Document Type : Original Article
Author
MSc ,Computer Science, Achievers University, Owo , Nigeria
Abstract
Virtual machine (VM) allocation remains a foundational challenge in Infrastructure-as-a-Service (IaaS) cloud computing, particularly as multi-tenant environments grow in scale, heterogeneity, and complexity. Recent advancements increasingly emphasize the dual objectives of fairness and optimization, reflecting the need for equitable resource distribution without compromising system performance. This systematic review synthesizes peer-reviewed research published between 2020 and 2025, evaluating fairness-driven, optimization-driven, and hybrid VM allocation models. Following a PRISMA-aligned search strategy across IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Google Scholar, 15 high-quality studies meeting strict inclusion criteria were selected. Findings reveal that while fairness-driven models successfully reduce resource starvation and improve equity, they often incur higher makespan and computational overhead. Conversely, optimization-driven models demonstrate substantial gains in throughput, energy efficiency, and load balancing, yet frequently deprioritize fairness, leading to disproportionate resource allocation for low-priority tasks. Hybrid models have subsequently emerged as a dominant research direction, leveraging multi-objective and machine learning-based approaches to balance efficiency and equity effectively. Despite this progress, key research gaps persist, including a lack of standardized fairness metrics, limited validation using real-world workload traces, and inconsistent reporting of scalability and convergence properties. This review situates the Optimized Proportional Equity Model (OPEM) within this evolving landscape, highlighting its deterministic, fairness-centered design and potential to contribute to transparent resource allocation frameworks. The study concludes with actionable recommendations for developing next-generation allocation mechanisms grounded in rigorous evaluation, hybrid optimization, and standardized assessment.
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