Job Scheduling Strategies for Parallel Processing : 15th International Workshop, Jsspp 2010, Atlanta, Ga, USA, April 23, 2010, Revised Selected Papers Paperback

th Thisvolumecontainsthepaperspresentedatthe15 workshoponJobSched- ing Strategies for Parallel Processing that was held in Atlanta (GA), USA, on April 23, 2010 in conjunction with the IEEE International Parallel Processing Symposium 2010.

This year 18 papers were submitted to the workshop. All submitted papers went through a complete review process, with the full version being read and evaluated by an average of four reviewers.

We would like to especially thank the program committee members and additional referees for their willingness to participate in this e?ort and their excellent, detailed reviews: Henri Casanova, Peter A.

Chronz, Walfredo Cirne, Julita Corbalan, Arash Deshmeh,DickEpema,DrorG.Feitelson,AllanGottlieb,RajkumarKettimuthu, Virginia Lo, Kuan Lu, Vicent Matossian, Jose E.

Moreira, Bill Nitzberg, Elizeu Santos-Neto,Angela C.Sodan,MarkS.

Squillante,DanTsafrir,Philipp Wieder, and Ramin Yahyapour.

The papers in this volume show a proli?c growth in the areas of applicability forparallelscheduling.Togetherwiththemorecommonschedulingaspects(such asclusterandGridscheduling,workloadanalysis,metrics,qualityofservice,and task scheduling), these papers increasingly discuss more recent problems and applications, such as virtualized environments, many-core processors, DNA - quencing, and Hadoop. This volume also includes a paper that summarizes Dan Tsafrir'sworkonunderstandingthe roleofuser estimates injob schedulingev- uations.

His insights, which were presented in this workshop'skeynote, are quite instructive and lead to the conclusion that accurate user estimates are indeed better fore?cientscheduling.Althoughthis conclusionmaysoundintuitive,itis actuallycontradictoryto previousstudies thatfound inaccurateestimates to - prove scheduler performance.

Following his analysis, Dan also suggests practical ways to deal with estimate inaccuracy for realistic job scheduler evaluations.