Modeling Host Load Peter A. Dinda Thesis Seminar 2/9/98.

Slides:



Advertisements
Similar presentations
Autocorrelation Functions and ARIMA Modelling
Advertisements

Volatility. Downloads  Today’s work is in: matlab_lec04.m  Functions we need today: simsec.m, simsecJ.m, simsecSV.m  Datasets we need today: data_msft.m.
An Evaluation of Linear Models for Host Load Prediction Peter A. Dinda David R. O’Hallaron Carnegie Mellon University.
Part II – TIME SERIES ANALYSIS C5 ARIMA (Box-Jenkins) Models
Time Series Building 1. Model Identification
R. Werner Solar Terrestrial Influences Institute - BAS Time Series Analysis by means of inference statistical methods.
Test Taking Skills Essay Tests Developed and Designed by Birma Gainor.
Trace-based Network Bandwidth Analysis and Prediction Yi QIAO 06/10/2002.
Properties of the estimates of the parameters of ARMA models.
Comparison of natural streamflows generated from a parametric and nonparametric stochastic model James Prairie(1,2), Balaji Rajagopalan(1) and Terry Fulp(2)
Forecasting JY Le Boudec 1. Contents 1.What is forecasting ? 2.Linear Regression 3.Avoiding Overfitting 4.Differencing 5.ARMA models 6.Sparse ARMA models.
Monte Carlo Integration Robert Lin April 20, 2004.
Host Load Trace Replay Peter A. Dinda Thesis Seminar 11/23/98.
Dynamic Mapping of Activation Trees Thesis Proposal January 29, 1998 Peter A. Dinda Committee David O’Hallaron (chair) Thomas Gross Peter Steenkiste Jaspal.
Modeling Cycles By ARMA
Pemodelan Kuantitatif Mat & Stat Pertemuan 3: Mata kuliah:K0194-Pemodelan Matematika Tahun:2008.
Responsive Interactive Applications by Dynamic Mapping of Activation Trees February 20, 1998 Peter A. Dinda School of Computer.
Understanding and Predicting Host Load Peter A. Dinda Carnegie Mellon University
1 Yi Qiao Jason Skicewicz Peter A. Dinda Prescience Laboratory Department of Computer Science Northwestern University Evanston, IL An Empirical Study.
1 Hybrid Agent-Based Modeling: Architectures,Analyses and Applications (Stage One) Li, Hailin.
Texture Recognition and Synthesis A Non-parametric Multi-Scale Statistical Model by De Bonet & Viola Artificial Intelligence Lab MIT Presentation by Pooja.
Financial Time Series CS3. Financial Time Series.
End of Chapter 8 Neil Weisenfeld March 28, 2005.
ARIMA Forecasting Lecture 7 and 8 - March 14-16, 2011
Dynamic Response for Motion Capture Animation Victor B. Zordan Anna Majkowska Bill Chiu Matthew Fast Riverside Graphics Lab University of California, Riverside.
Online Prediction of the Running Time Of Tasks Peter A. Dinda Department of Computer Science Northwestern University
Statistical Learning: Pattern Classification, Prediction, and Control Peter Bartlett August 2002, UC Berkeley CIS.
TRENDS IN YOUTH HOMICIDE: A MULTIVARIATE ASSESSMENT AND FORECASTING FOR POLICY IMPACT ROBERT NASH PARKER UNIVERSITY OF CALIFORNIA EMILY K. ASENCIO UNIVERSITY.
Load Analysis and Prediction for Responsive Interactive Applications Peter A. Dinda David R. O’Hallaron Carnegie Mellon University.
Realistic CPU Workloads Through Host Load Trace Playback Peter A. Dinda David R. O’Hallaron Carnegie Mellon University.
Approximation Metrics for Discrete and Continuous Systems Antoine Girard and George J. Pappas VERIMAG Workshop.
Transition Matrix Theory and Loss Development John B. Mahon CARe Meeting June 6, 2005 Instrat.
CSCI 347 / CS 4206: Data Mining Module 06: Evaluation Topic 01: Training, Testing, and Tuning Datasets.
Traffic modeling and Prediction ----Linear Models
Chapter 15 Forecasting Copyright © 2011 Pearson Addison-Wesley. All rights reserved. Slides by Niels-Hugo Blunch Washington and Lee University.
AR- MA- och ARMA-.
Downscaling in time. Aim is to make a probabilistic description of weather for next season –How often is it likely to rain, when is the rainy season likely.
Outline Classification Linear classifiers Perceptron Multi-class classification Generative approach Naïve Bayes classifier 2.
