Scheduling Scientific Workflow Applications with Deadline and Budget Constraints Using Genetic Algorithms

Grid technologies have progressed towards a service‐oriented paradigm that enables a new way of service provisioning based on utility computing models, which are capable of supporting diverse computing services. It facilitates scientific applications to take advantage of computing resources distribu...

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Vydáno v:Scientific programming Ročník 14; číslo 3-4; s. 217 - 230
Hlavní autoři: Yu, Jia, Buyya, Rajkumar
Médium: Journal Article
Jazyk:angličtina
Vydáno: 01.01.2006
ISSN:1058-9244, 1875-919X
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Abstract Grid technologies have progressed towards a service‐oriented paradigm that enables a new way of service provisioning based on utility computing models, which are capable of supporting diverse computing services. It facilitates scientific applications to take advantage of computing resources distributed world wide to enhance the capability and performance. Many scientific applications in areas such as bioinformatics and astronomy require workflow processing in which tasks are executed based on their control or data dependencies. Scheduling such interdependent tasks on utility Grid environments need to consider users′ QoS requirements. In this paper, we present a genetic algorithm approach to address scheduling optimization problems in workflow applications, based on two QoS constraints, deadline and budget.
AbstractList Grid technologies have progressed towards a service-oriented paradigm that enables a new way of service provisioning based on utility computing models, which are capable of supporting diverse computing services. It facilitates scientific applications to take advantage of computing resources distributed world wide to enhance the capability and performance. Many scientific applications in areas such as bioinformatics and astronomy require workflow processing in which tasks are executed based on their control or data dependencies. Scheduling such interdependent tasks on utility Grid environments need to consider users' QoS requirements. In this paper, we present a genetic algorithm approach to address scheduling optimization problems in workflow applications, based on two QoS constraints, deadline and budget.
Author Buyya, Rajkumar
Yu, Jia
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