Server Workload Model Identification: Monitoring and Control Tools for Linux

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.

[1]  Wolfgang E. Nagel,et al.  Power measurement techniques on standard compute nodes: A quantitative comparison , 2013, 2013 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS).

[2]  Yi Zhong,et al.  State-of-the-art research study for green cloud computing , 2011, The Journal of Supercomputing.

[3]  Michal P. Karpowicz,et al.  Energy‐efficient CPU frequency control for the Linux system , 2016, Concurr. Comput. Pract. Exp..

[4]  Lizhe Wang,et al.  Review of performance metrics for green data centers: a taxonomy study , 2011, The Journal of Supercomputing.

[5]  Shirley Moore,et al.  Measuring Energy and Power with PAPI , 2012, 2012 41st International Conference on Parallel Processing Workshops.

[6]  Green Abstraction Layer ( GAL ) ; Power management capabilities of the future energy telecommunication fixed network nodes , 2022 .

[7]  Thomas Ludwig,et al.  ARDUPOWER: A low-cost wattmeter to improve energy efficiency of HPC applications , 2015, 2015 Sixth International Green and Sustainable Computing Conference (IGSC).

[8]  Carlo Curino,et al.  Apache Hadoop YARN: yet another resource negotiator , 2013, SoCC.

[9]  Andrzej Karbowski,et al.  Two approaches to dynamic power management in energy-aware computer networks - methodological considerations , 2015, 2015 Federated Conference on Computer Science and Information Systems (FedCSIS).

[10]  Luiz André Barroso,et al.  The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, Second Edition , 2013, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, Second Edition.

[11]  Thomas Ilsche,et al.  Power measurements for compute nodes: Improving sampling rates, granularity and accuracy , 2015, 2015 Sixth International Green and Sustainable Computing Conference (IGSC).

[12]  Piotr Arabas,et al.  Preliminary results on the Linux libpcap model identification , 2015, 2015 20th International Conference on Methods and Models in Automation and Robotics (MMAR).

[13]  Wu-chun Feng,et al.  Towards energy-proportional computing for enterprise-class server workloads , 2013, ICPE '13.

[14]  Wolfgang E. Nagel,et al.  Flexible workload generation for HPC cluster efficiency benchmarking , 2012, Computer Science - Research and Development.

[15]  Andrzej Karbowski,et al.  Simultaneous routing and flow rate optimization in energy–aware computer networks , 2016, Int. J. Appl. Math. Comput. Sci..

[16]  Klaus-Dieter Lange,et al.  Identifying Shades of Green: The SPECpower Benchmarks , 2009, Computer.

[17]  Luiz André Barroso,et al.  The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines , 2009, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines.

[18]  Ewa Niewiadomska-Szynkiewicz,et al.  Design and implementation of energy-aware application-specific CPU frequency governors for the heterogeneous distributed computing systems , 2018, Future Gener. Comput. Syst..

[19]  Piotr Arabas,et al.  Server Power Consumption: Measurements and Modeling with MSRs , 2016, AUTOMATION.

[20]  Yiannis Georgiou,et al.  Energy Accounting and Control with SLURM Resource and Job Management System , 2014, ICDCN.

[21]  J. Koomey Worldwide electricity used in data centers , 2008 .

[22]  Scott O. Bradner,et al.  Benchmarking Methodology for Network Interconnect Devices , 1996, RFC.

[23]  John Shalf,et al.  The International Exascale Software Project roadmap , 2011, Int. J. High Perform. Comput. Appl..

[24]  Enrique S. Quintana-Ortí,et al.  Assessing Power Monitoring Approaches for Energy and Power Analysis of Computers , 2014, Sustain. Comput. Informatics Syst..

[25]  Wolfgang E. Nagel,et al.  HDEEM: High Definition Energy Efficiency Monitoring , 2014, 2014 Energy Efficient Supercomputing Workshop.

[26]  Stephen L. Olivier,et al.  High Performance Computing - Power Application Programming Interface Specification Version 1.1a , 2016 .

[27]  Athanasios V. Vasilakos,et al.  Cloud Computing , 2014, ACM Comput. Surv..

[28]  Pedro Tomás,et al.  SchedMon: A Performance and Energy Monitoring Tool for Modern Multi-cores , 2014, Euro-Par Workshops.

[29]  Judy Qiu,et al.  A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures , 2014, 2014 IEEE International Congress on Big Data.

[30]  Ewa Niewiadomska-Szynkiewicz,et al.  Energy-Aware Multilevel Control System for a Network of Linux Software Routers: Design and Implementation , 2018, IEEE Systems Journal.

[31]  Alexandru Iosup,et al.  Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing , 2011, IEEE Transactions on Parallel and Distributed Systems.

[32]  David Eyers,et al.  Myths in power estimation with Performance Monitoring Counters , 2014, Sustain. Comput. Informatics Syst..

[33]  Ewa Niewiadomska-Szynkiewicz,et al.  Dynamic power management in energy-aware computer networks and data intensive computing systems , 2014, Future Gener. Comput. Syst..

[34]  Karthick Rajamani,et al.  Energy Management for Commercial Servers , 2003, Computer.

[35]  Jack J. Dongarra,et al.  The LINPACK Benchmark: past, present and future , 2003, Concurr. Comput. Pract. Exp..