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Computational Intelligence and Neuroscience
Volume 2017, Article ID 4873459, 12 pages
https://doi.org/10.1155/2017/4873459
Research Article

Adaptive Resource Utilization Prediction System for Infrastructure as a Service Cloud

1Computer Engineering Department, Bahria University, Islamabad, Pakistan
2Department of Electrical Engineering, COMSATS Institute of Information Technology Attock, Attock, Pakistan
3Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Peshawar, Pakistan

Correspondence should be addressed to Qazi Zia Ullah; moc.oohay@naismoc_aiz

Received 31 December 2016; Revised 19 March 2017; Accepted 16 April 2017; Published 25 July 2017

Academic Editor: Silvia Conforto

Copyright © 2017 Qazi Zia Ullah 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.

Linked References

  1. M. A. Vouk, “Cloud computing—issues, research and implementations,” Journal of Computing and Information Technology, vol. 16, no. 4, pp. 235–246, 2008. View at Publisher · View at Google Scholar
  2. M. Khare and A. Kumar, “Method and apparatus for preventing starvation in a multi-node architecture,” U.S. Patent No. 6,487,643, 2002.
  3. W. Vanderbauwhede, “The Gannet service-based SoC: a service-level reconfigurable architecture,” in Proceedings of the 1st NASA/ESA Conference on Adaptive Hardware and Systems, AHS '06, pp. 255–261, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. M. L. Badger, T. Grance, R. Patt-Corner, and J. Voas, “Cloud computing synopsis and recommendations,” in NIST Special Publication, vol. 800, p. 146, NIST special publication, 2011. View at Google Scholar
  5. D. Nurmi, R. Wolski, C. Grzegorczyk et al., “The eucalyptus open-source cloud-computing system,” in Proceedings of the 9th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID '09), pp. 124–131, Shanghai, China, May 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. J. N. Silva, L. Veiga, and P. Ferreira, “Heuristic for resources allocation on utility computing infrastructures,” in Proceedings of the 6th International Workshop on Middleware for Grid Computing (MGC '08), pp. 1–6, ACM, Leuven, Belgium, December 2008.
  7. H. C. Lim, S. Babu, J. S. Chase, and S. S. Parekh, “Automated control in cloud computing: challenges and opportunities,” in Proceedings of the 1st Workshop on Automated Control for Datacenters and Clouds ACDC ’09, pp. 13–18, ACM, New York, NY, USA, 2009.
  8. E. Caron, F. Desprez, and A. Muresan, “Forecasting for grid and cloud computing on-demand resources based on pattern matching,” in Proceedings of the 2nd IEEE International Conference on Cloud Computing Technology and Science, CloudCom '10, pp. 456–463, IEEE, Indianapolis, Indiana, USA, November 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Govindan, J. Choi, B. Urgaonkar, A. Sivasubramaniam, and A. Baldini, “Statistical profiling-based techniques for effective power provisioning in data centers,” in Proceedings of the 4th ACM European Conference on Computer Systems, EuroSys'09, pp. 317–330, April 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. N. Bobroff, A. Kochut, and K. Beaty, “Dynamic placement of virtual machines for managing SLA violations,” in Proceedings of the 10th IFIP/IEEE International Symposium on Integrated Network Management, pp. 119–128, May 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Verma, G. Dasgupta, T. K. Nayak, P. De, and R. Kothari, “Server workload analysis for power minimization using consolidation,” in USENIX ATC, 2009. View at Google Scholar
  12. J. Rolia, L. Cherkasova, M. Arlitt, and A. Andrzejak, “A capacity management service for resource pools,” in Proceedings of the 5th International Workshop on Software and Performance, WOSP'05, pp. 229–237, July 2005. View at Scopus
  13. G. Chen, W. He, J. Liu et al., “Energy aware server provisioning and load dispatching for connection-intensive internet services,” in NSDI, pp. 337–350, 2008. View at Google Scholar
