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Cost-optimized cloud resource management for video streaming: ARIMA predictive approach

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Abstract

Predicting the number of views for videos is crucial for optimizing resource allocation and reducing costs in cloud-based video hosting platforms. In this manuscript, we propose a novel method for view count prediction using the ARIMA (AutoRegressive Integrated Moving Average) model. By accurately forecasting video viewership, we aim to minimize the allocation of unnecessary cloud resources while ensuring sufficient resources are available to handle peak demand. Our proposed method leverages historical viewership data to train and fine-tune the ARIMA model, enabling it to capture the underlying patterns and dynamics of video viewership. Through extensive experimental evaluations on a large dataset, we demonstrate the effectiveness of our approach in reducing cloud resource costs. Compared to existing methods, the proposed method achieves an average cost reduction of 25% while maintaining a high level of prediction accuracy. Furthermore, we observe a 15% improvement in resource utilization, indicating better resource allocation based on the predicted view counts.

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Data availability

The datasets analyzed during the current study are available in the Youtube trending Video Dataset repository, https://www.kaggle.com/datasets/rsrishav/youtube-trending-video-dataset.”

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. University of Mount Union, Alliance, OH, 6601, USA

    Mahmoud Darwich

  2. Ontario Tech University, Oshawa, ON, Canada

    Taghreed Alghamdi

  3. University of Mississippi, Oxford, 38677, MS, USA

    Kasem Khalil

  4. Electrical Engineering Department, Assiut University, Assiut, 71715, Egypt

    Kasem Khalil

  5. Southern University and A &M College, Baton Rouge, 70807, USA

    Yasser Ismail

  6. University of Louisiana at Lafayette, Lafayette, 70504, USA

    Magdy Bayoumi

Authors
  1. Mahmoud Darwich
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  2. Taghreed Alghamdi
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  4. Yasser Ismail
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  5. Magdy Bayoumi
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Correspondence to Mahmoud Darwich.

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Darwich, M., Alghamdi, T., Khalil, K. et al. Cost-optimized cloud resource management for video streaming: ARIMA predictive approach. Cluster Comput 27, 3163–3177 (2024). https://doi.org/10.1007/s10586-023-04135-2

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  • DOI: https://doi.org/10.1007/s10586-023-04135-2

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