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Cloud computing load prediction by decomposition reinforced attention long short-term memory network optimized by modified particle swarm optimization algorithm

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Abstract

Computer resources provision over the internet resulted in the wide spread usage of cloud computing paradigm. With the use of such resources come certain challenges that can hinder its performance. The allocation of resources takes time and if a dedicated mechanism for such a task is used it needs to be efficient. Additionally, one of the most important objectives in cloud environment is to avoid excess or scarce provision of resources, and this challenge is known as the cloud computing load forecasting. From the available literature, it can be seen that the recurrent neural network (RNN) based models for cloud forecasting are insufficiently investigated and the observed research gap is still to be overcome as more models should be tested. Therefore, to fill in this gap, the long short-term memory (LSTM) deep learning model was utilized in this research for cloud load time-series forecasting, which has proven to attain excellent results when dealing with time-series data. Proposed research evaluates LSTM models with and without attention layers. However, in order to be optimized for specific task, such models require hyperparameter tuning, which is non-deterministic polynomial-time (NP-hard) problem by nature. Since metaheuristics exhibit satisfying performance in addressing NP-hard tasks, this research also introduces a modified version of a well-known particle swarm optimization (PSO) algorithm. Moreover, to decompose complex cloud forecasting time-series, the variational mode decomposition (VMD) is used for data processing. To validate proposed methodology, three groups of experiments were applied including the techniques utilizing only the LSTM, LSTM and VMD, and LSTM with attention layer and VMD. The obtained simulation outcomes strongly indicate that the proposed solution provided the best overall performance compared to other techniques in terms of captured performance metrics, mean squared error, \(R^2\) coefficient, mean absolute error and index of agreement. The SHapley Additive exPlanations analysis was applied to the most successful forecasting model in order to assess the influence of each feature on the model’s predictions. Finally, devised methodology also has the potential to assist cloud providers in resource allocation and provisioning decision-making processes.

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  1. http://gwa.ewi.tudelft.nl/datasets/gwa-t-12-bitbrains.

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Authors and Affiliations

  1. Faculty of Informatics and Computing, Singidunum University, Danijelova 32, Belgrade, 11000, Serbia

    Nebojsa Bacanin, Miodrag Zivkovic & Aleksandar Petrovic

  2. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Nebojsa Bacanin

  3. Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, Belgrade, 11010, Serbia

    Vladimir Simic

  4. College of Engineering, Department of Industrial Engineering and Management, Yuan Ze University, Yuandong Rd., Taoyuan City, 320315, Taiwan

    Vladimir Simic

  5. Department of Quantitative Methods, School of Business, King Faisal University, Hofuf, Al-Ahsa, 31982, Saudi Arabia

    Melfi Alrasheedi

Authors
  1. Nebojsa Bacanin
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  2. Vladimir Simic
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  3. Miodrag Zivkovic
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  4. Melfi Alrasheedi
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  5. Aleksandar Petrovic
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Correspondence to Vladimir Simic.

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Bacanin, N., Simic, V., Zivkovic, M. et al. Cloud computing load prediction by decomposition reinforced attention long short-term memory network optimized by modified particle swarm optimization algorithm. Ann Oper Res (2023). https://doi.org/10.1007/s10479-023-05745-0

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