Efficient Algorithm for Large Scale Resource Management in Multi-Tenant Cloud Environment

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.

Efficient Algorithm, Large-Scale Resource Management, Multi-Tenant Cloud Environment, Cloud Computing, Deep Reinforcement Learning, Resource Scheduling.

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