Pre-emptive scheduling of on-line real time services with task migration for cloud computing

Abstract

This paper presents a new scheduling approach to focus on providing a solution for online scheduling problem of real-time tasks using “Infrastructure as a Service” model offered by cloud computing. The real time tasks are scheduled pre-emptively with the intent of maximizing the total utility and efficiency. In traditional approach, the task is scheduled non- pre-emptively with two different types of Time Utility Functions (TUFs) - a profit time utility function and a penalty time utility function. The task with highest expected gain is executed. When a new task arrives with highest priority then it cannot be taken for execution until it completes the currently running task. Therefore the higher priority task is waiting for a longer time. This scheduling method sensibly aborts the task when it misses its deadline. Note that, before a task is aborted, it consumes system resources including network bandwidth, storage space and processing power. This leads to affect the overall system performance and response time of a task. In our approach, a preemptive online scheduling with task migration algorithm for cloud computing environment is proposed in order to minimize the response time and to improve the efficiency of the tasks. Whenever a task misses its deadline, it will be migrated the task to another virtual machine. This improves the overall system performance and maximizes the total utility. Our simulation results outperform the traditional scheduling algorithms such as the Earliest Deadline First (EDF) and an earlier scheduling approach based on the similar model.