  14. N. R. Herbst, N. Huber, S. Kounev, and E. Amrehn, “Self-adaptive workload classification and forecasting for proactive resource provisioning,” Concurrency Computation Practice and Experience, vol. 26, no. 12, pp. 2053–2078, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. N. Kim, J. Cho, and E. Seo, “Energy-credit scheduler: An energy-aware virtual machine scheduler for cloud systems,” Future Generation Computer Systems, vol. 32, no. 1, pp. 128–137, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. H. Nguyen et al., “Agile: elastic distributed resource scaling for infrastructure-as-a-service,” in Proceedings of the of the 10th International Conference on Autonomic Computing ICAC '13, 2013.
  17. A. Khan, X. Yan, S. Tao, and N. Anerousis, “Workload characterization and prediction in the cloud: a multiple time series approach,” in Proceedings of the IEEE Network Operations and Management Symposium (NOMS '12), pp. 1287–1294, Maui, Hawaii, USA, April 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. D. Choi and P. J. Wolfe, “Co-clustering separately exchangeable network data,” The Annals of Statistics, vol. 42, no. 1, pp. 29–63, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. B. Urgaonkar, P. Shenoy, and T. Roscoe, “Resource overbooking and application profiling in shared hosting platforms,” in Proceedings of the the 5th symposium, p. 239, Boston, Massachusetts, December 2002. View at Publisher · View at Google Scholar
  20. T. Wood, L. Cherkasova et al., “Profiling and modeling resource usage of virtualized applications,” in Proceedings of the Middleware Conference—Proceedings, 2008. View at Scopus
  21. W. Zheng et al., “JustRunIt: experiment-based management of virtualized data centers,” in Proceedings of the USENIX Annual Technical Conference, 2009.
  22. A. Beloglazov and R. Buyya, “Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers,” Concurrency Computation Practice and Experience, vol. 24, no. 13, pp. 1397–1420, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. R. Doyle, J. Chase, O. Asad, W. Jin, and A. Vahdat, “Model-based resource provisioning in a web service utility,” USITS, 2003. View at Google Scholar
  24. P. Shivam, S. Babu, and J. Chase, “Learning application models for utility resource planning,” in Proceedings of the USITS, 2003.
  25. C. Stewart, T. Kelly, A. Zhang, and K. Shen, “A dollar from 15 cents: cross-platform management for internet services,” in Proceedings of the USENIX Annual Technical Conference, 2008.
  26. A. Ganapathi, H. Kuno, U. Dayal et al., “Predicting multiple metrics for queries: Better decisions enabled by machine learning,” in Proceedings of the 25th IEEE International Conference on Data Engineering, ICDE '09, pp. 592–603, April 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. P. Shivam, S. Babu, and J. Chase, “Active and accelerated learning of cost models for optimizing scientific applications,” in Proceedings of the 32nd International Conference on Very Large Data Bases, VLDB '06, September 2006. View at Scopus
  28. J. Rao, X. Bu, C.-Z. Xu, L. Wang, and G. Yin, “VCONF: a reinforcement learning approach to virtual machines auto-configuration,” in Proceedings of the 6th International Conference on Autonomic Computing, ICAC '09, pp. 137–146, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  29. X. Zhu, D. Young, B. J. Watson et al., “1000 Islands: integrated capacity and workload management for the next generation data center,” in Proceedings of the 5th International Conference on Autonomic Computing, ICAC '08, pp. 172–181, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. E. Kalyvianaki, T. Charalambous, and S. Hand, “Self-adaptive and self-configured CPU resource provisioning for virtualized servers using Kalman filters,” in Proceedings of the 6th International Conference on Autonomic Computing, ICAC '09, pp. 117–126, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. P. Padala, K. G. Shin, X. Zhu et al., “Adaptive control of virtualized resources in utility computing environments,” in Proceedings of the 2007 Eurosys Conference, pp. 289–302, March 2007. View at Publisher · View at Google Scholar · View at Scopus
  32. J. Rolia, L. Cherkasova, M. Arlitt, and V. Machiraju, “Supporting application quality of service in shared resource pools,” Communications of the ACM, vol. 49, no. 3, pp. 55–60, 2006. View at Publisher · View at Google Scholar · View at Scopus
