More Web Proxy on the site http://driver.im/

Article

Modeling the relative fitness of storage

Authors:

Michael P. Mesnier,

Raja R. Sambasivan,

Alice X. Zheng,

Gregory R. GangerAuthors Info & Claims

SIGMETRICS '07: Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems

Pages 37 - 48

https://doi.org/10.1145/1254882.1254887

Published: 12 June 2007 Publication History

Abstract

Relative fitness is a new black-box approach to modeling the performance of storage devices. In contrast with an absolute model that predicts the performance of a workload on a given storage device, a relative fitness model predicts performance differences between a pair of devices. There are two primary advantages to this approach. First, because are lative fitness model is constructed for a device pair, the application-device feedback of a closed workload can be captured (e.g., how the I/O arrival rate changes as the workload moves from device A to device B). Second, a relative fitness model allows performance and resource utilization to be used in place of workload characteristics. This is beneficial when workload characteristics are difficult to obtain or concisely express (e.g., rather than describe the spatio-temporal characteristics of a workload, one could use the observed cache behavior of device A to help predict the performance of B.

This paper describes the steps necessary to build a relative fitness model, with an approach that is general enough to be used with any black-box modeling technique. We compare relative fitness models and absolute models across a variety of workloads and storage devices. On average, relative fitness models predict bandwidth and throughput within 10-20% and can reduce prediction error by as much as a factor of two when compared to absolute models.

References

[1]

G. A. Alvarez, J. Wilkes, E. Borowsky, S. Go, T. H. Romer, R. Becker-Szendy, R. Golding, A. Merchant, M. Spasojevic, and A. Veitch. Minerva: an automated resource provisioning tool for large-scale storage systems. ACM Transactions on Computer Systems, 19(4):483--518. ACM, November 2001.

Digital Library

[2]

E. Anderson. Simple table-based modeling of storage devices. SSP Technical Report HPL-SSP-2001-4. HP Laboratories, July 2001.

[3]

E. Anderson, M. Hobbs, K. Keeton, S. Spence, M. Uysal, and A. Veitch. Hippodrome: running circles around storage administration. Conference on File and Storage Technologies (Monterey, CA, 28-30 January 2002), pages 175--188. USENIX Association, 2002.

Digital Library

[4]

N. Appliance. PostMark: A New File System Benchmark. http://www.netapp.com.

[5]

E. Borowsky, R. Golding, A. Merchant, L. Schreier, E. Shriver, M. Spasojevic, and J. Wilkes. Using attribute-managed storage to achieve QoS. International Workshop on Quality of Service (Pittsburgh, PA, 21-23 March 1997). IFIP, 1997.

[6]

L. Breiman, J. H.Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth.

[7]

M. J. Carey, D. J. DeWitt, M. J. Franklin, N. E. Hall, M. L. McAuliffe, J. F. Naughton, D. T. Schuh, M. H. Solomon, C. K. Tan, O. G. Tsatalos, S. J. White, and M. J. Zwilling. Shoring up persistent applications. ACM SIGMOD International Conference on Management of Data (Minneapolis, MN, 24-27 May 1994). Published as SIGMOD Record, 23(2):383--394. ACM Press, 1994.

Digital Library

[8]

D. J. Futuyma. Evolutionary Biology. Third edition. SUNY, Stony Brook. Sinauer. December 1998.

[9]

G. R. Ganger. Generating representative synthetic workloads: an unsolved problem. International Conference on Management and Performance Evaluation of Computer Systems (Nashville, TN), pages 1263--1269, 1995.

[10]

G. R. Ganger and Y. N. Patt. Using system-level models to evaluate I/O subsystem designs. IEEE Transactions on Computers, 47(6):667--678, June 1998.

Digital Library

[11]

G. R. Ganger, J. D. Strunk, and A. J. Klosterman. Self-Storage: brick-based storage with automated administration. Technical Report CMU-CS-03-178. Carnegie Mellon University, August 2003.

[12]

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer Verlag. 2001.

[13]

Intel. iSCSI. www.sourceforge.net/projects/intel-iscsi.

[14]

T. Kelly, I. Cohen, M. Goldszmidt, and K. Keeton. Inducing models of black-box storage arrays. Technical report HPL-2004-108. HP, June 2004.

[15]

Z. Kurmas and K. Keeton. Using the distiller to direct the development of self-configuration software. International Conference on Autonomic Computing (New York, NY, 17-18 May 2004), pages 172--179. IEEE, 2004.

