Decisive Path Scheduling: A New List Scheduling Method
ICPP '97 Proceedings of the international Conference on Parallel Processing
DFRN: A New Approach for Duplication Based Scheduling for Distributed Memory Multiprocessor Systems
IPPS '97 Proceedings of the 11th International Symposium on Parallel Processing
Comparative Study of Static Scheduling with Task Duplication for Distributed Systems
IRREGULAR '97 Proceedings of the 4th International Symposium on Solving Irregularly Structured Problems in Parallel
Optimal Checkpoint Interval Analysis Using Stochastic Petri Net
PRDC '01 Proceedings of the 2001 Pacific Rim International Symposium on Dependable Computing
A List Scheduling Algorithm for Scheduling Multi-user Jobs on Clusters
High Performance Computing for Computational Science - VECPAR 2008
Modelization of a communication protocol for CSCW systems using coloured petri nets
CDVE'05 Proceedings of the Second international conference on Cooperative Design, Visualization, and Engineering
Ramos: Concurrent writing and reconfiguration for collaborative systems
Journal of Parallel and Distributed Computing
Resource management of distributed virtual machines
International Journal of Ad Hoc and Ubiquitous Computing
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Since it has been shown that the multiprocessor scheduling problem is NP-complete, research efforts have resulted in numerous scheduling algorithms based on heuristics. The need for measuring the effectiveness of the various task scheduling algorithms leads to a great demand on modeling and evaluation tools. This paper proposes to use the stochastic Petri net (SPN) model and its equivalent continuous time Markov chain (CTMC) as the tool for modeling and evaluation of task scheduling algorithms. The proposed approach is applied to DPS (decisive path scheduling) algorithm to investigate its effectiveness. The proposed approach is verified by comparing the result obtained using the SPN model with that obtained by actually running DPS algorithm. The performance comparison reveals that the proposed approach provides very accurate performance evaluation for the scheduling algorithm when the CCR (communication to computation ratio) value is small.