Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/86197
Title: Can we speculate running application with server power consumption trace?
Authors: Li, Yuanlong
Hu, Han
Wen, Yonggang
Zhang, Jun
Keywords: DRNTU::Engineering::Computer science and engineering
Time Series Classification
Time Warping
Issue Date: 2018
Source: Li, Y., Hu, H., Wen, Y., & Zhang, J. (2018). Can We Speculate Running Application With Server Power Consumption Trace?. IEEE Transactions on Cybernetics, 48(5), 1500-1512. doi:10.1109/TCYB.2017.2703941
Series/Report no.: IEEE Transactions on Cybernetics
Abstract: In this paper, we propose to detect the running applications in a server by classifying the observed power consumption series for the purpose of data center energy consumption monitoring and analysis. Time series classification problem has been extensively studied with various distance measurements developed; also recently the deep learning-based sequence models have been proved to be promising. In this paper, we propose a novel distance measurement and build a time series classification algorithm hybridizing nearest neighbor and long short term memory (LSTM) neural network. More specifically, first we propose a new distance measurement termed as local time warping (LTW), which utilizes a user-specified index set for local warping, and is designed to be noncommutative and nondynamic programming. Second, we hybridize the 1-nearest neighbor (1NN)-LTW and LSTM together. In particular, we combine the prediction probability vector of 1NN-LTW and LSTM to determine the label of the test cases. Finally, using the power consumption data from a real data center, we show that the proposed LTW can improve the classification accuracy of dynamic time warping (DTW) from about 84% to 90%. Our experimental results prove that the proposed LTW is competitive on our data set compared with existed DTW variants and its noncommutative feature is indeed beneficial. We also test a linear version of LTW and find out that it can perform similar to state-of-the-art DTW-based method while it runs as fast as the linear runtime lower bound methods like LB_Keogh for our problem. With the hybrid algorithm, for the power series classification task we achieve an accuracy up to about 93%. Our research can inspire more studies on time series distance measurement and the hybrid of the deep learning models with other traditional models.
URI: https://hdl.handle.net/10356/86197
http://hdl.handle.net/10220/48300
ISSN: 2168-2267
DOI: http://dx.doi.org/10.1109/TCYB.2017.2703941
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TCYB.2017.2703941.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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