Digital Library

[16]

Z. Kurmas, K. Keeton, and K. Mackenzie. Synthesizing representative I/O workloads using iterative distillation. International Workshop on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (Orlando, FL, 12-15 October 2003). IEEE/ACM, 2003.

[17]

A. Merchant and P. S. Yu. Analytic modeling of clustered RAID with mapping based on nearly random permutation. IEEE Transactions on Computers, 45(3):367--373, March 1996.

Digital Library

[18]

T. M. Mitchell. Machine Learning. McGraw-Hill, 1997.

Digital Library

[19]

F. I. Popovici, A. C. A. Dusseau, and R. H. A. Dusseau. Robust, portable I/O scheduling with the disk mimic. USENIX Annual Technical Conference (San Antonio, TX, 09-14 June 2003), pages 297--310. IEEE, 2003.

[20]

C. Ruemmler and J. Wilkes. An introduction to disk drive modeling. IEEE Computer, 27(3):17--28, March 1994.

Digital Library

[21]

J. Satran. iSCSI. http://www.ietf.org/rfc/rfc3720.txt.

[22]

E. Shriver, A. Merchant, and J. Wilkes. An analytic behavior model for disk drives with readahead caches and request reordering. ACM SIGMETRICS Conference on Measurement and Modeling of Computer Systems (Madison, WI, 22-26 June 1999). Published as ACM SIGMETRICS Performance Evaluation Review, 26(1):182--191. ACM Press, 1990.

Digital Library

[23]

Transaction Processing Performance Council. TPC Benchmark C. http://www.tpc.org/tpcc.

[24]

M. Uysal, G. A. Alvarez, and A. Merchant. A modular, analytical throughput model for modern disk arrays. International Workshop on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (Cincinnati, OH, 15-18 August 2001), pages 183--192. IEEE, 2001.

Digital Library

[25]

E. Varki, A. Merchant, J. Xu, and X. Qiu. Issues and challenges in the performance analysis of real disk arrays. Transactions on Parallel and Distributed Systems, 15(6):559--574. IEEE, June 2004.

Digital Library

[26]

M. Wang, A. Ailamaki, and C. Faloutsos. Capturing the spatio-temporal behavior of real traffic data. IFIP WG 7.3 Symposium on Computer Performance (Rome, Italy, 23-27 September 2002). Published as Performance Evaluation, 49(1-4):147--163, 2002.

Digital Library

[27]

M. Wang, K. Au, A. Ailamaki, A. Brockwell, C. Faloutsos, and G. R. Ganger. Storage device performance prediction with CART models. International Workshop on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (Volendam, The Netherlands, 05-07 October 2004), pages 588--595. IEEE/ACM, 2004.

Digital Library

[28]

M. Wang, T. Madhyastha, N. H. Chan, S. Papadimitriou, and C. Faloutsos. Data mining meets performance evaluation: fast algorithms for modeling bursty traffic. International Conference on Data Engineering (San Jose, CA, 26-01 March 2002), pages 507--516. IEEE, 2002.

Digital Library

Cited By

Kadiyala SAlam MShrivastava YPatranabis SAbbas MBiswas AMukhopadhyay DSrikanthan T(2020)LAMBDAACM Transactions on Embedded Computing Systems10.1145/339085519:4(1-31)Online publication date: 21-Jun-2020
https://dl.acm.org/doi/10.1145/3390855
Zhao ZBarijough KGerstlauer A(2020)Network-level Design Space Exploration of Resource-constrained Networks-of-SystemsACM Transactions on Embedded Computing Systems10.1145/338791819:4(1-26)Online publication date: 21-Jun-2020
https://dl.acm.org/doi/10.1145/3387918
Hou BChen F(2018)Pacaca: Mining Object Correlations and Parallelism for Enhancing User Experience with Cloud Storage2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)10.1109/MASCOTS.2018.00036(293-305)Online publication date: Sep-2018
https://doi.org/10.1109/MASCOTS.2018.00036
Show More Cited By

Index Terms

Modeling the relative fitness of storage
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
2. Software and its engineering
  1. Software creation and management
    1. Software development process management
      1. Software development methods

Recommendations

Relative fitness models for storage
Design, implementation, and performance of storage systems

