TY - GEN
T1 - A Periodic Portfolio Scheduler for Scientific Computing in the Data Center
AU - Deng, Kefeng
AU - Verboon, Ruben
AU - Ren, Kaijun
AU - Iosup, Alexandru
PY - 2013
Y1 - 2013
N2 - The popularity of data centers in scientific computing has led to new architectures, new workload structures, and growing customer-bases. As a consequence, the selection of efficient scheduling algorithms for the data center is an increasingly costlier and more difficult challenge. To address this challenge, and contrasting previous work on scheduling for scientific workloads, we focus in this work on portfolio scheduling-here, the dynamic selection and use of a scheduling policy, depending on the current system and workload conditions, from a portfolio of multiple policies. We design a periodic portfolio scheduler for the workload of the entire data center, and equip it with a portfolio of resource provisioning and allocation policies. Through simulation based on real and synthetic workload traces, we show evidence that portfolio scheduling can automatically select the scheduling policy to match both user and data center objectives, and that portfolio scheduling can perform well in the data center, relative to its constituent policies. © 2014 Springer-Verlag.
AB - The popularity of data centers in scientific computing has led to new architectures, new workload structures, and growing customer-bases. As a consequence, the selection of efficient scheduling algorithms for the data center is an increasingly costlier and more difficult challenge. To address this challenge, and contrasting previous work on scheduling for scientific workloads, we focus in this work on portfolio scheduling-here, the dynamic selection and use of a scheduling policy, depending on the current system and workload conditions, from a portfolio of multiple policies. We design a periodic portfolio scheduler for the workload of the entire data center, and equip it with a portfolio of resource provisioning and allocation policies. Through simulation based on real and synthetic workload traces, we show evidence that portfolio scheduling can automatically select the scheduling policy to match both user and data center objectives, and that portfolio scheduling can perform well in the data center, relative to its constituent policies. © 2014 Springer-Verlag.
KW - Data center
KW - Portfolio scheduling
KW - Provisioning and allocation
KW - Scheduling policies
KW - Scientific workloads
UR - http://www.scopus.com/inward/record.url?scp=84903618201&partnerID=8YFLogxK
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U2 - 10.1007/978-3-662-43779-7_9
DO - 10.1007/978-3-662-43779-7_9
M3 - Conference contribution
SN - 9783662437780
VL - 8429 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 156
EP - 176
BT - Job Scheduling Strategies for Parallel Processing - 17th International Workshop, JSSPP 2013, Boston, MA, USA, May 24, 2013 Revised Selected Papers
PB - Springer/Verlag
T2 - 17th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2013
Y2 - 24 May 2014 through 24 May 2014
ER -