The elusive goal of workload characterization
ACM SIGMETRICS Performance Evaluation Review
The impact of job arrival patterns on parallel scheduling
ACM SIGMETRICS Performance Evaluation Review
Packing Schemes for Gang Scheduling
IPPS '96 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Workload Evolution on the Cornell Theory Center IBM SP2
IPPS '96 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Metrics for Parallel Job Scheduling and Their Convergence
JSSPP '01 Revised Papers from the 7th International Workshop on Job Scheduling Strategies for Parallel Processing
Workload Modeling for Performance Evaluation
Performance Evaluation of Complex Systems: Techniques and Tools, Performance 2002, Tutorial Lectures
Parallel Job Scheduling: A Performance Perspective
Performance Evaluation: Origins and Directions
Utilization and Predictability in Scheduling the IBM SP2 with Backfilling
IPPS '98 Proceedings of the 12th. International Parallel Processing Symposium on International Parallel Processing Symposium
The workload on parallel supercomputers: modeling the characteristics of rigid jobs
Journal of Parallel and Distributed Computing
Analysis and modeling of job arrivals in a production grid
ACM SIGMETRICS Performance Evaluation Review
Modeling correlated workloads by combining model based clustering and a localized sampling algorithm
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
Are user runtime estimates inherently inaccurate?
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
Modeling user runtime estimates
JSSPP'05 Proceedings of the 11th international conference on Job Scheduling Strategies for Parallel Processing
A similarity measure for time, frequency, and dependencies in large-scale workloads
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
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This paper proposes a comprehensive modeling architecture for workloads on parallel computers using Markov chains in combination with state dependent empirical distribution functions. This hybrid approach is based on the requirements of scheduling algorithms: the model considers the four essential job attributes submission time, number of required processors, estimated processing time, and actual processing time. So far, no model exists that considers all those attributed at the same time. To assess the goodness-of-fit of a workload model the similarity between sequences of real jobs and jobs generated from the model needs to be captured. We propose to reduce the complexity of this task and to evaluate the model by comparing the results of a widely-used scheduling algorithm instead. This approach is demonstrated with commonly used scheduling objectives. To verify this evaluation technique, standard criteria for assessing the goodness-of-fit for workload models are additionally applied.