SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Task assignment with unknown duration
Journal of the ACM (JACM)
Load profiling: a methodology for scheduling real-time tasks in a distributed system
ICDCS '97 Proceedings of the 17th International Conference on Distributed Computing Systems (ICDCS '97)
Describing Queueing Systems with MPA
Describing Queueing Systems with MPA
Fluid Flow Approximation of PEPA models
QEST '05 Proceedings of the Second International Conference on the Quantitative Evaluation of Systems
A Compositional Approach to Performance Modelling (Distinguished Dissertations in Computer Science)
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Modelling job allocation where service duration is unknown
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Surprising results on task assignment in server farms with high-variability workloads
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
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Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
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In this paper a novel job allocation scheme in distributed systems (TAGS) is modelled using the Markovian process algebra PEPA. This scheme requires no prior knowledge of job size and has been shown to be more efficient than round robin and random allocation when the job size distribution is heavy tailed and the load is not high. In this paper the job size distribution is assumed to be of a phase-type and the queues are bounded. Numerical results are derived and compared with those derived from models employing random allocation and the shortest queue strategy. It is shown that TAGS can perform well for a range of performance metrics. Furthermore, an attempt is made to characterise those scenarios where TAGS is beneficial in terms of the coefficient of variation and load.