An Enhanced Active Monitoring Based Load Balancing Algorithm In Cloud Environment

Abstract

In current world scenarios the cloud computing is becoming popular these days due to its high availability and applicability. Because of the Internet daily increasing traffic introduces the requirement of the load balancing concept to get the most utilization of the resources available on cloud. A number of algorithms have been proposed to assign the client’s request to the available cloud nodes and to provide efficient mechanisms. And the aim is to increase the overall performance of the cloud and provide more satisfaction to users and efficient services. Initially this study gives an introduction to cloud computing and load balancing algorithms. A detailed research analysis is made on the modification of active monitoring load balancing algorithm and the utilization concept of an active monitoring load balancing algorithm. To do so a number of methods are applied: like that of using a java language to modify the load balancing algorithm and then by adding the proposed algorithm to the CloudAnalyst tool in order to fulfill the study. To analyzed and develop the concept of active monitoring load balancing algorithm. Now days there are number of recent literatures on cloud computing in general and its deployment mechanisms in particular were well studied. The result of the study shows that, considering an active monitoring load balancing algorithm by proposed an improved active monitoring load balancing algorithm in cloud computing environment. At the time of increasing load on a virtual machine the complexity of the load balancing algorithm increases. So that to increase its performance and to reduce the complexity of the load balancing algorithm we used buffer. Based on this we reduced the complexity of active monitoring load balancing algorithm at the time of number of request increases on a virtual machine. To apply all the above mentioned concepts and to increase its performance depend on the service broker policy based on the overall response time and overall data center processing time.