NWSLite: A general-purpose, nonparametric prediction utility for embedded systems

Time series-based prediction methods have a wide range of uses in embedded systems. Many OS algorithms and applications require accurate prediction of demand and supply of resources. However, configuring prediction algorithms is not easy, since the dynamics of the underlying data requires continuous observation of the prediction error and dynamic adaptation of the parameters to achieve high accuracy. Current prediction methods are either too costly to implement on resource-constrained devices or their parameterization is static, making them inappropriate and inaccurate for a wide range of datasets. This paper presents NWSLite, a prediction utility that addresses these shortcomings on resource-restricted platforms.

[1]  Chandra Krintz,et al.  Application-level prediction of battery dissipation , 2004, Proceedings of the 2004 International Symposium on Low Power Electronics and Design (IEEE Cat. No.04TH8758).

[2]  J. Flinn,et al.  Energy-aware adaptation for mobile applications , 1999, SOSP.

[3]  Richard Wolski,et al.  Representing Dynamic Performance Information in Grid Environments with the Network Weather Service , 2002, 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID'02).

[4]  Mahadev Satyanarayanan,et al.  Agile application-aware adaptation for mobility , 1997, SOSP.

[5]  Scott Shenker,et al.  Scheduling for reduced CPU energy , 1994, OSDI '94.

[6]  Anantha Chandrakasan,et al.  Dynamic voltage scheduling using adaptive filtering of workload traces , 2001, VLSI Design 2001. Fourteenth International Conference on VLSI Design.

[7]  Neil Spring,et al.  Application level scheduling of gene sequence comparison on metacomputers , 1998 .

[8]  Mahadev Satyanarayanan,et al.  Predictive Resource Management for Wearable Computing , 2003, MobiSys '03.

[9]  Geoffrey H. Kuenning,et al.  The remote processing framework for portable computer power saving , 1999, SAC '99.

[10]  Francine Berman,et al.  Overview of the Book: Grid Computing – Making the Global Infrastructure a Reality , 2003 .

[11]  Peter C. Young,et al.  Recursive Estimation and Time-Series Analysis: An Introduction , 1984 .

[12]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[13]  Paramvir Bahl,et al.  Characterizing user behavior and network performance in a public wireless LAN , 2002, SIGMETRICS '02.

[14]  Mahadev Satyanarayanan,et al.  Tactics-based remote execution for mobile computing , 2003, MobiSys '03.

[15]  Richard Wolski,et al.  The network weather service: a distributed resource performance forecasting service for metacomputing , 1999, Future Gener. Comput. Syst..

[16]  Jason Flinn,et al.  Energy-aware adaptation for mobile applications , 1999, SOSP.

[17]  Thomas D. Burd,et al.  The simulation and evaluation of dynamic voltage scaling algorithms , 1998, Proceedings. 1998 International Symposium on Low Power Electronics and Design (IEEE Cat. No.98TH8379).

[18]  Francine Berman,et al.  Application-Level Scheduling on Distributed Heterogeneous Networks , 1996, Proceedings of the 1996 ACM/IEEE Conference on Supercomputing.

[19]  Hal Wasserman,et al.  Comparing algorithm for dynamic speed-setting of a low-power CPU , 1995, MobiCom '95.

[20]  Richard Wolski,et al.  Experiences with predicting resource performance on-line in computational grid settings , 2003, PERV.

[21]  Philip Levis,et al.  Policies for dynamic clock scheduling , 2000, OSDI.

[22]  Chandra Krintz,et al.  ACE: a resource-aware adaptive compression environment , 2003, Proceedings ITCC 2003. International Conference on Information Technology: Coding and Computing.

[23]  Geoffrey H. Kuenning,et al.  Saving portable computer battery power through remote process execution , 1998, MOCO.

[24]  Gregory E. Bottomley,et al.  A novel approach for stabilizing recursive least squares filters , 1991, IEEE Trans. Signal Process..

[25]  Brian D. Noble,et al.  Mobile network estimation , 2001, MobiCom '01.

[26]  Mahadev Satyanarayanan,et al.  Balancing performance, energy, and quality in pervasive computing , 2002, Proceedings 22nd International Conference on Distributed Computing Systems.

[27]  Todd M. Austin,et al.  The SimpleScalar tool set, version 2.0 , 1997, CARN.

[28]  Amit Sinha,et al.  Energy efficient operating systems and software , 2001 .

[29]  Chandra Krintz,et al.  NWSLite: a light-weight prediction utility for mobile devices , 2004, MobiSys '04.

[30]  Richard Wolski,et al.  Predicting CPU Availability on the Computational Grid Using the Network Weather Service , 1999, Parallel Process. Lett..

[31]  Richard Wolski,et al.  Dynamically forecasting network performance using the Network Weather Service , 1998, Cluster Computing.

[32]  Mahadev Satyanarayanan,et al.  Self-tuned remote execution for pervasive computing , 2001, Proceedings Eighth Workshop on Hot Topics in Operating Systems.

[33]  James M. Rehg,et al.  A Compilation Framework for Power and Energy Management on Mobile Computers , 2001, LCPC.

[34]  Mahadev Satyanarayanan,et al.  Operating system support for mobile interactive applications , 2002 .

[35]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[36]  Allen Newell,et al.  The psychology of human-computer interaction , 1983 .

[37]  Cheng Wang,et al.  Computation offloading to save energy on handheld devices: a partition scheme , 2001, CASES '01.

[38]  Mahadev Satyanarayanan,et al.  Extending mobile computer battery life through energy-aware adaptation , 2001 .

[39]  Hong Wang,et al.  Recursive estimation and time-series analysis , 1986, IEEE Trans. Acoust. Speech Signal Process..

[40]  Peter C. Young,et al.  Recursive Estimation and Time Series Analysis , 1984 .