Efficient resource allocation techniques for enhancing QoS parameters in cloud

Abstract

Cloud computing integrates two different computing paradigms such as distributed computing and parallel computing. Cloud computing offers multi-tenancy with countless services and follows pay-and-use strategy. Customer satisfaction can be improved by improving the Quality of Service (QoS). Many algorithms and methods such as First Come First Served (FCFS), Service Level Agreement (SLA), Queue Management, Scheduling and Load Balancing are existing for resource allocation and management. This research work is focused on scheduling, load balancing and reducing energy consumption and response time. The quality of service is improved by concentrating on resource validation and request validation. The first work called MADRA focuses on dynamic resource allocation using multi agents to share the resources from remote locations. In order to allocate the resources efficiently and dynamically, multiple agents are used in various stages of the resource allocation. These agents will interact with Cloud users during the service time and allocate resources efficiently without disturbing the Cloud server. On the average, the proposed strategy, MADRA, reduces the response time by 20.39% and consumption of energy by 12.55% when compared with existing work in the literature. Next, a framework for resource allocation meeting the challenges of Cloud such as resource management and allocation, is proposed in this Thesis. The proposed framework improves the performance by analyzing the requests and the resources. newline