incurs a cost which may be monetary or may involve a period of unavailability. The optimal switching policy is obtained numerically by solving a dynamic programming equation. Two simple heuristic policies-one static and one dynamic-are evaluated by simulation and are compared to the optimal policy. The dynamic heuristic is shown to perform well over a range of parameters, including changes in demand.