The rapid growth of cloud computing has significantly increased the demand for efficient resource management techniques capable of supporting large-scale multi-tenant cloud environments. As cloud infrastructures continue to expand, managing heterogeneous computing resources while ensuring scalability, optimal resource utilization, Quality of Service (QoS), Service Level Agreement (SLA) compliance, energy efficiency and reduced operational costs has become increasingly challenging. Existing resource management approaches often suffer from poor scalability, high computational overhead, inefficient workload distribution and limited adaptability to dynamic workload variations. This study presents an efficient algorithm for large-scale resource management in a multi-tenant cloud environment. The proposed framework integrates intelligent resource scheduling, workload balancing and adaptive virtual machine allocation to optimize resource utilization while satisfying multiple performance objectives. An object-oriented system development methodology was employed to design the framework, while a Deep Reinforcement Learning (DRL)-based optimization algorithm was implemented to enable autonomous decision-making through continuous learning from workload patterns, resource states and environmental feedback. The proposed algorithm efficiently allocates and manages cloud resources across multiple tenants, minimizing resource contention, improving system throughput, reducing response time and energy consumption and enhancing overall cloud performance. Experimental evaluation demonstrates that the proposed approach provides a scalable, adaptive and computationally efficient solution for large-scale resource management in modern multi-tenant cloud environments.
Onwuegbuchulem Gift., Bennett E.O., M. D. et al.· International journal of re...· 0 citations
Cloud computing environments depend heavily on efficient Dynamic Resource Allocation (DRA) mechanisms to ensure optimal utilization of computational resources while maintaining low operational cost, reduced energy consumption, and acceptable Quality of Service (QoS) under continuously fluctuating workloads. However, many existing resource allocation techniques in cloud systems are limited by poor adaptability, high computational overhead, inefficient virtual machine migration, and inability to simultaneously optimize multiple conflicting objectives such as throughput, Service Level Agreement (SLA) compliance, and power efficiency. These limitations create the need for a more intelligent, scalable and adaptive resource management framework capable of making real-time allocation decisions in heterogeneous cloud environments. This study therefore presents the design and development of DynamiCloud, a scalable and computationally efficient multi-objective dynamic resource allocation model for cloud computing. The research aimed at developing an efficient algorithm for multi-objective Dynamic Resource Allocation (DRA) in cloud computing. An object-oriented system design methodology was adopted in modeling the proposed framework; while a Deep Reinforcement Learning (DRL)-based optimization algorithm was implemented to enable the system learn optimal VM allocation and reallocation policies from environmental states, reward signals, and workload behavior patterns. The design was implemented using python. Comparing the results of our implementation with the existing tools shows that our objectives were met.
Onwuegbuchulem Gift., Bennett, E.O., Matthias D. et al.· Journal of Artificial Intell...· 0 citations