AN ADAPTIVE HYBRID OPTIMIZATION FRAMEWORK FOR SUSTAINABLE ENERGY MANAGEMENT IN CLOUD DATA CENTERS

This research proposes an Adaptive Hybrid Optimization Framework for Sustainable Energy Management in Cloud Data Centers. The framework addresses the increasing energy and environmental challenges associated with cloud infrastructure by integrating workload prediction, adaptive hybrid optimization, VM placement, VM consolidation, renewable-energy-aware scheduling, and multi-objective sustainability management. Unlike conventional approaches that focus primarily on minimizing energy consumption, the proposed framework simultaneously considers energy, operational cost, carbon emissions, SLA compliance, resource utilization, VM migration overhead, and renewable-energy utilization. The principal research contribution is the development of an adaptive optimization architecture capable of changing its decision-making strategy according to workload conditions, energy availability, and operational constraints. The proposed methodology can be evaluated using CloudSim/CloudSim Plus and benchmark workload scenarios against conventional and intelligent optimization techniques. The research is expected to contribute toward the development of intelligent, energy-efficient, cost-effective, and environmentally sustainable cloud data centers. Future validation using real-world cloud infrastructure will be essential to establish scalability and practical deployment feasibility.

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