Server Workload Model Identification: Monitoring and Control Tools for Linux

Authors

DOI:

https://doi.org/10.26636/jtit.2016.2.713

Keywords:

cloud computing, energy efficiency, Linux, server performance metering

Abstract

Server power control in data centers is a coordinated process carefully designed to reach multiple data center management objectives. The main objectives include avoiding power capacity overloads and system overheating, as well as fulfilling service-level agreements (SLAs). In addition to the primary goals, server control process aims to maximize various energy efficiency metrics subject to reliability constraints. Monitoring of data center performance is fundamental for its efficient management. In order to keep track of how well the computing tasks are processed, cluster control systems need to collect accurate measurements of activities of cluster components. This paper presents a brief overview of performance and power consumption monitoring tools available in the Linux systems.

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References

[1] J. Koomey, Growth in Data Center Electricity Use 2005 to 2010. Oakland, CA: Analytical Press, 2011.
View in Google Scholar

[2] B. Subramaniam and W.-C. Feng, “Towards energy-proportional computing for enterprise-class server workloads”, in Proc. 4th ACM/SPEC Int. Conf. Perform. Engin. ICPE 2013, Prague, Czech Republic, 2013, pp. 15–26.
View in Google Scholar

[3] J. Dongarra et al., “The international exascale software project roadmap”, Int. J. High Perform. Comput. Appl., vol. 25, no. 1, pp. 3–60, 2011.
View in Google Scholar

[4] S.-Y. Jing, S. Ali, K. She, and Y. Zhong, “State-of-the-art research study for green cloud computing”, The J. of Supercomput., vol. 65, no. 1, pp. 445–468, 2013.
View in Google Scholar

[5] V. K. Vavilapalli et al., “Apache Hadoop YARN: yet another resource negotiator”, in Proc. 4th Ann. Symp. on Cloud Comput. SOCC’13, Santa Clara, CA, USA, 2013, pp. 5:1–5:16.
View in Google Scholar

[6] S. Jha, J. Qiu, A. Luckow, P. Mantha, and G. C. Fox, “A tale of two data-intensive paradigms: applications, abstractions, and architectures”, in Proc. 3rd IEEE Int. Congr. on Big Data BigData 2014, Anchorage, AK, USA, 2014, pp. 645–652.
View in Google Scholar

[7] Y. Georgiou et al., “Energy accounting and control with SLURM resource and job management system”, in Distributed Computing and Networking, M. Chatterjee et al., Eds., LNCS, vol. 8314, pp. 96–118. Springer, 2014.
View in Google Scholar

[8] E. Niewiadomska-Szynkiewicz et al., “Dynamic power management in energy-aware computer networks and data intensive computing systems”, Future Gener. Comp. Syst., vol. 37, pp. 284–296, 2014 (doi: 10.1016/j.future.2013.10.002).
View in Google Scholar

[9] M. P. Karpowicz, P. Arabas, and E. Niewiadomska-Szynkiewicz, “Energy-aware multilevel control system for a network of Linux software routers: design and implementation”, IEEE Syst. J., vol. PP, no. 99, pp. 1–12, 2015 (doi: 10.1109/JSYST.2015.2489244).
View in Google Scholar

[10] P. Jaskóła, P. Arabas, and A. Karbowski, “Simultaneous routing and flow rate optimization in energy–aware computer networks”, Int. J. Appl. Mathem. & Comp. Sci., vol. 26, no. 1, pp. 231–243, 2016.
View in Google Scholar

[11] A. Karbowski and P. Jaskóła, “Two approaches to dynamic power management in energy-aware computer networks – methodological considerations”, in Proc. of Feder. Conf. Comp. Science and Inform. Syst. FedCSIS 2015, Łódź, Poland, 2015, vol. 5, pp. 1177–1182.
View in Google Scholar

[12] ACPI Specification Document [Online]. Available: http://www.acpi.info
View in Google Scholar

[13] J. H. Laros III et al., “High performance computing-power application programming interface specification”, Tech. Rep. SAND2014-17061, Sandia National Laboratories, 2014.
View in Google Scholar

