HyMORAC: A Hybrid Multi-Objective Framework for Adaptive VM Consolidation and Resource Allocation in Cloud Computing

Virtualization is a cornerstone of cloud computing, enabling the dynamic creation, selection, and migration of virtual machines (VMs) to meet diverse and evolving user demands. The VM selection and migration process is inherently NP-hard, as it must balance multiple interdependent factors such as energy consumption, operational cost, service level agreement (SLA) compliance, performance degradation, and load balancing. Single-parameter optimization approaches often yield suboptimal results. This study introduces HyMORAC (Hybrid Multi-Objective Resource Allocation and Consolidation), a novel framework that integrates adaptive VM consolidation with resource allocation in energy-optimized cloud data centers. HyMORAC employs the Analytic Hierarchy Process (AHP), a robust multi-criteria decision-making method, to evaluate and prioritize VMs based on Million Instructions Per Second (MIPS), bandwidth, and RAM. This ensures minimal migration time, optimal resource utilization, and reduced operational overhead. Upon user request, SLA negotiations are initiated, and VMs are allocated according to AHP-derived rankings, mitigating performance bottlenecks and enhancing load distribution. The proposed framework is implemented and evaluated using the CloudSim simulator, with performance compared against the Modified Minimum Migration Time (MMT) policy. Experimental results show that HyMORAC reduces energy consumption by up to 16% , decreases make-span by up to 43% , and lowers SLA violations by over 50% , delivering a scalable and efficient solution for next-generation cloud infrastructures.