The Journal of Supercomputing

, Volume 75, Issue 4, pp 2149–2180 | Cite as

Improving reliability and reducing cost of task execution on preemptible VM instances using machine learning approach

  • Ashish Kumar MishraEmail author
  • Dharmendra K. Yadav
  • Yogesh Kumar
  • Naman Jain


Cloud users can acquire resources in the form of virtual machines (VMs) instances for computing. These instances can be on-demand, reserved and spot instances. Spot-priced virtual machines are offered at the reduced cost compared to on-demand and reserved but are unreliable to use as their availability depends on user’s bid. To use spot instances (preemptible VMs), users have to bid for resources and trade-off between monetary cost and reliability as reliability increases with the increase in cost of execution. The cost of execution can be reduced significantly with the use of preemptible VM instances. These instances are only available until users bid higher in comparison with spot price that is fixed by the cloud providers. Hence, it becomes a critical challenge to minimize the associated cost and increases the reliability for a given deadline. In this article, an algorithm has been designed for predicting the spot price to facilitate the users in bidding. Further, a checkpointing algorithm has been proposed for saving the task’s progress at optimal time intervals by the use of the proposed spot price prediction algorithm. The proposed algorithms in the article emphasize the use of preprocessed data for prediction of prices in short intervals. The prediction algorithm is based on machine learning techniques. It predicts the price and provides a comprehensive comparison for prediction of the prices for long term (12 h) as well as short term (10 min). For predicting the long-term and short-term prices, different machine learning techniques have been used on the basis of least error in prediction. The best suitable machine learning algorithm with least error is selected for prediction as well as checkpointing. Using these algorithms, one can improve reliability and reduce cost of computing on preemptible VM instances significantly. To the best of our knowledge, this is the first attempt of its kind in this field.


Cloud Computing Virtual machine Price Prediction Probability Fault tolerance Checkpointing Reliability 


  1. 1.
    AWS Command Line Interface (2018)., Accessed 2 Mar 2018
  2. 2.
    Agarwal S, Mishra AK, Yadav DK (2017) Forecasting price of amazon spot instances using neural networks. Int J Appl Eng Res 12(20):10276–10283Google Scholar
  3. 3.
    Agmon Ben-Yehuda O, Ben-Yehuda M, Schuster A, Tsafrir D (2013) Deconstructing amazon ec2 spot instance pricing. ACM Trans Econ Comput 1(3):16CrossRefGoogle Scholar
  4. 4.
    Alkharif S, Lee K, Kim H (2018) Time-series analysis for price prediction of opportunistic cloud computing resources. In: Proceedings of the 7th International Conference on Emerging Databases. Springer, pp 221–229Google Scholar
  5. 5.
    Calheiros RN, Ranjan R, Beloglazov A, Rose CAFD, Buyya R (2011) Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw, Pract Exper 41(1):23–50. CrossRefGoogle Scholar
  6. 6.
    Chichin S, Vo QB, Kowalczyk R (2017) Towards efficient and truthful market mechanisms for double-sided cloud markets. IEEE Trans Serv Comput 10(1):37–51CrossRefGoogle Scholar
  7. 7.
    Domanal SG, Reddy GRM (2018) An efficient cost optimized scheduling for spot instances in heterogeneous cloud environment. Future Gener Comput Syst 84:11–21CrossRefGoogle Scholar
  8. 8.
    Doulai P, Cahill W (2001) Short-term price forecasting in electric energy market. In: Proceedings of the International Power Engineering Conference, pp 17–19Google Scholar
  9. 9.
    Guresen E, Kayakutlu G, Daim TU (2011) Using artificial neural network models in stock market index prediction. Exp Syst Appl 38(8):10389–10397CrossRefGoogle Scholar
  10. 10.
    Hasan M, Goraya MS (2018) Fault tolerance in cloud computing environment: a systematic survey. Comput Ind 99:156–172CrossRefGoogle Scholar
  11. 11.
    Jung D, Chin S, Chung K, Yu H, Gil J (2011) An efficient checkpointing scheme using price history of spot instances in cloud computing environment. In: IFIP International Conference on Network and Parallel Computing. Springer, pp 185–200Google Scholar
  12. 12.
    Karunakaran S, Sundarraj R (2015) Bidding strategies for spot instances in cloud computing markets. IEEE Internet Comput 1:1–1Google Scholar
  13. 13.
    Khatua S, Mukherjee N (2013) A novel checkpointing scheme for amazon ec2 spot instances. In: 2013 13th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGrid). IEEE, pp 180–181Google Scholar
  14. 14.
    Latiff MSA, Madni SHH, Abdullahi M et al (2018) Fault tolerance aware scheduling technique for cloud computing environment using dynamic clustering algorithm. Neural Comput Appl 29(1):279–293CrossRefGoogle Scholar
  15. 15.
    Latiff MSA et al (2017) A checkpointed league championship algorithm-based cloud scheduling scheme with secure fault tolerance responsiveness. Appl Soft Comput 61:670–680CrossRefGoogle Scholar
  16. 16.
    Meroufel B, Belalem G (2018) Optimization of checkpointing/recovery strategy in cloud computing with adaptive storage management. Concurr comput Pract Exper 30(24):e4906CrossRefGoogle Scholar
  17. 17.
    Mishra AK, Umrao BK, Yadav DK (2018) A survey on optimal utilization of preemptible vm instances in cloud computing. J Supercomput 74(11):5980–6032CrossRefGoogle Scholar
  18. 18.
    Morgan J (2014) Classification and regression tree analysis. Report no 1, Boston University School of Public HealthGoogle Scholar
  19. 19.
    Sahay KB, Tripathi M (2014) An analysis of short-term price forecasting of power market by using ann. In: 2014 6th IEEE Power India International Conference (PIICON). IEEE, pp 1–6Google Scholar
  20. 20.
    Salehan A, Deldari H, Abrishami S (2017) An online valuation-based sealed winner-bid auction game for resource allocation and pricing in clouds. J Supercomput 73(11):4868–4905CrossRefGoogle Scholar
  21. 21.
    Singh VK, Dutta K (2015) Dynamic price prediction for amazon spot instances. In: 2015 48th Hawaii International Conference on System Sciences (HICSS). IEEE, pp 1513–1520Google Scholar
  22. 22.
    Tang S, Yuan J, Wang C, Li XY (2014) A framework for amazon ec2 bidding strategy under sla constraints. IEEE Trans Parallel Distrib Syst 25(1):211Google Scholar
  23. 23.
    Turchenko V, Shults V, Turchenko I, Wallace RM, Sheikhalishahi M, Vazquez-Poletti JL, Grandinetti L (2014) Spot price prediction for cloud computingusing neural networks. Int J Comput 12(4):348359Google Scholar
  24. 24.
    Wallace RM, Turchenko V, Sheikhalishahi M, Turchenko I, Shults V, Vazquez-Poletti JL, Grandinetti L (2013) Applications of neural-based spot market prediction for cloud computing. In: 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), vol 2. IEEE, pp 710–716Google Scholar
  25. 25.
    Yi S, Kondo D, Andrzejak A (2010) Reducing costs of spot instances via checkpointing in the amazon elastic compute cloud. In: 2010 IEEE 3rd International Conference on Cloud Computing (CLOUD). IEEE, pp 236–243Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Ashish Kumar Mishra
    • 1
    Email author
  • Dharmendra K. Yadav
    • 1
  • Yogesh Kumar
    • 1
  • Naman Jain
    • 1
  1. 1.Computer Science and Engineering DepartmentMotilal Nehru National Institute of Technology AllahabadAllahabadIndia

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