Scheduling strategies and long-range dependence
Queueing Systems: Theory and Applications
Appendix: A primer on heavy-tailed distributions
Queueing Systems: Theory and Applications
Large deviations of sojourn times in processor sharing queues
Queueing Systems: Theory and Applications
A large-deviations analysis of the GI/GI/1 SRPT queue
Queueing Systems: Theory and Applications
Queueing Systems: Theory and Applications
ACM SIGMETRICS Performance Evaluation Review
Steady state approximations of limited processor sharing queues in heavy traffic
Queueing Systems: Theory and Applications
Preventing Large Sojourn Times Using SMART Scheduling
Operations Research
Self-adaptive admission control policies for resource-sharing systems
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Separation of timescales in a two-layered network
Proceedings of the 24th International Teletraffic Congress
Is Tail-Optimal Scheduling Possible?
Operations Research
| Hi-index | 0.00 |
From a rare events perspective, scheduling disciplines that work well under light (exponential) tailed workload distributions do not perform well under heavy (power) tailed workload distributions, and vice versa, leading to fundamental problems in designing schedulers that are robust to distributional assumptions on the job sizes. This paper shows how to exploit partial workload information (system load) to design a scheduler that provides robust performance across heavy-tailed and light-tailed workloads. Specifically, we derive new asymptotics for the tail of the stationary sojourn time under Limited Processor Sharing (LPS) scheduling for both heavy-tailed and light-tailed job size distributions, and show that LPS can be robust to the tail of the job size distribution if the multiprogramming level is chosen carefully as a function of the load.