Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2014, Special Issue (2014), Article ID 217547, 12 pages.

Estimated Interval-Based Checkpointing (EIC) on Spot Instances in Cloud Computing

Daeyong Jung, JongBeom Lim, Heonchang Yu, and Taeweon Suh

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In cloud computing, users can rent computing resources from service providers according to their demand. Spot instances are unreliable resources provided by cloud computing services at low monetary cost. When users perform tasks on spot instances, there is an inevitable risk of failures that causes the delay of task execution time, resulting in a serious deterioration of quality of service (QoS). To deal with the problem on spot instances, we propose an estimated interval-based checkpointing (EIC) using weighted moving average. Our scheme sets the thresholds of price and execution time based on history. Whenever the actual price and the execution time cross over the thresholds, the system saves the state of spot instances. The Bollinger Bands is adopted to inform the ranges of estimated cost and execution time for user's discretion. The simulation results reveal that, compared to the HBC and REC, the EIC reduces the number of checkpoints and the rollback time. Consequently, the task execution time is decreased with EIC by HBC and REC. The EIC also provides the benefit of the cost reduction by HBC and REC, on average. We also found that the actual cost and execution time fall within the estimated ranges suggested by the Bollinger Bands.

Article information

J. Appl. Math., Volume 2014, Special Issue (2014), Article ID 217547, 12 pages.

First available in Project Euclid: 1 October 2014

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Jung, Daeyong; Lim, JongBeom; Yu, Heonchang; Suh, Taeweon. Estimated Interval-Based Checkpointing (EIC) on Spot Instances in Cloud Computing. J. Appl. Math. 2014, Special Issue (2014), Article ID 217547, 12 pages. doi:10.1155/2014/217547.

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