1 FARIMA(p,d,q) Model and Application n FARIMA Models -- fractional autoregressive integrated moving average n Generating FARIMA Processes n Traffic Modeling.
© 2010 AT&T Intellectual Property. All rights reserved. AT&T, the AT&T logo and all other AT&T marks contained herein are trademarks of AT&T Intellectual.
1 University of Maryland Linger-Longer: Fine-Grain Cycle Stealing in Networks of Workstations Kyung Dong Ryu © Copyright 2000, Kyung Dong Ryu, All Rights.
Learning to perceive how hand-written digits were drawn Geoffrey Hinton Canadian Institute for Advanced Research and University of Toronto.
A Comparison of a SWAT model for the Cannonsville Watershed with and without Variable Source Area Hydrology Josh Woodbury Christine A. Shoemaker Dillon.
FORECASTING. Minimum Mean Square Error Forecasting.
Time Series Basics (2) Fin250f: Lecture 3.2 Fall 2005 Reading: Taylor, chapter , 3.9(skip 3.6.1)
June 10, 1999 Discrete Event Simulation - 3 What other subsystems do we need to simulate? Although Packets are responsible for the largest amount of events,
Processing Sequential Sensor Data The “John Krumm perspective” Thomas Plötz November 29 th, 2011.
1 8. Back-testing of trading strategies 8.1Bootstrap Brock et al (1992), Davidson & Hinkley (1997), Fusai & Roncoroni (2008). Bootstrap: picking up at.
Jiann-Ming Wu, Ya-Ting Zhou, Chun-Chang Wu National Dong Hwa University Department of Applied Mathematics Hualien, Taiwan Learning Markov-chain embedded.
0 - 1 © 2007 Texas Instruments Inc, Content developed in partnership with Tel-Aviv University From MATLAB ® and Simulink ® to Real Time with TI DSPs Spectrum.
EGR 301 Applications for ARIMA Simulations.
Seasonal ARMA forecasting and Fitting the bivariate data to GARCH John DOE.
Regression Analysis1. 2 INTRODUCTION TO EMPIRICAL MODELS LEAST SQUARES ESTIMATION OF THE PARAMETERS PROPERTIES OF THE LEAST SQUARES ESTIMATORS AND ESTIMATION.
1 Chapter 8: Model Inference and Averaging Presented by Hui Fang.
Colorado Center for Astrodynamics Research The University of Colorado 1 STATISTICAL ORBIT DETERMINATION Kalman Filter with Process Noise Gauss- Markov.
Hidden Markov Models. A Hidden Markov Model consists of 1.A sequence of states {X t |t  T } = {X 1, X 2,..., X T }, and 2.A sequence of observations.
Introduction to stochastic processes
Time Series Analysis PART II. Econometric Forecasting Forecasting is an important part of econometric analysis, for some people probably the most important.
Subodh Kant. Auto-Regressive Integrated Moving Average Also known as Box-Jenkins methodology A type of linear model Capable of representing stationary.
Applied Econometric Time Series Third Edition
Partially Predictable
Statistical Methods Carey Williamson Department of Computer Science
Meat sales forecasting (Panvita Group)
Partially Predictable
Stochastic Volatility Models: Bayesian Framework
Discrete-time markov chain (continuation)
Some Initial Results on Network Bandwidth Prediction
4 Step Simple Diagram Sample text Sample text Sample text Sample text
BOX JENKINS (ARIMA) METHODOLOGY
Reinforcement Learning (2)
Presentation transcript:

Modeling Host Load Peter A. Dinda Thesis Seminar 2/9/98

2 Outline Why model host load? Desirable properties Modeling techniques Results of Markov modeling

3 Why Model Host Load? Load partially determines exec time Benchmarks for trace-based simulation –Classification scheme for load traces Generalization of simulation results –Performance in terms of model parameters [t min,t max ]?

4 Desirable Properties Classification power –Few parameters –Significant properties of original traces Generation of load traces –Traces with similar significant properties Prediction of load –Mean square error sense

5 Modeling Techniques Markov models Stochastic processes –AR, MA, ARMA, ARIMA,...

6 Markov Modeling Simple model –Discretized load levels map to states O(n^2) transitions Vector model –State includes last d discretized levels Capture momentum, acceleration,... O(n^2d) transitions 39 load traces (1 Hz sample rate, 1 week)

7

8

9

10

11