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Advisor(s)
Abstract(s)
Although reduction in operating costs remains to be a
key motivation for migration to Cloud environments, Power
consumption is a big concern for data centers and cloud service
providers. Many big data applications execute on Hadoop
MapReduce framework for processing large workloads. In this
paper, we investigate the tradeoff between energy consumption
and workload running on Hadoop clusters using multiple virtual
machines. We characterize power consumption profiles for
various data intensive workloads and correlate these to quality of
service (QoS) metrics such as job execution time. Based on
experiments, we ascertain that power consumption profiles for big
data applications can be used to optimize energy efficiency in data
centers. We infer that these profiles can be used by Cloud service
providers and consumers to specify green metrics in Service Level
Agreements (SLA).
Description
IEEE International Conference on Computer Communications (INFOCOM 2017). 1 to 4, May, 2017, Workshop Big Data and Cloud Performance. Atlanta, U.S.A..
Keywords
MapReduce Energy efficiency Virtual Hadoop clusters Power consumption