Energy and Performance Centric Resource Allocation Framework in Virtual Machine Consolidation using Reinforcement Learning Approach

Abstract

Virtual machines are deployed to ease the performance, management overhead and access regularities of applications on cloud platforms. Virtual machines are often susceptible to overloading burdens, delays and other hurdles during server consolidation and migration processes. In order to regulate the energy dissipation, monitor the overloading and under loading issues, dynamic consolidation processes are introduced to distribute loads across virtual machines substantially. Consolidation process demands additional computations and resources to reallocate the services from one virtual machine to another, subjected to adherence of Service Level Agreements. The proposed methodology advocates the implementation of a novel architecture for consolidating virtual machines and thus balance the energy and performance parameters. Overall resource requirements, Performance to Power Ratio (PPR) are primary factors in design of proposed Dynamic Weightage algorithm, including the clustering approach with respect to reinforcement learning techniques. Cluster of optimal virtual machines are formed, resources are allocated based on optimal energy and performance expectations. Resource requests from virtual machines are derived into a matching relationship factor to represent the respective hosts with PPR considerations. These estimations also deliver the overall workload incurred during virtual machine consolidation. It is observed that the performance throughout the cluster is maintained to be a nominal with minimal trade-off to energy. The architecture is implemented over an offline platform, which are distributed environments, enabling scalability and improved efficiency of the entire system. The system is validated on CloudSim simulator with datasets retrieved from PlanetLab. From the results obtained, it is noted that energy conservation has yielded up to 47%, with promising quality of service parameters. Results are compared with other state of art algorithms for distributed architectures and heterogeneous environments to prove the efficiency of the system.