Analysis of Server Resource Consumption of Meteorological Satellite Application System Based on Contour Curve
DOI: 10.23977/acss.2018.21004 | Downloads: 26 | Views: 2865
Author(s)
Xiangang Zhao 1, Cunqun Fan 1, Manyun Lin 1, Lizi Xie 1, Xi Zhang 1
Affiliation(s)
1 National Satellite Meteorological Centre, Beijing, China
Corresponding Author
Cunqun FanABSTRACT
In order to effectively analyze the system server resource consumption, this paper proposed a server resource consumption analysis method based contour curve. Firstly, the definition of resource consumption analysis was given, and then several analysis methods of resource consumption were given, including: application resource consumption profile, application resource consumption profile standard deviation, application resource consumption frequency, application resource consumption monotonicity and so on. Finally, based on this, the total consumption, consumption mean, kurtosis, skewness, reload, resource load and so on were analyzed. The method proposed in this paper can effectively analyze the server resource consumption in depth, improve the server resource utilization and reduce the server hardware loss to a certain extent.
KEYWORDS
Meteorological satellite, Server resource, Consumption analysis, Contour curveCITE THIS PAPER
Xiangang, Z., Cunqun, F., Manyun, L., Lizi, X., Xi, Z., Analysis of Server Resource Consumption of Meteorological Satellite Application System Based on Contour Curve, Advances in Computer, Signals and Systems (2018) Vol. 2: 19-25.
REFERENCES
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[2] Li J, Narasayya V, Chaudhuri S. Robust estimation of resource consumption for SQL queries using statistical techniques[J]. Proceedings of the Vldb Endowment, 2012, 5(11):1555-1566.
[3] Dargie W, Schill A. Analysis of the Power and Hardware Resource Consumption of Servers under Different Load Balancing Policies[C]. IEEE, International Conference on Cloud Computing. 2012:772-778.
[4] Tziritas N, Khan S U, Xu C Z, et al. On minimizing the resource consumption of cloud applications using process migrations[J]. Journal of Parallel & Distributed Computing, 2013, 73(12):1690-1704.
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