Journal
IEEE TRANSACTIONS ON CLOUD COMPUTING
Volume 2, Issue 1, Pages 85-98Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCC.2013.2297928
Keywords
Cloud computing; monetary cost optimizations; workflows
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Funding
- Singapore National Research Foundation under its Environmental & Water Technologies Strategic Research Programme
- Environment & Water Industry Programme Office (EWI) of the PUB [1002-IRIS-09]
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Recently, performance and monetary cost optimizations for workflows from various applications in the cloud have become a hot research topic. However, we find that most existing studies adopt ad hoc optimization strategies, which fail to capture the key optimization opportunities for different workloads and cloud offerings (e.g., virtual machines with different prices). This paper proposes ToF, a general transformation-based optimization framework for workflows in the cloud. Specifically, ToF formulates six basic workflow transformation operations. An arbitrary performance and cost optimization process can be represented as a transformation plan (i.e., a sequence of basic transformation operations). All transformations form a huge optimization space. We further develop a cost model guided planner to efficiently find the optimized transformation for a predefined goal (e.g., minimizing the monetary cost with a given performance requirement). We develop ToF on real cloud environments including Amazon EC2 and Rackspace. Our experimental results demonstrate the effectiveness of ToF in optimizing the performance and cost in comparison with other existing approaches.
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