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BiPhase adaptive learning-based neural network model for cloud datacenter workload forecasting

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Abstract

Cloud computing promises elasticity, flexibility and cost-effectiveness to satisfy service level agreement conditions. The cloud service providers should plan and provision the computing resources rapidly to ensure the availability of infrastructure to match the demands with closed proximity. The workload prediction has become critical as it can be helpful in managing the infrastructure effectively. In this paper, we present a workload forecasting framework based on neural network model with supervised learning technique. An improved and adaptive differential evolution algorithm is developed to improve the learning efficiency of predictive model. The algorithm is capable of optimizing the best suitable mutation operator and crossover operator. The prediction accuracy and convergence rate of the learning are observed to be improved due to its adaptive behavior in pattern learning from sampled data. The predictive model’s performance is evaluated on four real-world data traces including Google cluster trace and NASA Kennedy Space Center logs. The results are compared with state-of-the-art methods, and improvements up to 91%, 97% and 97.2% are observed over self-adaptive differential evolution, backpropagation and average-based workload prediction techniques, respectively.

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Acknowledgements

This work is financially supported by Ministry of Electronics and Information Technology (MeitY), Government of India.

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Authors and Affiliations

  1. Department of Computer Engineering and Applications, GLA University, Mathura, India

    Jitendra Kumar

  2. Department of Computer Applications, National Institute of Technology, Kurukshetra, Kurukshetra, India

    Jitendra Kumar, Deepika Saxena & Ashutosh Kumar Singh

  3. Department of Electronics Engineering, Indian Institute of Technology-BHU Varanasi, Varanasi, India

    Anand Mohan

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  1. Jitendra Kumar
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  2. Deepika Saxena
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  4. Anand Mohan
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Correspondence to Jitendra Kumar.

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Kumar, J., Saxena, D., Singh, A.K. et al. BiPhase adaptive learning-based neural network model for cloud datacenter workload forecasting. Soft Comput 24, 14593–14610 (2020). https://doi.org/10.1007/s00500-020-04808-9

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