Load Balancing in Cloud Computing Using Support Vector Machine and Optimized Dynamic Task Scheduling

Cloud computing enables the internet based
computing to everywhere. In cloud computing the task
scheduling and load balancing is the important issue that must
be considered in data centers. In cloud platform service
providers allocate the resources based on the available
resources this will increase the QoS. The process of
distributing workloads is a load balancing Technique. In this
paper focusing the load balancing between the virtual
machine. Load balancing are different types that is network
load, memory capacity, storage capacity and CPU load. Load
balancing supports failover, enabling scalability and decreases
costs and maximizes the resource availability. This paper
presents the detailed review of load balancing with machine
learning technique such as naïve bayes, random forest,
Decision Tree and Support Vector Machine. By these machine
learning model, SVM algorithm significantly to classify the
incoming request to reduce the processing time (makespan).
Also provide the moth flame optimization technique for
dynamic task scheduling to make better scheduling under
different load condition. We have experimented and evaluated
this algorithm in a cloud simulation environment; we obtain
better outcome than some other well-known algorithms. The
Simulation results demonstrate that the proposed technique
has a better development in terms of reducing the makespan
time and load balancing.