Characteristics of a Large Shared Memory Production Workload
JSSPP '01 Revised Papers from the 7th International Workshop on Job Scheduling Strategies for Parallel Processing
The workload on parallel supercomputers: modeling the characteristics of rigid jobs
Journal of Parallel and Distributed Computing
Adaptive Memory Allocations in Clusters to Handle Unexpectedly Large Data-Intensive Jobs
IEEE Transactions on Parallel and Distributed Systems
Profitable services in an uncertain world
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Addressing Sporadic Contention on Shared Computing Clusters
HPCASIA '05 Proceedings of the Eighth International Conference on High-Performance Computing in Asia-Pacific Region
Analysis and modeling of job arrivals in a production grid
ACM SIGMETRICS Performance Evaluation Review
Workload dynamics on clusters and grids
The Journal of Supercomputing
Analytical modelling of networks in multicomputer systems under bursty and batch arrival traffic
The Journal of Supercomputing
Modeling job arrivals in a data-intensive grid
JSSPP'06 Proceedings of the 12th international conference on Job scheduling strategies for parallel processing
A hybrid Markov chain model for workload on parallel computers
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
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In this paper we present an initial analysis of the job arrival patterns from a real parallel computing system and we develop a class of traffic models to characterize these arrival patterns. Our analysis of the job arrival data illustrates traffic patterns that exhibit heavy-tail behavior and other characteristics which are quite different from the arrival processes used in previous studies of parallel scheduling. We then investigate the impact of these arrival traffic patterns on the performance of parallel space-sharing scheduling strategies.