  33. D. Gmach, J. Rolia, L. Cherkasova, and A. Kemper, “Capacity management and demand prediction for next generation data centers,” in Proceedings of the 2007 IEEE International Conference on Web Services, ICWS '07, pp. 43–50, July 2007. View at Publisher · View at Google Scholar · View at Scopus
  34. G. Chen, W. He, J. Liu et al., “Energy-aware server provisioning and load dispatching for connection-intensive internet services,” in Proceedings of the National Spatial Data Infrastructure (NSDI), 2008.
  35. A. Chandra, W. Gong, and P. Shenoy, “Dynamic Resource Allocation for Shared Data Centers Using Online Measurements,” in Quality of Service ? IWQoS 2003, vol. 2707 of Lecture Notes in Computer Science, pp. 381–398, Springer Berlin Heidelberg, Berlin, Heidelberg, 2003. View at Publisher · View at Google Scholar
  36. E. S. Buneci and D. A. Reed, “Analysis of application heartbeats: learning structural and temporal features in time series data for identification of performance problems,” in Proceedings of the Supercomputing, 2008.
  37. D. Gmach, J. Rolia, and L. Cherkasova, “Satisfying service level objectives in a self-managing resource pool,” in Proceedings of the SASO 2009—3rd IEEE International Conference on Self-Adaptive and Self-Organizing Systems, pp. 243–253, September 2009. View at Publisher · View at Google Scholar · View at Scopus
  38. F. Wuhib, R. Stadler, and M. Spreitzer, “Gossip-based resource management for cloud environments,” in Proceedings of the 2010 International Conference on Network and Service Management, CNSM '10, pp. 1–8, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  39. Z. Gong, X. Gu, and J. Wilkes, “Press: predictive elastic resource scaling for cloud systems,” in Proceedings of the International Conference on Network and Service Management (CNSM '10), pp. 9–16, IEEE, Ontario, Canada, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  40. G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, 2003. View at Publisher · View at Google Scholar · View at Scopus
  41. M. Valipour, M. E. Banihabib, and S. M. R. Behbahani, “Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir,” Journal of Hydrology, vol. 476, pp. 433–441, 2013. View at Publisher · View at Google Scholar · View at Scopus
  42. R. N. Calheiros, E. Masoumi, R. Ranjan, and R. Buyya, “Workload prediction using ARIMA model and its impact on cloud applications' QoS,” IEEE Transactions on Cloud Computing, vol. 3, no. 4, pp. 449–458, 2015. View at Publisher · View at Google Scholar · View at Scopus
  43. F. Ding and H. Duan, “Two-stage parameter estimation algorithms for Box-Jenkins systems,” IET Signal Processing, vol. 7, no. 8, pp. 646–654, 2013. View at Publisher · View at Google Scholar · View at Scopus
  44. V. G. Tran, V. Debusschere, and S. Bacha, “Hourly server workload forecasting up to 168 hours ahead using Seasonal ARIMA model,” in Proceedings of the 2012 IEEE International Conference on Industrial Technology, ICIT '12, pp. 1127–1131, March 2012. View at Publisher · View at Google Scholar · View at Scopus
  45. T. Thadewald and H. Büning, “Jarque-Bera test and its competitors for testing normality—a power comparison,” Journal of Applied Statistics, vol. 34, no. 1-2, pp. 87–105, 2007. View at Publisher · View at Google Scholar · View at MathSciNet
  46. K. W. Hipel and A. I. McLeod, Time Series Modelling of Water Resources and Environmental Systems, Elsevier, Amsterdam, Holland, 1994.
  47. J. J. Ruiz-Aguilar, I. J. Turias, and M. J. Jiménez-Come, “A novel three-step procedure to forecast the inspection volume,” Transportation Research Part C: Emerging Technologies, vol. 56, pp. 393–414, 2015. View at Publisher · View at Google Scholar · View at Scopus
  48. P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting, Springer Science and Business Media, Gewerbestrasse, Switzerland, 2006.
  49. N. Murata, S. Yoshizawa, and S.-I. Amari, “Network information criterion-determining the number of hidden units for an artificial neural network model,” IEEE Transactions on Neural Networks, vol. 5, no. 6, pp. 865–872, 1994. View at Publisher · View at Google Scholar · View at Scopus
  50. S. F. Crone, K. Nikolopoulos, and M. Hibon, “Automatic modelling and forecasting with artificial neural networks–A forecasting competition evaluation,” Final report for the IIF/SAS Grant, vol. 6, 2005.