D.Radhika, Assistant professor
Department of computer Science and Engineering Vivekanandha College of Engineering for women Tirunchengodu, Namakkal- 637 205.
Dr.M.Duraipandian, Professor
Department of computer Science and Engineering Nehru Institute of Technology Kaliyapuram, Coimbatore Tamil Nadu – 641 105.
Virtual Machine Task Classification Using Support Vector Machine and Improved MFO Based Task Scheduling
Authors
Abstract
The processing of data in Big data computing necessitates a significant number of CPU
cycles and network bandwidth. Dataflow is a huge data processing programming model that
comprises of jobs structured in a graph structure. Scheduling these jobs is one of the most
active study fields, with the primary goal of allocating tasks based on available resources. It
is critical to efficiently schedule jobs in a way that minimizes task completion time and
maximizes resource utilization. In recent years, many research works on task scheduling
problems in cloud computing and various heuristic approaches have been evolved which the
thesis focuses upon. Most of these efforts are focused on improving the performance such as
minimizing the makespan and efficient utilization of cloud resources that benefits the cloud
users and the providers. In this paper, Big Data analysis processing in cloud environment for
efficient dynamic task scheduling by using different techniques as machine learning
classifier and optimization approach. In machine learning classifier as Support Vector
Machine (SVM) for classification of virtual machine task classification. In this classifier can
classify the incoming request efficiently and reduce the makespan and execution time.
Further, we used moth flame optimization technique for allocating the classified task from
SVM classifier. In this proposed system have classification of virtual machine (VM) task and
evaluate the resource allocation decision making procedures. In this experiment, we carried
out by using cloud simulation environment to evaluate and analysis the proposed method. In
this proposed scheme can efficiently reduce the makespan time and load balancing to
improve the better VM Classification. And aslo we compare the proposed moth flame
optimization (MFO) with min-max algorithm and particle swarm optimization to compare the
performance respectively.