NDLI: CHAOS: Composable Highly Accurate OS-based power models

Content Provider	IEEE Xplore Digital Library
Author	Davis, J.D. Rivoire, S. Goldszmidt, M. Ardestani, E.K.
Copyright Year	2012
Description	Author affiliation: Dept. of Computer Engineering, University of California Santa Cruz, USA (Ardestani, E.K.) \|\| Microsoft Research, Silicon Valley, USA (Davis, J.D.; Goldszmidt, M.) \|\| Dept. of Computer Science, Sonoma State University, USA (Rivoire, S.)
Abstract	Models of computers' power consumption enable a variety of energy-efficiency optimizations and reduce data center instrumentation costs. In this paper, we present Composable, Highly Accurate, OS-based (CHAOS) full-system power models for machines and clusters. CHAOS models, which use high-level OS performance counters, yield highly accurate predictions without the intrusiveness and portability problems of hardware counters or board-level instrumentation. Furthermore, they are automatically generated by a low-overhead software framework (less than 1% CPU utilization on a mobile-class processor). We evaluate CHAOS models using MapReduce-style workloads, executed on server-class systems as well as energy-efficient low-power desktops, laptops, and embedded systems. We also generate and validate a generic, cross-platform feature set for cluster power models. To facilitate comparisons across different models and platforms, we define a metric called Dynamic Range Error (DRE) to describe how well the model characterizes the dynamic system behavior. Using this metric, we quantify the tradeoffs between model complexity and accuracy for different workloads. Our results show that the generic cross-platform feature set degrades prediction accuracy by at most 1% DRE compared to power models using the best cluster-specific feature set. To the best of our knowledge, this is the most complete study of system power modeling covering such a wide variety of platforms, workloads, and models.
Starting Page	153
Ending Page	163
File Size	1070831
Page Count	11
File Format	PDF
ISBN	9781467345316
e-ISBN	9781467345323
DOI	10.1109/IISWC.2012.6402920
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radiation detectors Predictive models Computational modeling Clustering algorithms Servers Biological system modeling Measurement
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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