Collection of customers: a correlated M/G/1 queue
SIGMETRICS '92/PERFORMANCE '92 Proceedings of the 1992 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
The impact of autocorrelation on queuing systems
Management Science
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Experimental queueing analysis with long-range dependent packet traffic
IEEE/ACM Transactions on Networking (TON)
Self-similarity in World Wide Web traffic: evidence and possible causes
Proceedings of the 1996 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Internet traffic: periodicity, tail behavior, and performance implications
System performance evaluation
Packing Schemes for Gang Scheduling
IPPS '96 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Single-Server Queue with Markov-Dependent Inter-Arrival and Service Times
Queueing Systems: Theory and Applications
Load Unbalancing to Improve Performance under Autocorrelated Traffic
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
Locality of sampling and diversity in parallel system workloads
Proceedings of the 21st annual international conference on Supercomputing
Parallel computer workload modeling with markov chains
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
Workload characteristics of a multi-cluster supercomputer
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
Analysis and control of correlated web server queues
Computer Communications
Operations Research Letters
Local area network characteristics, with implications for broadband network congestion management
IEEE Journal on Selected Areas in Communications
| Hi-index | 0.00 |
Correlations in traffic patterns are an important facet of the workloads faced by real systems, and one that has far-reaching consequences on the performance and optimization of the systems involved. However, all the existing analytical work on understanding the effect of correlations between successive service requirements (job sizes) is limited to First-Come First-Served scheduling. This leaves open fundamental questions: How do various scheduling policies interact with correlated job sizes? Can scheduling be used to mitigate the harmful effects of correlations? In this paper we take the first step towards answering these questions. Under a simple model for job size correlations, we present the first asymptotic analysis of various common size-independent scheduling policies when the job size sequence exhibits high correlation. Our analysis reveals that the characteristics of various scheduling policies, as well as their performance relative to each other, are markedly different under the assumption of i.i.d. job sizes versus correlated job sizes. Further, among the class of size-independent scheduling policies, there is no single scheduling policy that is optimal for all degrees of correlations and thus any optimal policy must learn the correlations. We support the asymptotic analysis with numerical algorithms for exact performance analysis under an arbitrary degree of correlation, and with simulations. Finally, we verify the lessons from our correlation model on real-world traces.