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Journal of Applied Mathematics
Volume 2014 (2014), Article ID 217547, 12 pages
Research Article

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

Department of Computer Science Education, Korea University, Seoul, Republic of Korea

Received 21 January 2014; Accepted 6 May 2014; Published 28 May 2014

Academic Editor: Young-Sik Jeong

Copyright © 2014 Daeyong Jung et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.