Auto-scaling of virtual resources for scientific workflows on hybrid clouds
- Authors
- Ahn Y.; Kim Y.
- Issue Date
- Jun-2014
- Publisher
- Association for Computing Machinery
- Keywords
- Auto-scaling; Cloud computing; Hybrid; Workflow
- Citation
- ScienceCloud 2014 - Proceedings of the 2014 ACM International Workshop on Scientific Cloud Computing, Co-located with HPDC 2014, pp 47 - 512
- Pages
- 466
- Journal Title
- ScienceCloud 2014 - Proceedings of the 2014 ACM International Workshop on Scientific Cloud Computing, Co-located with HPDC 2014
- Start Page
- 47
- End Page
- 512
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/11034
- DOI
- 10.1145/2608029.2608036
- Abstract
- Cloud computing technology enables applications to employ scalable resources dynamically. Scientists can promote large-scale scientific computational experiments over cloud environment. It is essential for many-task-computing (MTC) to certificate stable executions of applications even rapid changes of vital status of physical resources and furnish high performance resources in a long period. Auto-scaling with virtualization provides efficient and integrated cloud resource utilization. Auto-scaling issues have been actively studied as effective resource management in order to utilize large-scale data center in a good shape but most of the auto-scaling methods just easily support performance metrics such as CPU utilization and data transfer latency but seldom consider execution deadline or characteristics of an application. We propose an auto-scaling method that finishes all tasks by user specified deadline. We accomplish our goal by dynamically allocating VMs to maximize resource utilization while meeting a deadline and considering task dependency and data transfer time in workflow application. We have evaluated our auto-scaling method with protein annotation workflow application which tasks are specified as a workflow in hybrid cloud environment. The results of a simulation show the method performs automatically resource allocation actually needed satisfying deadline constraints. Copyright © 2014 ACM.
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