[14] D. DeBonis et al., “A power API for the HPC community”, Sandia Report SAND2014-17061, Sandia National Laboratories, 2014.
View in Google Scholar

[15] R. Bolla et al., “Green Abstraction Layer (GAL): power management capabilities of the future energy telecommunication fixed network nodes”, Techn. Rep. ES 203 237, ETSI, 2014.
View in Google Scholar

[16] C. Lefurgy et al., “Energy management for commercial servers”, Computer, vol. 36, no. 12, pp. 39–48, 2003.
View in Google Scholar

[17] L. A. Barroso, J. Clidaras, and U. Hölzle, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, 2nd ed. Morgan & Claypool Publ., 2013.
View in Google Scholar

[18] L. Wang and S. U. Khan, “Review of performance metrics for green data centers: a taxonomy study”, The J. Supercomput., vol. 63, no. 3, pp. 639–656, 2013.
View in Google Scholar

[19] T. Mastelic et al., “Cloud computing: survey on energy efficiency”, ACM Comput. Surveys, vol. 47, no. 2, pp. 33:1–33:36, 2015.
View in Google Scholar

[20] B. Subramaniam, W. Saunders, T. Scogland, and W.-C. Feng, “Trends in energy-efficient computing: A perspective from the Green500”, in 4th Int. Green Comput. Conf. IGCC 2013, Arlington, VA, USA, 2013, pp. 1–8.
View in Google Scholar

[21] Standard Performance Evaluation Corporation (SPEC), SPEC Power and Performance Benchmark Methodology [Online]. Available: http://www.spec.org/power_ssj2008/
View in Google Scholar

[22] K.-D. Lange, “Identifying shades of green: The SPECpower benchmarks”, IEEE Computer, vol. 42, no. 3, pp. 95–97, 2009.
View in Google Scholar

[23] D. Molka, D. Hackenberg, R. Schöne, T. Minartz, and W. E. Nagel, “Flexible workload generation for HPC cluster efficiency benchmarking”, Comp. Science-Res. & Develop., vol. 27, no. 4, pp. 235–243, 2012.
View in Google Scholar

[24] J. J. Dongarra, P. Luszczek, and A. Petitet, “The LINPACK Benchmark: past, present and future”, Concurr. & Comput.: Practice and Experience, vol. 15, no. 9, pp. 803–820, 2003.
View in Google Scholar

[25] A. Iosup et al., “Performance analysis of cloud computing services for many-tasks scientific computing”, IEEE Trans. Parall. & Distrib. Syst., vol. 22, no. 6, pp. 931–945, 2011.
View in Google Scholar

[26] S. Bradner and J. McQuaid, “RFC 2544: Benchmarking methodology for network interconnect devices”, Mar. 1999.
View in Google Scholar

[27] P. Arabas and M. Karpowicz, “Server power consumption: measurements and modeling with MSRs”, in Challenges in Automation, Robotics and Measurement Techniques, R. Szewczyk, C. Zieliński, and M. Kaliczyńska, Eds., Advances in Intelligent Systems and Computing, vol. 440, pp. 233–244. Springer, 2016.
View in Google Scholar

[28] M. P. Karpowicz and P. Arabas, “Preliminary results on the Linux libpcap model identification”, in Proc. 20th Int. Conf. Methods & Models Autom. Robot. MMAR 2015, Międzyzdroje, Poland, 2015, pp. 1056–1061.
View in Google Scholar

[29] SNIA Emerald, SNIA Emerald Power Efficiency Measurement Specification [Online]. Available: http://www.snia.org
View in Google Scholar

[30] Storage Performance Council (SPC), Storage Performance Council SPC Benchmark 2/Energy Extension [Online]. Available: http://www.storageperformance.org
View in Google Scholar

[31] D. Hackenberg et al., “Power measurement techniques on standard compute nodes: A quantitative comparison”, in Proc. IEEE Int. Symp. on Perform. Anal. Syst. & Software ISPASS 2013, Austin, TX, USA, 2013, pp. 194–204.
View in Google Scholar

