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Reseach Article

BMC based Data Logger for Performance Analysis

by Nikhilesh Nayak, Dheerendra Singh Tomar, Shanmugasundaram M.
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 9
Year of Publication: 2017
Authors: Nikhilesh Nayak, Dheerendra Singh Tomar, Shanmugasundaram M.
10.5120/ijais2017451732

Nikhilesh Nayak, Dheerendra Singh Tomar, Shanmugasundaram M. . BMC based Data Logger for Performance Analysis. International Journal of Applied Information Systems. 12, 9 ( Dec 2017), 37-40. DOI=10.5120/ijais2017451732

@article{ 10.5120/ijais2017451732,
author = { Nikhilesh Nayak, Dheerendra Singh Tomar, Shanmugasundaram M. },
title = { BMC based Data Logger for Performance Analysis },
journal = { International Journal of Applied Information Systems },
issue_date = { Dec 2017 },
volume = { 12 },
number = { 9 },
month = { Dec },
year = { 2017 },
issn = { 2249-0868 },
pages = { 37-40 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number9/1017-2017451732/ },
doi = { 10.5120/ijais2017451732 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:08:38.543077+05:30
%A Nikhilesh Nayak
%A Dheerendra Singh Tomar
%A Shanmugasundaram M.
%T BMC based Data Logger for Performance Analysis
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 9
%P 37-40
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the present scenario, a server rack has multiple platforms attached to it, each designed to perform a different set of actions, thus, having different hardware requirements. To increase the throughput of such a platform either the hardware requirements are multiplied or the platform is replaced completely. This unoptimized method is rather expensive and inefficient. [1] This paper focuses on improving the performance of a system by providing accurate analysis and predict hardware requirements to improve overall throughput. For this, data logs are collected over a period of time which take performance data dumps of sensors connected to the platform via BMC. These sensors monitor the platform and measure its internal physical parameters. This data is then used to create a database and a training set. This set is used to train a machine learning algorithm which gives an efficient algorithm to analyze the present performance and give accurate prediction. This gives an optimal solution to increase throughput of a platform. [2]

References
  1. Intel Corporation (2012). Intel Server Boards and Server Platforms Server Management Guide. pp.1-7, 68-100.
  2. Intel Corporation (2017). Intel® 64 and IA-32 Architectures Optimization Reference Manual. 1st ed. pp.15-76.
  3. GitHub | OpenBMC. (2015). openbmc/facebook. [online] Available at: https://github.com/facebook/openbmc [Accessed 27 Oct. 2017].
  4. Lwn.net. (2017). OpenBMC, a distribution for baseboard management controllers [LWN.net]. [online] Available at: https://lwn.net/Articles/683320/ [Accessed 23 Oct. 2017].
  5. Miniard, C. and Montavista Software (2006). IPMI – A Gentle Introduction with OpenIPMI. 1st ed. pp.9-24, 89-94,115-143.
  6. Intel Corporation, Hewlett-Packard Company, NEC Corporation and Dell Inc. (2013). Intelligent Platform Management Interface Specification Second Generation. 2nd ed. pp.12-26, 207-215, 419-447.
  7. TensorFlow. (2017). TensorFlow Linear Model Tutorial. [online] Available at: https://www.tensorflow.org/tutorials/wide [Accessed 1 Nov. 2017].
  8. The Practical Dev. (2017). Design Philosophy - Introduction to Tensorflow Part 1. [online] Available at: https://dev.to/kasperfred/design-philosophy-of-tensorflow---introduction-to-tensorflow-part-1-ajp [Accessed 20 Sep. 2017].
  9. Intel Corporation (2007). Intel 64 and IA-32 Architectures Software Developer's Manual, Volume 3A. Intel Corporation. August. pp. 14–1
  10. R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, Machine learning: An artificial intelligence approach. Springer Science & Business Media, 2013.
  11. Agarwal, P. (2016). Machine Learning Toolbox. Machine Learning and Applications: An International Journal, 3(3), pp.25-34.
  12. Smola, A. and Vishwanathan, S. (2010). Introduction to Machine Learning. The Press Syndicate of the University of Cambridge, pp.165-194.
  13. Keras.io. (2017). Keras Documentation. [online] Available at: https://keras.io/#keras-the-python-deep-learning-library [Accessed 20 Oct. 2017].
  14. Towards Data Science. (2017). PyTorch vs TensorFlow?—?spotting the difference – Towards Data Science. [online] Available at: https://towardsdatascience.com/pytorch-vs-tensorflow-spotting-the-difference-25c75777377b [Accessed 18 Oct. 2017].
  15. Brownlee, J. (2017). Logistic Regression for Machine Learning - Machine Learning Mastery. [online] Machine Learning Mastery. Available at: https://machinelearningmastery.com/logistic-regression-for-machine-learning/ [Accessed 2 Nov. 2017].
  16. TensorFlow. (2017). Getting Started With TensorFlow | TensorFlow. [online] Available at: https://www.tensorflow.org/get_started/get_started [Accessed 8 Oct. 2017].
Index Terms

Computer Science
Information Sciences

Keywords

Data logger sensors python IPMI BMC machine learning database and performance