Task Scheduling Management for Load Balancing Using Task Grouping Based on Cloud Computing

Abstract

Managing task scheduling management in cloud computing is an essential part for the landscape of complex procedure tasks based on various resources in a proficient and scalable path. The aim of this research is to dynamically optimize the aforesaid issue of task scheduling. The task management improvises the imperfection algorithm by pursue on weighted fair queuing model, which is significantly effective compared to the existing method. A task scheduling model has been created to demonstrate the proposed scheduler management. Study shows the improvement in the adaptation of round robin and shortest job first algorithm performing better than the existing algorithm according to the differentiate execution measurements such as, turnaround time, task size and average waiting time. In addition, context switches play an important role in algorithm by sharing between multiple tasks and running task in the scheduler. Altogether, a significant improvement between existing algorithm and proposed studies follows up accordingly to a specific context switching takes place.