Evaluating Reinforcement Learning For Cloud Resource Allocation Within Multi-Tenant Environments: A Comparative Theoretical Analysis With Traditional Algorithms

— This paper explores the theory of reinforcement learning as a cloud optimisation and allocation technique within a Zimbabwean telco-oriented multi-tenant environment. Conventional allocation algorithms and approaches, including heuristic, rule-based, and load-driven algorithms, are already in use in modern cloud implementations such as VMware and AWS. Still, they are often unable to handle dynamic, bursty traffic patterns. This research, therefore, examines the existing literature on reinforcement learning-based allocation techniques and compares them with known scheduling models. A multi-tenant cloud model is conceptualized to frame comparisons and outline the major factors, namely tenant variability, allocation goals, infrastructure, limitations, and decision-making behaviour. It compares the two families of algorithms at the theoretical level, including scalability, fairness, responsiveness, resource efficiency, and scale responsiveness to dynamic demand. The research adds to a polished perspective on the potential advantages and drawbacks of reinforcement learning in multifaceted cloud setups and offers a theoretical framework that identifies the circumstances in which RL methods could provide a better allocation functionality. The results will give researchers, cloud architects, and telecommunications stakeholders a baseline for future empirical, simulation-based, or implementation-driven assessments of intelligent cloud resource allocation strategies. Keywords—Cloud Resource Allocation: Conceptual Framework: Multi-Tenant Environments: Reinforcement Learning: SLA Performance: Traditional Algorithms