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Articles

Auto-adaptive learning-based workload forecasting in dynamic cloud environment

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Pages 541-551 | Received 07 Jun 2019, Accepted 21 Sep 2020, Published online: 12 Oct 2020

References

  • Kumar J, Saxena D, Singh AK, et al. Biphase adaptive learning-based neural network model for cloud datacenter workload forecasting. Soft Comput. 2020;1–18.
  • Saxena D, Vaisla KS, Rauthan MS. Abstract model of trusted and secure middleware framework for multi-cloud environment. In: International Conference on Advanced Informatics for Computing Research; Springer; 2018. p. 469–479.
  • Saxena D, Chauhan R, Kait R. Dynamic fair priority optimization task scheduling algorithm in cloud computing: concepts and implementations. Inter J Comput Netw Inform Secur. 2016;8(2):41.
  • Saxena D, Chauhan S. A review on dynamic fair priority task scheduling algorithm in cloud computing. Inter J Sci Envir Technol. 2014;3(3):997–1003.
  • Amiri M, Mohammad-Khanli L, Mirandola R. An online learning model based on episode mining for workload prediction in cloud. Future Gener Comp Sy. 2018;87:83–101. doi: https://doi.org/10.1016/j.future.2018.04.044
  • Zhang Q, Yang LT, Yan Z, et al. An efficient deep learning model to predict cloud workload for industry informatics. IEEE Trans Industr Inform. 2018;14(7):3170–3178. doi: https://doi.org/10.1109/TII.2018.2808910
  • Khan A, Yan X, Tao S, et al. Workload characterization and prediction in the cloud: a multiple time series approach. In: 2012 IEEE Network Operations and Management Symposium. IEEE; 2012. p. 1287–1294.
  • Amekraz Z, Hadi MY. Higher order statistics based method for workload prediction in the cloud using arma model. In: 2018 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE; 2018. p. 1–5.
  • Calheiros RN, Masoumi E, Ranjan R, et al. Workload prediction using arima model and its impact on cloud applications' qos. IEEE Trans Cloud Comput. 2015;3(4):449–458. doi: https://doi.org/10.1109/TCC.2014.2350475
  • Nielsen H, Brunak S, von Heijne G. Machine learning approaches for the prediction of signal peptides and other protein sorting signals. Engineering¡/DIFdel¿Protein Eng.. 1999;12(1):3–9.
  • Shirzad E, Saadatfar H. Job failure prediction in hadoop based on log file analysis. Inter J Comput Appl. 2020;1–10.
  • Saxena D, Singh AK. Energy aware resource efficient-(eare) server consolidation framework for cloud datacenter. In: Advances in communication and computational technology. Springer; 2020. p. 1455–1464.
  • Saxena D, Singh A. Security embedded dynamic resource allocation model for cloud data centre. Electron Lett. 2020;56(20):1062–1065. doi: https://doi.org/10.1049/el.2020.1736
  • Cetinski K, Juric MB. Ame-wpc: advanced model for efficient workload prediction in the cloud. J Netw Comput Appl. 2015;55:191–201. doi: https://doi.org/10.1016/j.jnca.2015.06.001
  • Islam S, Keung J, Lee K, et al. Empirical prediction models for adaptive resource provisioning in the cloud. Future Gener Comp Sy. 2012;28(1):155–162. doi: https://doi.org/10.1016/j.future.2011.05.027
  • Lu Y, Panneerselvam J, Liu L, et al. Rvlbpnn: a workload forecasting model for smart cloud computing. Sci Program. 2016;2016.
  • Liu C, Liu C, Shang Y, et al. An adaptive prediction approach based on workload pattern discrimination in the cloud. J Netw Comput Appl. 2017;80:35–44. doi: https://doi.org/10.1016/j.jnca.2016.12.017
  • Duy TVT, Sato Y, Inoguchi Y. Improving accuracy of host load predictions on computational grids by artificial neural networks. Int J Parallel Emergent Distrib Syst. 2011;26(4):275–290. doi: https://doi.org/10.1080/17445760.2010.481786
  • Prevost JJ, Nagothu KM, Kelley B, et al. Prediction of cloud data center networks loads using stochastic and neural models. In: 2011 6th International Conference on System of Systems Engineering; IEEE; 2011. p. 276–281.
