Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Exploiting process lifetime distributions for dynamic load balancing
ACM Transactions on Computer Systems (TOCS)
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Stochastic dynamic programming and the control of queueing systems
Stochastic dynamic programming and the control of queueing systems
Heavy-tailed probability distributions in the World Wide Web
A practical guide to heavy tails
Analytic modeling of load balancing policies for tasks with heavy-tailed distributions
Proceedings of the 2nd international workshop on Software and performance
Prioritized resource allocation for stressed networks
IEEE/ACM Transactions on Networking (TON)
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Scheduling and Load Balancing in Parallel and Distributed Systems
Scheduling and Load Balancing in Parallel and Distributed Systems
Appendix: A primer on heavy-tailed distributions
Queueing Systems: Theory and Applications
Two M/M/1 Queues with Transfers of Customers
Queueing Systems: Theory and Applications
Simulation input analysis: difficulties in simulating queues with Pareto service
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Load Sharing in Distributed Systems
IEEE Transactions on Computers
On the controversy over tailweight of distributions
Operations Research Letters
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Suppose that customers arrive at a service center (call center, web server, etc.) with two stations in accordance with independent Poisson processes. Service times at either station follow the same general distribution, are independent of each other and are independent of the arrival process. The system is charged station-dependent holding costs at each station per customer per unit time. At any point in time, a decision-maker may decide to move, at a cost, some number of jobs in one queue to the other. The goals of this paper are twofold. First, we are interested in providing insights into this decision-making scenario. We do so, in the important case that the service time distribution is highly variable or simply has a heavy tail. Secondly, we propose that the savvy use of Markov decision processes can lead to easily implementable heuristics when features of the service time distribution can be captured by introducing multiple customer classes. To this end, we consider a two-station proxy for the original system, where the service times are assumed to be exponential, but of one of two classes with different rates. We prove structural results for this proxy and show that these results lead to heuristics that perform well.