Cost optimized provisioning of elastic resources for application workflows

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Workflow technologies have become a major vehicle for easy and efficient development of scientific applications. In the meantime, state-of-the-art resource provisioning technologies such as cloud computing enable users to acquire computing resources dynamically and elastically. A critical challenge in integrating workflow technologies with resource provisioning technologies is to determine the right amount of resources required for the execution of workflows in order to minimize the financial cost from the perspective of users and to maximize the resource utilization from the perspective of resource providers. This paper suggests an architecture for the automatic execution of large-scale workflow-based applications on dynamically and elastically provisioned computing resources. Especially, we focus on its core algorithm named PBTS (Partitioned Balanced Time Scheduling), which estimates the minimum number of computing hosts required to execute a workflow within a user-specified finish time. The PBTS algorithm is designed to fit both elastic resource provisioning models such as Amazon EC2 and malleable parallel application models such as MapReduce. The experimental results with a number of synthetic workflows and several real science workflows demonstrate that PBTS estimates the resource capacity close to the theoretical low bound. (C) 2011 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2011-10
Language
English
Article Type
Article
Citation

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF GRID COMPUTING AND ESCIENCE, v.27, no.8, pp.1011 - 1026

ISSN
0167-739X
URI
http://hdl.handle.net/10203/100683
Appears in Collection
CS-Journal Papers(저널논문)
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