CloudSocket: Fine-Grained Power Sensing System for Datacenters

Today's data centers have various computing and storage devices for processing a myriad of data, and they generally consume a considerable amount of electrical energy. This paper proposes a smart grid-inspired methodology to observe and profile the power consumption of a data center. Based on t...

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Bibliographic Details
Published inIEEE access Vol. 6; pp. 49601 - 49610
Main Authors Lee, Seil, Kim, Hanjoo, Park, Seongsik, Kim, Seijoon, Choe, Hyeokjun, Yoon, Sungroh
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
cloud computing
Computation
Computer centers
Computer networks
Computer storage devices
Data centers
Datacenter
Machine learning
Monitoring
Power consumption
Power demand
Power management
Power measurement
Sensors
Servers
Smart grid
Smart grids
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Summary:Today's data centers have various computing and storage devices for processing a myriad of data, and they generally consume a considerable amount of electrical energy. This paper proposes a smart grid-inspired methodology to observe and profile the power consumption of a data center. Based on this technique, our paper provides information that is useful for moderating the peak power consumption of the data centers. Our power measurement platform consists of several devices named CloudSockets , and each CloudSocket unit can measure the power consumption of multiple computing nodes and periodically transmit measurement data wirelessly to the coordinator unit. This data can be used to analyze the relationship between the workload and the power consumption of the data center. We tested our methodology through the application of various algorithms with a 32-node distributed system that runs Apache Spark for large-scale data analytics. An analysis of our experimental results reveals how and where the peak power of each node in the grid overlaps, providing opportunities for informed coordination of the computing components for peak power reduction.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2868469