Energy Efficient Resource Management and Task Scheduling at the Cloud Data Center

Abstract

Due to the growing demand for cloud services, allocation of energy efficient resources (CPU, memory, storage, etc.) and utilization of these resources are the major challenging issues of a large cloud data center. To meet the ever increasing demand of the customers, more number of servers are needed at the data center. These data centers require more cooling devices in order to keep the data center at a specified temperature resulting in more energy consumption and CO2 emission. The user requested on demand virtual machine (VM) allocation problem is widely known as a combinatorial optimization problem. Due to the large number of PMs present in the data center, the specified VM allocation problem is related to the NP-hard/NP-complete complexity class. Finding an optimal solution to the specified VM allocation problem with the multi-objective approach in the polynomial time will thus create a lot of challenges. Further, the networking devices of data center like switches consume 10% to 20% of the total energy consumed by IT devices in the data center. Hence, the network-aware VM allocation algorithm is required to minimize the energy consumption of switches and physical machines (PMs) at the cloud data center. Further, a policy for migrating VMs from underutilized PMs to the energy efficient PMs is required over a period of time without violating the service level agreement (SLA) between the cloud service provider and the customer. In order to minimize both the energy consumption and resources wastage, this thesis presents multi-objective VM allocation to PM using hybrid bio-inspired algorithms (HGACSO, HGAPSO, and HGAPSOSA) based on GA, CSO, PSO, and SA algorithms. Further, to save the energy consumption of networking switches in the cloud data center, a branch-and-bound based exact algorithm is proposed for VM allocation problem. The proposed branch-and-bound based exact algorithm saves the energy consumption of PMs and networking switches at the cloud data center. Further, the proposed VM migration technique and a 9task scheduling technique based on the First-Fit approximation algorithm will not only reduce the energy consumption at the cloud data center but also avoids the SLA. The experimental results were carried out in both homogeneous and heterogeneous cloud data center environments. Experimental results demonstrated that the proposed VM allocation algorithms outperform the state-of-the-art benchmark and peer research algorithms.