Execution time prediction using rough set theory in hybrid cloud

Chih Tien Fan, Yue Shan Chang, Wei Jen Wang, Shyan Ming Yuan

Research output: Contribution to conferencePaperpeer-review

10 Scopus citations


Execution time prediction is an important issue in cloud computing. Predicting the execution time fast and accurately not only can help users to schedule jobs smarter, but also maximize the throughput and minimize the resource consumption of cloud platform. While hybrid cloud provides methods to federate multiple cloud platforms, different cloud platforms have different resource attributes, which will increase the difficulties to predict a job's execution time. In this paper, we exploit Rough Set Theory (RST), which is a well-known prediction technique that uses the historical data, to predict the execution time of jobs. The evaluation presents that RST can utilize the accuracy of the execution time, while the decision can be made in a short period of time.


Conference9th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2012 and 9th IEEE International Conference on Autonomic and Trusted Computing, ATC 2012


  • Execution Time Prediction
  • History Based Approach
  • Hybrid Cloud
  • Private Cloud
  • Public Cloud
  • Rough Set Theory
  • Rough Sets


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