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AI-Optimized Resource Allocation in Cloud Computing: Performance Engineering Through Predictive Load Balancing

Year: 2025

Type: article

Abstract: Cloud computing has revolutionized modern computing by providing scalable and on-demand computing resources. However, efficient resource allocation remains a critical challenge, directly affecting system performance, cost, and energy consumption. This research explores the role of AI-driven predictive load balancing in optimizing resource allocation within the scope of performance engineering and cloud engineering. By leveraging machine learning-based forecasting models, cloud infrastructure can predict workload fluctuations and dynamically allocate resources, improving efficiency, reliability, and overall system performance. Experimental results demonstrate significant improvements in resource utilization, response time, and energy efficiency

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14964740

Authors: Hitesh Jodhavat, Nirmesh Khandelwal, Gaurav Mishra

Institutions: Oracle (United States), Amazon (United States)

Language: English

Topic: Cloud Computing and Resource Management

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W6930671519

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