Relative fitness is a new black-box approach to modeling storage devices. Whereas conventional black-box models train to predict a device's performance given "device-independent" workload characteristics, relative fitness models learn to predict the ...
Modeling the relative fitness of storage
SIGMETRICS '07 Conference Proceedings

Relative fitness is a new black-box approach to modeling the performance of storage devices. In contrast with an absolute model that predicts the performance of a workload on a given storage device, a relative fitness model predicts performance ...
On modeling the relative fitness of storage

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGMETRICS '07: Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems

June 2007

398 pages

ISBN:9781595936394

DOI:10.1145/1254882

General Chair:
Leana Golubchik
University of Southern California, USA
,
Program Chairs:
Mostafa Ammar
Georgia Institute of Technology, USA
,
Mor Harchol-Balter
Carnegie Mellon University, USA

ACM SIGMETRICS Performance Evaluation Review Volume 35, Issue 1
SIGMETRICS '07 Conference Proceedings
June 2007
382 pages
ISSN:0163-5999
DOI:10.1145/1269899
Issue’s Table of Contents

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 June 2007

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

SIGMETRICS07

Sponsor:

SIGMETRICS07: ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

June 12 - 16, 2007

California, San Diego, USA

Acceptance Rates

Overall Acceptance Rate 459 of 2,691 submissions, 17%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

82
Total Citations
View Citations
748
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)0

Reflects downloads up to 02 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Kadiyala SAlam MShrivastava YPatranabis SAbbas MBiswas AMukhopadhyay DSrikanthan T(2020)LAMBDAACM Transactions on Embedded Computing Systems10.1145/339085519:4(1-31)Online publication date: 21-Jun-2020
https://dl.acm.org/doi/10.1145/3390855
Zhao ZBarijough KGerstlauer A(2020)Network-level Design Space Exploration of Resource-constrained Networks-of-SystemsACM Transactions on Embedded Computing Systems10.1145/338791819:4(1-26)Online publication date: 21-Jun-2020
https://dl.acm.org/doi/10.1145/3387918
Hou BChen F(2018)Pacaca: Mining Object Correlations and Parallelism for Enhancing User Experience with Cloud Storage2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)10.1109/MASCOTS.2018.00036(293-305)Online publication date: Sep-2018
https://doi.org/10.1109/MASCOTS.2018.00036
Singhal RNambiar MDesai BToyama MBernardino J(2016)Predicting SQL Query Execution Time for Large Data VolumeProceedings of the 20th International Database Engineering & Applications Symposium10.1145/2938503.2938552(378-385)Online publication date: 11-Jul-2016
https://dl.acm.org/doi/10.1145/2938503.2938552
Chen FHou BLee R(2016)Internal Parallelism of Flash Memory-Based Solid-State DrivesACM Transactions on Storage10.1145/281837612:3(1-39)Online publication date: 12-May-2016
https://dl.acm.org/doi/10.1145/2818376
Hsu CPanta RRa MFreeh V(2016)Inside-Out: Reliable Performance Prediction for Distributed Storage Systems in the Cloud2016 IEEE 35th Symposium on Reliable Distributed Systems (SRDS)10.1109/SRDS.2016.025(127-136)Online publication date: Sep-2016
https://doi.org/10.1109/SRDS.2016.025
Thamsen LVerbitskiy ISchmidt FRenner TKao O(2016)Selecting resources for distributed dataflow systems according to runtime targets2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC)10.1109/PCCC.2016.7820629(1-8)Online publication date: Dec-2016
https://doi.org/10.1109/PCCC.2016.7820629
Deshpande SDheenadayalan KSrinivasaraghavan GMuralidhara V(2016)Filer Response Time Prediction Using Adaptively-Learned Forecasting Models Based on Counter Time Series Data2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA.2016.0012(13-18)Online publication date: Dec-2016
https://doi.org/10.1109/ICMLA.2016.0012
Basak JBharde M(2015)Dynamic provisioning of storage workloadsProceedings of the 29th Usenix Conference on Large Installation System Administration10.5555/2907890.2907892(13-24)Online publication date: 8-Nov-2015
https://dl.acm.org/doi/10.5555/2907890.2907892
Jidong Zhai Mingliang Liu Ye Jin Xiaosong Ma Wenguang Chen (2015)Automatic Cloud I/O Configurator for I/O Intensive Parallel ApplicationsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2014.237827726:12(3275-3288)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1109/TPDS.2014.2378277
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten