Self-adapting scheduling for tasks with dependencies in stochastic environments

Riakotakis, I; Ciorba, FM; Andronikos, T; Papakonstantinou, G

dc.contributor.author	Riakotakis, I	en
dc.contributor.author	Ciorba, FM	en
dc.contributor.author	Andronikos, T	en
dc.contributor.author	Papakonstantinou, G	en
dc.date.accessioned	2014-03-01T02:44:11Z
dc.date.available	2014-03-01T02:44:11Z
dc.date.issued	2006	en
dc.identifier.issn	15525244	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31739
dc.subject	Dynamic algorithms	en
dc.subject	Load balancing	en
dc.subject	Loops with dependencies	en
dc.subject	Stochastic environments	en
dc.subject.other	Boolean functions	en
dc.subject.other	DC motors	en
dc.subject.other	Dynamic programming	en
dc.subject.other	Loads (forces)	en
dc.subject.other	Parallel processing systems	en
dc.subject.other	Scheduling	en
dc.subject.other	Stochastic programming	en
dc.subject.other	Synthetic aperture sonar	en
dc.subject.other	Cluster computing	en
dc.subject.other	Dynamic load balancing algorithms	en
dc.subject.other	Experimental results	en
dc.subject.other	Heterogeneous clusters	en
dc.subject.other	In order	en
dc.subject.other	Inter-arrival times	en
dc.subject.other	international conferences	en
dc.subject.other	Life times	en
dc.subject.other	Load Balancing	en
dc.subject.other	Load patterns	en
dc.subject.other	Nested loops	en
dc.subject.other	paper addresses	en
dc.subject.other	parallel applications	en
dc.subject.other	Self-adapting	en
dc.subject.other	Self-scheduling	en
dc.subject.other	Stochastic environments	en
dc.subject.other	Stochastic processing	en
dc.subject.other	Scheduling algorithms	en
dc.title	Self-adapting scheduling for tasks with dependencies in stochastic environments	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/CLUSTR.2006.311912	en
heal.identifier.secondary	4100418	en
heal.identifier.secondary	http://dx.doi.org/10.1109/CLUSTR.2006.311912	en
heal.publicationDate	2006	en
heal.abstract	This paper addresses dynamic load balancing algorithms for non-dedicated heterogeneous clusters of workstations. We propose an algorithm called Self-Adapting Scheduling (SAS), targeted at nested loops with dependencies in a stochastic environment. This means that the load entering the system, not belonging to the parallel application under execution, follows an unpredictable pattern which can be modeled by a stochastic process. SAS takes into account the history of previous timing results and the load patterns in order to make accurate load balancing predictions. We study the performance of SAS in comparison with DTSS. We established in previous work that DTSS is the most efficient self-scheduling algorithm for loops with dependencies on heterogeneous clusters. We test our algorithm under the assumption that the interarrival times and life-times of incoming jobs are exponentially distributed. The experimental results show that SAS significantly outperforms DTSS especially with rapidly varying loads. © 2006 IEEE.	en
heal.journalName	Proceedings - IEEE International Conference on Cluster Computing, ICCC	en
dc.identifier.doi	10.1109/CLUSTR.2006.311912	en