TaskTracker aware scheduler with resource availability control for Hadoop MapReduce Online publication date: Wed, 06-Nov-2019
by Jisha S. Manjaly; T. Subbulakshmi
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 14, No. 3/4, 2019
Abstract: Schedulers are playing a vital role in task assignment for Hadoop MapReduce. In some scenario, the default schedulers of Hadoop spawn tasks in TaskTracker without checking the external dependency and may fail. As a result, Hadoop should rerun the tasks in another TaskTracker. To address this issue, TaskTracker aware scheduler has been introduced. This paper focuses the resource availability control of TaskTracker aware scheduler. The proposed scheduler will not allow a task to run and fail if the load of the TaskTracker reaches its threshold for the Job. The performance of this scheduler may increase if the scheduler is aware of the status of the resources present in the TaskTracker nodes. The main features of this scheduler are user controllability of jobs and configuration based resource utilisation control for task allocation.
Online publication date: Wed, 06-Nov-2019
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Intelligence Paradigms (IJAIP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com