An improved grey wolf optimization algorithm based task scheduling in cloud computing environment

Abstract EN

The demand for massive computing power and storage space has been escalating in various fields and in order to satisfy this need a new technology known as cloud computing is introduced.

The capability of providing these services effectively and economically has made cloud computing technology more popular.

With the advent of virtualization, IT services being offered have started to shift to cloud computing.

Virtualization had paved way for resource availability in an inexhaustible manner.

As Cloud Computing is still at its unrefined form and to derive its full potential more analysis is needed.

The way in which resources and tasks get allocated in cloud environment requires more analysis.

This in turn accounts for the Quality of Services (QoS) of the services offered by cloud service providers.

This paper proposes to simulate the PerformanceCost Grey Wolf Optimization (PCGWO) algorithm based to achieve optimization in the process of allocation of resources and tasks in cloud computing domain using CloudSim toolkit.

The main purpose is to lower both the processing time and cost in accordance to objective function.

The superiority of proposed technique is evident from the simulation results that show a comprehensive reduction in task completion time and cost.

Also using this technique more no.

of tasks can be efficiently completed within the deadline.

Thus the results indicate that in accordance to performance the PCGWO method fares better than existing algorithms