Efficient Resource Management Technique in Virtual Machines for Improvement of CPU Utilization

Resumen

A cloud computing is a fastest growing technology and been used for many of the
applications in which the services are provided by cloud service providers in data centers
such as virtual machines (VMs), to users. The optimal allocation should be satisfy the
requirements of users and also the service providers. The proposed methodology will be a
adaptive Archimedes optimization algorithm (A2OA) for resource allocation in the cloud
computing. This adaptive method wil be utilized to solve the optimization problem and
allocated the user tasks. The adaptive method will be a combination of Archimeded
optimization algorithm (AOA) and Seagull Optimization Algorithm (SOA). In the AOA,
the updating process will be enhanced with the assistance of the SOA. Based on the
proposed A2OA, the tasks will be allocated to the user optimally. The proposed
methodology will be implemented in MATLAB and performances will be analyzed. The
performances of the proposed methodology will be evaluated based on performance
metrices such as make span, load standard deviation, load ratio, user provider satisfication
degree, response time and convergence analysis. The proposed methodology will be
compared with the conventional methods such as Genetic Algorithm (GA), Particle Swarm
Algorithm (PSO) and Whale Optimization Algorithm (WOA) respectively.