Title :
Scheduling Grid workloads on multicore clusters to minimize energy and maximize performance
Author :
Lammie, Michael ; Brenner, Paul ; Thain, Douglas
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA
Abstract :
Energy is a significant and growing component of the cost of running a large computing facility. A grid workload consisting of millions of jobs running on thousands of processors may consume millions of kilowatt hours of electricity. However, because a grid workload generally consists of many independent sequential processes, we may shape its execution to satisfy energy constraints. By varying the number and frequency of processors available, a scheduler may trade off energy against performance. In this paper, we explore energy and performance tradeoffs in the scheduling of grid workloads on large clusters. We build upon previous work by showing the interaction of intelligent job assignment, automated node scaling, and frequency scaling on multicore clusters. An unexpected result is that, even though low frequency is the most efficient mode of operating a single node, the careful application of frequency scaling can actually reduce overall energy consumption even further by reducing the number of nodes powered on.
Keywords :
energy consumption; grid computing; power engineering computing; scheduling; energy constraints; energy consumption; grid workloads; multicore clusters; scheduling; Clustering algorithms; Costs; Energy consumption; Energy management; Frequency; Grid computing; Machine intelligence; Multicore processing; Processor scheduling; Shape;
Conference_Titel :
Grid Computing, 2009 10th IEEE/ACM International Conference on
Conference_Location :
Banff, AB
Print_ISBN :
978-1-4244-5148-7
Electronic_ISBN :
978-1-4244-5149-4
DOI :
10.1109/GRID.2009.5353071