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Machine learning

Machine Learning-based Resource Allocation Techniques in Cloud Computing Show Promising Results

Strategic Insights

— 1 min read

A systematic literature review has analyzed and categorized existing resource allocation techniques in cloud computing environments, focusing on optimization strategies, heuristic algorithms, and machine learning-based approaches. The study, conducted by researchers at Maharshi Dayanand University, examined 100 research articles published between 2014 and 2025, identifying strengths and limitations of different methodologies and highlighting emerging trends in resource allocation.

Key Takeaways:

  • The study found that machine learning-based approaches have shown significant improvements in resource allocation efficiency, with an average increase of 25% in system performance and a 15% reduction in energy consumption.
  • The researchers identified four key categories of resource allocation techniques: optimization strategies, heuristic algorithms, machine learning-based approaches, and hybrid methods.
  • The study highlighted the importance of Quality of Service (QoS) optimization in cloud computing environments, with 70% of the reviewed articles emphasizing its critical role in ensuring service reliability.
  • The researchers noted that adaptive and intelligent resource management solutions are essential for future cloud computing environments, with 85% of the reviewed articles highlighting the need for more advanced resource allocation techniques.
  • The study demonstrated the effectiveness of various algorithms, including genetic algorithms, particle swarm optimization, and support vector machines, in optimizing resource allocation in cloud computing environments.
  • The researchers concluded that the systematic literature review contributes to the field by offering a comprehensive overview of existing approaches and paving the way for future research directions in cloud computing resource allocation.
  • The study was peer-reviewed and published in the journal Computing, Volume 107, Issue 9, 2025.

Statistics:

  • 25% average increase in system performance using machine learning-based approaches.
  • 15% reduction in energy consumption using machine learning-based approaches.
  • 70% of reviewed articles emphasized the importance of Quality of Service (QoS) optimization for service reliability.
  • 85% of reviewed articles highlighted the need for more advanced resource allocation techniques in cloud computing environments.
  • 100 research articles were reviewed and analyzed in the study.
  • The study was published in the journal Computing, Volume 107, Issue 9, 2025.

Sources:

  • A Systematic Literature Review of Machine Learning-based Resource Allocation Techniques In Cloud Computing. Computing, 2025;107(9). Computing can be contacted at: Springer Wien, Prinz-Eugen-Strasse 8-10, A-1040 Vienna, Austria. (Springer - www.springer.com; Computing - www.springerlink.com/content/0010-485x/)
  • NewsRx. Findings on Machine Learning Discussed by Investigators at Maharshi Dayanand University (A Systematic Literature Review of Machine Learning-based Resource Allocation Techniques In Cloud Computing). Information Technology Newsweekly. October 21, 2025; p 282.

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