[32] J. Mair, D. Eyers, Z. Huang, and H. Zhang, “Myths in power estimation with performance monitoring counters”, Sustain. Comput.: Inform. & Syst., vol. 4, no. 2, pp. 83–93, 2014.
View in Google Scholar

[33] M. E. M. Diouri et al., “Assessing power monitoring approaches for energy and power analysis of computers”, Sustainable Comput.: Informatics and Syst., vol. 4, no. 2, pp. 68–82, 2014.
View in Google Scholar

[34] Intel Intelligent Power Node Manager [Online]. Available: http://www.intel.com
View in Google Scholar

[35] D. Hackenberg et al., “HDEEM: high definition energy efficiency monitoring”, in Proc. IEEE Energy Efficient Supercomput. Worksh. SC14, New Orleans, LA, USA, 2014, pp. 1–10.
View in Google Scholar

[36] M. F. Dolz, M. R. Heidari, M. Kuhn, T. Ludwig, and G. Fabregat, “ARDUPOWER: A low-cost wattmeter to improve energy efficiency of HPC applications”, in Proc. 6th Int. Green Comput. Conf. & Sustain. Comput. Conf. IGSC 2015, Las Vegas, NV, USA, 2015, pp. 1–8.
View in Google Scholar

[37] Intel IA-64 and IA-32 Architectures Software Developer’s Manual [Online]. Available: http://www.intel.com
View in Google Scholar

[38] NVML API Reference Manual, 2012 [Online]. Available: http://developer.nvidia.com
View in Google Scholar

[39] T. Ilsche, D. Hackenberg, S. Graul, R. Schöne, and J. Schuchart, “Power measurements for compute nodes: improving sampling rates, granularity and accuracy”, in Proc. 6th Int. Green Comput. Conf. and Sustain. Comput. Conf. IGSC 2015, Las Vegas, NV, USA, 2015.
View in Google Scholar

[40] V. M. Weaver et al., “Measuring energy and power with PAPI”, in Proc. 41st Int. Conf. Parallel Proces. Worksh. ICPPW 2012, Pittsburgh, PA, USA, 2012, pp. 262–268.
View in Google Scholar

[41] L. Taniça, A. Ilic, P. Tomás, and L. Sousa, “Schedmon: A performance and energy monitoring tool for modern multi-cores”, in Euro-Par 2014: Parallel Processing Workshops, LNCS, vol. 8806, pp. 230–241. Springer, 2014.
View in Google Scholar

[42] Performance Application Programming Interface (PAPI) [Online]. Available: http://icl.cs.utk.edu/papi
View in Google Scholar

[43] Unofficial Linux Perf. Events Performance Counter Web Page [Online]. Available: http://web.eece.maine.edu/~vweaver/projects/perf_events
View in Google Scholar

[44] M. Gerndt, E. César, and S. Benkner, Eds., Automatic Tuning of HPC Applications. Shaker Verlag, 2015.
View in Google Scholar

[45] M. P. Karpowicz, “Energy-efficient CPU frequency control for the Linux system”, Concur. & Computat.: Pract. & Exper., vol. 28, no. 2, pp. 420–437, 2016 (doi: 10.1002/cpe.3476).
View in Google Scholar

[46] D. Brandewie, “Intel P-state driver” [Online]. Available: http://www.kernel.org/doc/
View in Google Scholar

[47] M. P. Karpowicz, P. Arabas, and E. Niewiadomska-Szynkiewicz, “Design and implementation of energy-aware application-specific CPU frequency governors for the heterogeneous distributed computing systems”, Future Generation Computer Systems, available online, 2016 (doi: 10.1016/j.future.2016.05.011).
View in Google Scholar

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Submitted

2023-06-22

Published

2016-06-30

Issue

Section

RESEARCH ARTICLES

How to Cite

[1]
M. Karpowicz and P. Arabas, “Server Workload Model Identification: Monitoring and Control Tools for Linux”, JTIT, vol. 64, no. 2, pp. 5–12, Jun. 2016, doi: 10.26636/jtit.2016.2.713.

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