  • Kumar J, Singh AK. Dynamic resource scaling in cloud using neural network and black hole algorithm. In: 2016 Fifth International Conference on Eco-friendly Computing and Communication Systems (ICECCS); IEEE; 2016. p. 63–67.
  • Kumar J, Singh AK. Workload prediction in cloud using artificial neural network and adaptive differential evolution. Future Gener Comp Sy. 2018;81:41–52. doi: https://doi.org/10.1016/j.future.2017.10.047
  • Zhu Z, Fan P. Machine learning based prediction and classification of computational jobs in cloud computing centers. arXiv preprint arXiv:190303759. 2019.
  • Chen Z, Hu J, Min G, et al. Towards accurate prediction for high-dimensional and highly-variable cloud workloads with deep learning. IEEE Trans Parallel Distrib Syst. 2019;31(4):923–934. doi: https://doi.org/10.1109/TPDS.2019.2953745
  • Zhu X, Uysal M, Wang Z, et al. What does control theory bring to systems research? ACM SIGOPS Oper Syst Rev. 2009;43(1):62–69. doi: https://doi.org/10.1145/1496909.1496922
  • Wu H, Zhang W, Zhang J, et al. A benefit-aware on-demand provisioning approach for multi-tier applications in cloud computing. Front Comput Sci. 2013;7(4):459–474. doi: https://doi.org/10.1007/s11704-013-2201-8
  • Amiri M, Mohammad-Khanli L. Survey on prediction models of applications for resources provisioning in cloud. J Netw Comput Appl. 2017;82:93–113. doi: https://doi.org/10.1016/j.jnca.2017.01.016
  • Urgaonkar B, Shenoy P, Chandra A, et al. Dynamic provisioning of multi-tier internet applications. In: Second International Conference on Autonomic Computing (ICAC'05); Citeseer; 2005. p. 217–228.
  • Zhang Q, Cherkasova L, Smirni E. A regression-based analytic model for dynamic resource provisioning of multi-tier applications. In: Fourth International Conference on Autonomic Computing (ICAC'07); IEEE; 2007. p. 27–27.
  • Herbst N, Amin A, Andrzejak A, et al. Online workload forecasting. In: Self-aware computing systems. Springer; 2017. p. 529–553.
  • Cao R, Yu Z, Marbach T, et al. Load prediction for data centers based on database service. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC); Vol. 1; IEEE; 2018. p. 728–737.
  • Price KV. Differential evolution: a fast and simple numerical optimizer. In: Fuzzy Information Processing Society, 1996. NAFIPS., 1996 Biennial Conference of the North American; IEEE; 1996 June. p. 524–527.
  • Wang S, Li Y, Yang H, et al. Self-adaptive differential evolution algorithm with improved mutation strategy. Soft Comput. 2018;22(10):3433–3447. doi: https://doi.org/10.1007/s00500-017-2588-5
  • Dawar D, Ludwig SA. Effect of strategy adaptation on differential evolution in presence and absence of parameter adaptation: an investigation. J Artif Intell Soft Comput Res. 2018;8(3):211–235. doi: https://doi.org/10.1515/jaiscr-2018-0014
  • Iorio AW, Li X. Solving rotated multi-objective optimization problems using differential evolution. In: Australasian Joint Conference on Artificial Intelligence. Springer; 2004. p. 861–872.
  • Zhang L, Chang H, Xu R. Equal-width partitioning roulette wheel selection in genetic algorithm. In: 2012 Conference on Technologies and Applications of Artificial Intelligence; IEEE; 2012. p. 62–67.
  • Wright AH. Genetic algorithms for real parameter optimization. In: Foundations of genetic algorithms. Vol. 1. Elsevier; 1991. p. 205–218.
  • Pavai G, Geetha T. A survey on crossover operators. ACM Comput Surveys (CSUR). 2017;49(4):72. doi: https://doi.org/10.1145/3009966
  • Wang YN, Wu LH, Yuan XF. Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure. Soft Comput. 2010;14(3):193. doi: https://doi.org/10.1007/s00500-008-0394-9
  • Arlitt MF, Williamson CL. Web server workload characterization: the search for invariants. SIGMETRICS Perform Eval Rev. 1996 May;24(1):126–137. Traces available at ftp://ita.ee.lbl.gov/html/. doi: https://doi.org/10.1145/233008.233034

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