Obtaining the Knowledge of a Server Performance from Non-Intrusively Measurable Metrics
AbstractMost network services are provided by server computers. To provide these services with good quality, the server performance must be managed adequately. For the server management, the performance information is commonly obtained from the operating system (OS) and hardware of the managed computer. However, this method has a disadvantage. If the performance is degraded by excessive load or hardware faults, it becomes difficult to collect and transmit information. Thus, it is necessary to obtain the information without interfering with the server’s OS and hardware. This paper investigates a technique that utilizes non-intrusively measureable metrics that are obtained through passive traffic monitoring and electric currents monitored by the sensors attached to the power supply. However, these metrics do not directly represent the performance experienced by users. Hence, it is necessary to discover the complicated function that maps the metrics to the true performance information. To discover this function from the measured samples, a machine learning technique based on a decision tree is examined. The technique is important because it is applicable to the power management of server clusters and the immigration control of virtual servers.
S. Ohta and R. Andou, "WWW server load balancing technique employing passive measurement of server performance," ECTI Transactions on Electrical Engineering, Electronics, and Communications, vol. 8, pp. 59-66, Feb. 2010.
S. Ohta and T. Hirota, "Machine learning approach to the power management of server clusters," Proc. the 11th IEEE International Conference on Computer and Information Technology (CIT-2011), Conference Publishing Services, Aug. 2011, pp. 571-578.
S. Ohta and T. Hirota, "Power management of server clusters via machine learning and passive traffic measurement," Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications, vol. 3, no. 7, pp. 7-16, July 2013.
E. Pinheiro, R. Bianchini, E. V. Carrera, and T. Heath, "Load balancing and unbalancing for power and performance in cluster-based systems," Proc. Workshop on Compilers and Operating Systems for Low Power (COLP '01), Sept. 2001, pp. 4.1-4.8.
J. Xu and J. A. B. Fortes, "A Multi-objective approach to virtual machine management in datacenters," Proc. the 8th International Conference on Autonomic Computing (ICAC '11), ACM, June 2011, pp. 225-234.
D. Mosberger and T. Jin, "httperf – A tool for measuring web server performance," ACM SIGMETRICS Performance Evaluation Review, vol. 26, pp. 31-37, Dec. 1998.
M. Achour et al., "PHP Manual," http://php.net/manual/en/, May 2, 2016.
H. A. Kim and D. R. O’Hallaron, "Counting network flows in real time," Proc. IEEE 2003 Global Communications Conference (GLOBECOM 2003), IEEE, Dec. 2003, pp. 3888-3893.
M. S. Kim, Y. J. Won, H. J. Lee, J. W. Hong, and R. Boutaba, "Flow-based characteristic analysis of Internet application traffic," Proc. E2EMON, IFIP, Oct. 2004, pp. 62-67.
C. Estan, G. Varghese, and M. Fisk, "Bitmap algorithms for counting active flows on high speed links," Proc. the 3rd ACM SIGCOMM Conference on Internet Measurement (IMC '03), ACM, Oct. 2003, pp. 153-166.
S. Zhu and S. Ohta, "Real-time flow counting in IP networks: strict analysis and design issues," Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications, vol. 2, no. 2, pp. 7-17, Feb. 2012.
K. Y. Whang, B. T. Vander-Zanden, and H. M. Taylor, "A linear-time probabilistic counting algorithm for database applications," ACM Transactions on Database Systems, vol. 15, pp. 208-229, June 1990.
P. Pradhan, R. Tewari, S. Sahu, A. Chandra, and P. Shenoy, "An observation-based approach towards self-managing web server," Proc. the 10th International Workshop on Quality of Service (IWQoS 2002), IEEE, May 2002, pp. 13-20.
C. H. Tsai, K. G. Shin, J. Reumann, and S. Singhal, "Online web cluster capacity estimation and its application to energy conservation," IEEE Transactions on Parallel and Distributed Systems, vol. 18, pp. 932-945, July 2007.
S. Marsland, Machine learning: an algorithmic perspective, Boca Raton, Fl: Chapman and Hall/CRC, 2009.
N. B. Amor, S. Benferhat, and Z. Elouedi, "Naive Bayes vs decision trees in intrusion detection systems," Proc. 9th Annual ACM Symposium on Applied Computing (SAC '04), ACM, Mar. 2004, pp. 420-424.
W. Li and A. W. Moore, "A machine learning approach for efficient traffic classification," Proc. 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS'07), IEEE, Oct. 2007, pp. 310-317.
S. Ohta, R. Kurebayashi, and K. Kobayashi, "Minimizing false positives of a decision tree classifier for intrusion detection on the Internet," Journal of Network and Systems Management, vol. 16, pp. 399-419, Dec. 2008.
T. Hayashi and S. Ohta, "Performance degradation detection of virtual machines via passive measurement and machine learning," International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS), vol. 5, pp. 40-56, Apr. 2014.
J. R. Quinlan, C4.5: programs for machine learning, San Mateo, Ca: Morgan Kaufmann, 1993.
Tcpdump & libpcap, "Official web site of tcpdump," http://www.tcpdump.org/, May 2, 2016.
Akamai, "Press Release November 6, 2006," http://www.akamai.com/html/about/press/releases/2006/press_110606.html, Nov. 16, 2011.
FormFactors.org, "ATX Specification," http://www.formfactors.org/developer/specs/atx2_2.pdf, May 2, 2016.
Arduino Project, "Arduino Home Page," http://www.arduino.cc/, May 2, 2016.
D. Callaway, "Lookbusy - a synthetic load generator," http://www.devin.com/lookbusy/, May 2, 2016.
A. Waterland, "Stress project page," http://people.seas.harvard.edu/~apw/stress/, May 2, 2016.
Copyright (c) 2016 International Journal of Engineering and Technology Innovation
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Submission of a manuscript implies: that the work described has not been published before that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication. Authors can retain copyright in their articles with no restrictions. Also, author can post the final, peer-reviewed manuscript version (postprint) to any repository or website.
Since Jan. 01, 2019, IJETI will publish new articles with Creative Commons Attribution Non-Commercial License, under Creative Commons Attribution Non-Commercial 4.0 International (CC BY-NC 4.0) License.
The Creative Commons Attribution Non-Commercial (CC-BY-NC) License permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.