Randomization, speculation, and adaptation in batch schedulers
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
IEEE Transactions on Parallel and Distributed Systems
Attacking the bottlenecks of backfilling schedulers
Cluster Computing
IPPS '99/SPDP '99 Proceedings of the 13th International Symposium on Parallel Processing and the 10th Symposium on Parallel and Distributed Processing
Core Algorithms of the Maui Scheduler
JSSPP '01 Revised Papers from the 7th International Workshop on Job Scheduling Strategies for Parallel Processing
Multiple-Queue Backfilling Scheduling with Priorities and Reservations for Parallel Systems
JSSPP '02 Revised Papers from the 8th International Workshop on Job Scheduling Strategies for Parallel Processing
An Integrated Approach to Parallel Scheduling Using Gang-Scheduling, Backfilling, and Migration
IEEE Transactions on Parallel and Distributed Systems
Self-Adapting Backfilling Scheduling for Parallel Systems
ICPP '02 Proceedings of the 2002 International Conference on Parallel Processing
Parallel job scheduling — a status report
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
Power-aware resource allocation in high-end systems via online simulation
Proceedings of the 19th annual international conference on Supercomputing
Xen and the Art of Cluster Scheduling
VTDC '06 Proceedings of the 2nd International Workshop on Virtualization Technology in Distributed Computing
Exploring portfolio scheduling for long-term execution of scientific workloads in IaaS clouds
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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Although thoroughly investigated, job scheduling for high-end parallel systems remains an inexact science, requiring significant experience and intuition from system administrators to properly configure batch schedulers. Production schedulers provide many parameters for their configuration, but tuning these parameters appropriately can be very difficult - their effects and interactions are often nonintuitive. In this paper, we introduce a methodology for automating the difficult process of job scheduler parameterization. Our proposed methodology is based on using past workload behavior to predict future workload, and on online simulations of a model of the actual system to provide on-the-fly suggestions to the scheduler for automated parameter adjustment. Detailed performance comparisons via simulation using actual supercomputing traces indicate that out methodology consistently outperforms other workload-aware methods for scheduler parameterization.