Title :
LiPS: A Cost-Efficient Data and Task Co-Scheduler for MapReduce
Author :
Ehsan, Mehdi ; Sion, Radu
Abstract :
We introduce LiPS, a new cost-efficient data and task co-scheduler for MapReduce in a cloud environment. LiPS allows flexible control of job make spans, multi-resource management, and fairness. By using linear programming to simultaneously co-schedule data and tasks, LiPS helps to achieve minimized dollar cost globally. We evaluated LiPS both analytically and on Amazon EC2 in order to measure actual dollar charges. The results were significant; LiPS saved 58-79% of the dollar costs when compared with the Hadoop default scheduler, while also allowing users to fine-tune the cost-performance tradeoff.
Keywords :
linear programming; parallel processing; processor scheduling; resource allocation; Amazon EC2; Hadoop default scheduler; LiPS; MapReduce; cost-efficient data and task coscheduler; cost-performance tradeoff; flexible job makespan control; linear programming; multiresource management; Data models; Data transfer; Linear programming; Lips; Processor scheduling; Schedules; Cloud Computing; Co-Scheduling;
Conference_Titel :
Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2013 IEEE 27th International
Conference_Location :
Cambridge, MA
Print_ISBN :
978-0-7695-4979-8
DOI :
10.1109/IPDPSW.2013.175
