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

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 promise in improving resource allocation efficiency in cloud computing environments, with an average improvement of 25% in task completion time and 30% in resource utilization.
• The review highlighted the need for more research on adaptive and intelligent resource management solutions, with a particular emphasis on developing algorithms that can learn from real-time data and adapt to changing workloads.
• The study identified several challenges in implementing machine learning-based resource allocation techniques, including data quality issues, model complexity, and lack of standardization in evaluation metrics.
• The researchers emphasized the importance of developing cloud-agnostic solutions that can be easily integrated into different cloud platforms and services.
• The review also discussed the role of big data analytics in optimizing resource allocation, with a focus on various machine learning algorithms and their applications in cloud computing.
• The study cited several examples of successful applications of machine learning-based resource allocation techniques, including a cloud-based platform for disaster recovery and a platform for managing resources in edge computing environments.
• The researchers suggested that future research directions should focus on developing more efficient and scalable machine learning-based approaches, integrating machine learning with other optimization techniques, and exploring new applications of machine learning in cloud computing.

Statistics:

• The study analyzed 100 research articles published between 2014 and 2025, with a total of 2,500 citations.
• The average improvement in task completion time using machine learning-based approaches was 25%.
• The average improvement in resource utilization using machine learning-based approaches was 30%.
• The review highlighted the need for more research on adaptive and intelligent resource management solutions, with a particular emphasis on developing algorithms that can learn from real-time data and adapt to changing workloads.
• The study identified several challenges in implementing machine learning-based resource allocation techniques, including data quality issues, model complexity, and lack of standardization in evaluation metrics.

Sources:

• Dalal, S., et al. 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.