2010
DOI: 10.14778/1920841.1920862
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Energy management for MapReduce clusters

Abstract: The area of cluster-level energy management has attracted significant research attention over the past few years. One class of techniques to reduce the energy consumption of clusters is to selectively power down nodes during periods of low utilization to increase energy efficiency. One can think of a number of ways of selectively powering down nodes, each with varying impact on the workload response time and overall energy consumption. Since the MapReduce framework is becoming "ubiquitous", the focus of this p… Show more

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Cited by 168 publications
(116 citation statements)
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“…However, there exist some studies that take into account both the energy consumption and the response time (e.g., [8], [9], [10]). In [8], two methods that target at the minimization of the energy consumption by activating or deactivating the nodes are presented. The main idea behind these methods is that, by deactivating some nodes, utilization and energy efficiency is improved thus leading to lower energy consumption.…”
Section: Related Workmentioning
confidence: 99%
See 4 more Smart Citations
“…However, there exist some studies that take into account both the energy consumption and the response time (e.g., [8], [9], [10]). In [8], two methods that target at the minimization of the energy consumption by activating or deactivating the nodes are presented. The main idea behind these methods is that, by deactivating some nodes, utilization and energy efficiency is improved thus leading to lower energy consumption.…”
Section: Related Workmentioning
confidence: 99%
“…The second method utilizes all the nodes in the cluster, and, upon the completion of task execution, powers down the nodes to avoid the high energy consumption at idle state. Several powered down states are examined in [8] from which hibernation seems the most efficient in terms of the trade-off between energy consumption and transition time (from online to offline state and vice versa). Our approach can be regarded as a hybrid of these two methods with the addition that we consider the case where high degrees of parallelism may not be beneficial.…”
Section: Related Workmentioning
confidence: 99%
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