An energy efficient task scheduling and VM consolidation approach for multi tenant cloud environment

Abstract

Cloud computing is an emerging paradigm that utilizes computing resources over online distribution service. Cloud data centers are designed to effectively utilize the cloud resources such as networks, storage, services, applications and servers. Power consumption is one of the most important issue for the operation and maintenance of cloud data centers. Cloud applications consume huge amount of energy, high operational cost and carbon emission to environment. Many previous works addressed these problems by using virtualization techniques such as VM migration, placement, scheduling and load balancing etc. In the proposed research work, novel heuristic energy efficiency architecture model is designed to minimize the energy consumption and environmental impact, by considering green cloud data centers. Scalable framework is used to enhance the QoS with auto scaling techniques. In this newlinetechnique cloud resources are allocated and booted quickly to meet response time requirements, which depends upon incoming load. To improve the utilization of resources, resource consolidation considers with two energy awarealgorithm which includes VM placement and Live VM migration. The main aim of energy efficient model is to keep the CPU utilization under specified threshold limit for improving power efficiency and ensure QoS. A Novel framework is designed to enhance the cloud environment with optimization of power. In ord er to reduce the execution time and improve the resource utilization among user s task, Grey System Neural Network (GSNN) schedulin g is proposed. This hybrid scheduling is enabled to withstand effectively even for N number of tasks. newline newline