Minimizing dependencies within generic classes for faster and smaller programs
Proceedings of the 24th ACM SIGPLAN conference on Object oriented programming systems languages and applications
PV-EASY: a strict fairness guaranteed and prediction enabled scheduler in parallel job scheduling
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Supporting GPU sharing in cloud environments with a transparent runtime consolidation framework
Proceedings of the 20th international symposium on High performance distributed computing
A Highly Scalable Decentralized Scheduler of Tasks with Deadlines
GRID '11 Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing
On extracting session data from activity logs
Proceedings of the 5th Annual International Systems and Storage Conference
A User-Based Model of Grid Computing Workloads
GRID '12 Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing
A task routing approach to large-scale scheduling
Future Generation Computer Systems
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It is customary to use open-system trace-driven simulations to evaluate the performance of parallel-system schedulers. As a consequence, all schedulers have evolved to optimize the packing of jobs in the schedule, as a means to improve a number of performance metrics that are conjectured to be correlated with user satisfaction, with the premise that this will result in a higher productivity in reality. We argue that these simulations suffer from severe limitations that lead to suboptimal scheduler designs and to even dismissing potentially good design alternatives. We propose an alternative simulation methodology called site-level simulation, in which the workload for the evaluation is generated dynamically by user models that interact with the system. We present a novel scheduler called CREASY that exploits knowledge on user behavior to directly improve user satisfaction and compare its performance to the original packing-based EASY scheduler. We show that user productivity improves by up to 50 percent under the user-aware design, while according to the conventional metrics, performance may actually degrade.