Unevenness measurement using the support vector machine and dynamic multiservice load balancing with modified genetic algorithm in cloud-based multimedia system

Abstract

Cloud computing is the most concerned and growing field in the real world, which is used by various fields to handle and manage the multimedia application due to availability of more amount of resources. At the time of multiple multimedia requests entering into the cloud server, fining and provisioning the required resources for the incoming multimedia requests of different king would be a more difficult task. In the existing system centralised hierarchical cloud-based multimedia system (CMS) is introduced which consists of elements such as resource manager, cluster head and server clusters. This word can assign the user required resources effectively with reduced cost. Also, existing research overcomes the load balancing problem which might occur at the time of multiple multimedia services with different characteristics entering into the system and allocating them in the server without considering their load capacity level using genetic algorithm. However, genetic algorithm would fail to find the optimal resource with optimal load level due to local search optimisation problem. A, load balancing cannot be done effectively in case of arrival of multimedia tasks with varying characteristics. This problem is resolved in the proposed research methodology by introducing the novel load balancing system in which both task unevenness problems and the genetic algorithms local search optimisation problems are resolved. This paper proposed an effective multimedia load balancing (MLB) scheme for CMS using support vector machine (SVM) and dynamic multiservice load balancing with adaptive genetic algorithm (AGA) (MLB-SVM-AGA). In this work, SVM is introduced for the purpose of quantifying the unevenness in the utilisation of multiple resources on a resource manager on the client side and confirmed at the server side in the each cluster. Unevenness scenario can be modelled as a mathematical hyperplane problem, but in most cases it is computationally intractable. Here, AGA is used for the purpose of solving the problem of dynamic load balancing. The experimental evaluation is conducted in the CloudSim toolkit for both proposed and existing research methodologies. The performance evaluation is done and the results demonstrate that AGA approach is able to dynamically spread the multimedia task load equally.