International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The exponential growth of digital transformation in higher education has positioned cloud computing as a critical enabler of academic and research excellence worldwide. Cloud computing has transformed university resource management through Infrastructure as a Service (IaaS), providing scalable and flexible solutions. However, optimizing resource allocation remains challenging due to dynamic workloads and fluctuating user demands, often resulting in underutilization, overprovisioning, and increased operational costs. Traditional allocation strategies inadequately address evolving academic requirements, necessitating data-driven approaches. This paper explores machine learning techniques for optimizing resource allocation in university cloud IaaS environments. The objectives were to: analyze Random Forest classification for predicting resource demand and examine K-means clustering for identifying usage patterns and anomalies in resource utilization. A mixed-methods research design was employed, collecting data from four Kenyan public universities: Moi University, Masinde Muliro University of Science and Technology, Turkana University, and Alupe University. Stratified sampling represented institutions of varying sizes, while purposive sampling selected ICT administrators and directors. Data sources included interviews, system logs, historical usage reports, and open IaaS datasets, analyzed through machine learning and thematic analysis. Key findings demonstrate significant optimization potential. The Random Forest model achieved 87.6% accuracy in demand prediction, effectively identifying peak periods and anomalies. K-means clustering revealed four distinct usage patterns (low, medium, high, and variable), enabling strategic resource planning. The combined application of both techniques enhanced resource allocation efficiency by 17%, reduced system response time by 33%, improved availability to 98.2%, and decreased operational costs by 20.7%. The study concludes that machine learning approaches significantly optimize university cloud IaaS resource management. The complementary nature of supervised and unsupervised learning techniques provides comprehensive insights for effective resource allocation, with practical implications for cost reduction and performance improvement in higher education institutions.

Keywords: Cloud resource allocation, Infrastructure as a Service (IaaS), Random Forest classification, K-means clustering and Machine learning optimization.

Downloads: | DOI: 10.17148/IJARCCE.2025.14901