Fair scheduling algorithms in grids

Doulamis, ND; Doulamis, AD; Varvarigos, EA; Varvarigou, TA

dc.contributor.author	Doulamis, ND	en
dc.contributor.author	Doulamis, AD	en
dc.contributor.author	Varvarigos, EA	en
dc.contributor.author	Varvarigou, TA	en
dc.date.accessioned	2014-03-01T01:26:21Z
dc.date.available	2014-03-01T01:26:21Z
dc.date.issued	2007	en
dc.identifier.issn	1045-9219	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18029
dc.subject	Fair grid scheduling	en
dc.subject	Grid computing	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Computer simulation	en
dc.subject.other	Grid computing	en
dc.subject.other	Image processing	en
dc.subject.other	Optimization	en
dc.subject.other	Program processors	en
dc.subject.other	Adjusted fair task order	en
dc.subject.other	Fair grid scheduling	en
dc.subject.other	Simple fair task order	en
dc.subject.other	Scheduling algorithms	en
dc.title	Fair scheduling algorithms in grids	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TPDS.2007.1053	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TPDS.2007.1053	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	In this paper, we propose a new algorithm for fair scheduling, and we compare it to other scheduling schemes such as the Earliest Deadline First and the First Come First Serve schemes. Our algorithm uses a max-min fair sharing approach for providing fair access to users. When there is no shortage of resources, the algorithm assigns to each task enough computational power for it to finish within its deadline. When there is congestion, the main idea is to fairly reduce the CPU rates assigned to the tasks, so that the share of resources that each user gets is proportional to the user's weight. The weight of a user may be defined as the user's contribution to the infrastructure or the price he is willing to pay for services or any other socioeconomic consideration. In our algorithms, all tasks whose requirements are lower than their fair share CPU rate are served at their demanded CPU rates. However, the CPU rates of tasks whose requirements are larger than their fair share CPU rate are reduced to fit the total available computational capacity in a fair manner.Three different versions of fair scheduling are adopted in this paper; the Simple Fair Task Order (SFTO), which schedules the tasks according to their respective fair completion times, the Adjusted Fair Task Order (AFTO), that refines the SFTO policy by ordering the tasks using the adjusted fair completion times, and the Max-min Fair Share (MMFS) scheduling policy, which simultaneously addresses the problem of finding a fair task order and assigning a processor to each task based on a Max-Min fair sharing policy. Experimental results and comparisons with traditional scheduling schemes, such as the Earliest Deadline First (EDF) and the First Come First Served (FCFS) are presented using three different error criteria. Validation of the simulations using real experiments of tasks generated from 3D image rendering processes is also provided. The three proposed scheduling schemes can be integrated into existing Grid computing architectures. © 2007 IEEE.	en
heal.publisher	IEEE COMPUTER SOC	en
heal.journalName	IEEE Transactions on Parallel and Distributed Systems	en
dc.identifier.doi	10.1109/TPDS.2007.1053	en
dc.identifier.isi	ISI:000249702800012	en
dc.identifier.volume	18	en
dc.identifier.issue	11	en
dc.identifier.spage	1630	en
dc.identifier.epage	1648	en