A Reduced-Load Equivalence for Generalised Processor Sharing Networks with Long-Tailed Input Flows
Queueing Systems: Theory and Applications
Heavy Tails: The Effect of the Service Discipline
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
The impact of the service discipline on delay asymptotics
Performance Evaluation - Modelling techniques and tools for computer performance evaluation
Waiting Time Asymptotics in the Single Server Queue with Service in Random Order
Queueing Systems: Theory and Applications
Tail asymptotics for the queue length in an M/G/1 retrial queue
Queueing Systems: Theory and Applications
Sojourn time asymptotics in processor-sharing queues
Queueing Systems: Theory and Applications
ACM SIGMETRICS Performance Evaluation Review
Heavy-tailed asymptotics for a fluid model driven by an M/G/1 queue
Performance Evaluation
Global and local asymptotics for the busy period of an M/G/1 queue
Queueing Systems: Theory and Applications
Uniform approximations for the M/G/1 queue with subexponential processing times
Queueing Systems: Theory and Applications
Queueing Systems: Theory and Applications
Sojourn time tails in the single server queue with heavy-tailed service times
Queueing Systems: Theory and Applications
Performance analysis of a video streaming buffer
ICN'05 Proceedings of the 4th international conference on Networking - Volume Part I
Delay tails in MapReduce scheduling
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
Is Tail-Optimal Scheduling Possible?
Operations Research
Queueing Systems: Theory and Applications
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We characterise the tail behaviour of the busy period distribution in theGI/ G/1 queue under the assumption that the tail of the service time distribution is of intermediate regular variation. This extends a result of de Meyer and Teugels (de Meyer and Teugels 1980), who treated theM/ G/1 queue with a regularly varying service time distribution. Our method of proof is, opposed to the one in de Meyer and Teugels (1980), probabilistic, and reveals an insightful relationship between the busy period and the cycle maximum.