Stochastic model and evolutionary optimization algorithm for grid scheduling

Grid computing deals with computationally intensive distributed resources on heterogeneous environment, so grid scheduling is a fundamental challenge and is critical to performance and cost. Traditional grid scheduling algorithms most use deterministic models. But grid environments in the real world...

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Veröffentlicht in:2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery Jg. 1; S. 424 - 428
Hauptverfasser: Xuelin Shi, Ying Zhao
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.08.2010
Schlagworte:
Algorithm design and analysis
Computer architecture
Evolutionary Algorithm
Grid Scheduling
Grid Workflow
QoS
Quality of service
Scheduling
Scheduling algorithm
Stochastic processes
Stochastic Scheduling
ISBN:1424459311, 9781424459315
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Zusammenfassung:Grid computing deals with computationally intensive distributed resources on heterogeneous environment, so grid scheduling is a fundamental challenge and is critical to performance and cost. Traditional grid scheduling algorithms most use deterministic models. But grid environments in the real world are subject to many sources of uncertainty or randomness, such as network status, job execution costs, which are often not known precisely in advance. A good model for a scheduling problem should address these of uncertainty. This paper presents a new stochastic model for grid scheduling and a novel evolutionary scheduling algorithm based on this model. Furthermore the optimization methods are used to improve grid QoS. At last we demonstrate the grid workflow management architecture on which the solution can be practically performed. The simulated experiments show that our scheduling algorithm is feasible.
ISBN:1424459311
9781424459315
DOI:10.1109/FSKD.2010.5569624