IE 594 : Research Methodology – Discrete Event Simulation David S. Kim Spring 2009.

Slides:



Advertisements
Similar presentations
CPSC 502, Lecture 15Slide 1 Introduction to Artificial Intelligence (AI) Computer Science cpsc502, Lecture 15 Nov, 1, 2011 Slide credit: C. Conati, S.
Advertisements

Operations Control Key Sources: Data Analysis and Decision Making (Albrigth, Winston and Zappe) An Introduction to Management Science: Quantitative Approaches.
Overarching Goal: Understand that computer models require the merging of mathematics and science. 1.Understand how computational reasoning can be infused.
Copyright © 2005 Department of Computer Science CPSC 641 Winter PERFORMANCE EVALUATION Often in Computer Science you need to: – demonstrate that.
Silberschatz, Galvin and Gagne  2002 Modified for CSCI 399, Royden, Operating System Concepts Operating Systems Lecture 19 Scheduling IV.
W. McNair Bostick, Oumarou Badini, James W. Jones, Russell S. Yost, Claudio O. Stockle, and Amadou Kodio Ensemble Kalman Filter Estimation of Soil Carbon.
FIN 685: Risk Management Topic 5: Simulation Larry Schrenk, Instructor.
PROVIDING DISTRIBUTED FORECASTS OF PRECIPITATION USING A STATISTICAL NOWCAST SCHEME Neil I. Fox and Chris K. Wikle University of Missouri- Columbia.
1 PERFORMANCE EVALUATION H Often one needs to design and conduct an experiment in order to: – demonstrate that a new technique or concept is feasible –demonstrate.
Discrete-Event Simulation: A First Course Steve Park and Larry Leemis College of William and Mary.
Modeling OFDM Radio Channel Sachin Adlakha EE206A Spring 2001.
Discrete Event Simulation How to generate RV according to a specified distribution? geometric Poisson etc. Example of a DEVS: repair problem.
Designing QoE experiments to evaluate Peer-to-Peer streaming applications Tom Z.J. Fu, CUHK Dah Ming Chiu, CUHK Zhibin Lei, ASTRI VCIP 2010, Huang Shan,
Chapter 6: Database Evolution Title: AutoAdmin “What-if” Index Analysis Utility Authors: Surajit Chaudhuri, Vivek Narasayya ACM SIGMOD 1998.
ANOVA Determining Which Means Differ in Single Factor Models Determining Which Means Differ in Single Factor Models.
OS Fall ’ 02 Performance Evaluation Operating Systems Fall 2002.
Simulation.
UMass Lowell Computer Science Advanced Algorithms Computational Geometry Prof. Karen Daniels Spring, 2004 Project.
Performance Evaluation
Simulation Models as a Research Method Professor Alexander Settles.
Evolutionary Computational Intelligence Lecture 9: Noisy Fitness Ferrante Neri University of Jyväskylä.
1 Validation and Verification of Simulation Models.
1 PERFORMANCE EVALUATION H Often in Computer Science you need to: – demonstrate that a new concept, technique, or algorithm is feasible –demonstrate that.
Investigation of TDMA solution for the hidden terminal problem (With “ Wavion ” ) Final Report Presentation.
Simulation Waiting Line. 2 Introduction Definition (informal) A model is a simplified description of an entity (an object, a system of objects) such that.
Chapter 01 Introduction to Probability Models Course Focus Textbook Approach Why Study This?
OS Fall ’ 02 Performance Evaluation Operating Systems Fall 2002.
1 Confidence Intervals for Means. 2 When the sample size n< 30 case1-1. the underlying distribution is normal with known variance case1-2. the underlying.
AutoSimOA : A Framework for Automated Analysis of Simulation Output Stewart Robinson Katy Hoad, Ruth Davies Funded by.
Analysis of Simulation Results Andy Wang CIS Computer Systems Performance Analysis.
Discrete Event Simulation in Automotive Final Process System Vishvas Patel John Ma Throughput Analysis & Simulations General Motors 1999 Centerpoint Parkway.
Simulation Output Analysis
Incident Response Mechanism for Chemical Facilities By Stephen Fortier and Greg Shaw George Washington University, Institute for Crisis, Disaster and Risk.
Introduction to Discrete Event Simulation Customer population Service system Served customers Waiting line Priority rule Service facilities Figure C.1.
1 Performance Evaluation of Computer Networks: Part II Objectives r Simulation Modeling r Classification of Simulation Modeling r Discrete-Event Simulation.
Genetic Regulatory Network Inference Russell Schwartz Department of Biological Sciences Carnegie Mellon University.
 1  Outline  stages and topics in simulation  generation of random variates.
Modeling and simulation of systems Simulation optimization and example of its usage in flexible production system control.
Verification & Validation
Modeling & Simulation: An Introduction Some slides in this presentation have been copyrighted to Dr. Amr Elmougy.
Monte Carlo Simulation CWR 6536 Stochastic Subsurface Hydrology.
Stochastic Linear Programming by Series of Monte-Carlo Estimators Leonidas SAKALAUSKAS Institute of Mathematics&Informatics Vilnius, Lithuania
Hung X. Nguyen and Matthew Roughan The University of Adelaide, Australia SAIL: Statistically Accurate Internet Loss Measurements.
Major objective of this course is: Design and analysis of modern algorithms Different variants Accuracy Efficiency Comparing efficiencies Motivation thinking.
1 STAT 500 – Statistics for Managers STAT 500 Statistics for Managers.
Center for Radiative Shock Hydrodynamics Fall 2011 Review Assessment of predictive capability Derek Bingham 1.
Chap. 5 Building Valid, Credible, and Appropriately Detailed Simulation Models.
PROCESS MODELLING AND MODEL ANALYSIS © CAPE Centre, The University of Queensland Hungarian Academy of Sciences Statistical Model Calibration and Validation.
Prentice HallHigh Performance TCP/IP Networking, Hassan-Jain Chapter 4 TCP/IP Network Simulation.
ECE 466/658: Performance Evaluation and Simulation Introduction Instructor: Christos Panayiotou.
Simulation Techniques Overview Simulation environments emulation/ exec- driven event- driven sim trace- driven sim stochastic sim Workload parameters System.
Chapter 2 Fundamental Simulation Concepts
A Hyper-heuristic for scheduling independent jobs in Computational Grids Author: Juan Antonio Gonzalez Sanchez Coauthors: Maria Serna and Fatos Xhafa.
Introduction to Simulation Andy Wang CIS Computer Systems Performance Analysis.
OPERATING SYSTEMS CS 3530 Summer 2014 Systems and Models Chapter 03.
Csci 418/618 Simulation Models Dr. Ken Nygard, IACC 262B
Simulation. Types of simulation Discrete-event simulation – Used for modeling of a system as it evolves over time by a representation in which the state.
Building Valid, Credible & Appropriately Detailed Simulation Models
Modelling & Simulation of Semiconductor Devices Lecture 1 & 2 Introduction to Modelling & Simulation.
Think like an experimentalist 10/11/10. Melissa, you’re a modeler! And I do “systems biology”. So model this data for me!!
CPSC 531: System Modeling and Simulation
C.-S. Shieh, EC, KUAS, Taiwan
Chapter 10 Verification and Validation of Simulation Models
Professor Arne Thesen, University of Wisconsin-Madison
Objective of This Course
Computer Systems Performance Evaluation
Computer Systems Performance Evaluation
MECH 3550 : Simulation & Visualization
Dr. Arslan Ornek MATHEMATICAL MODELS
Presentation transcript:

IE 594 : Research Methodology – Discrete Event Simulation David S. Kim Spring 2009

Research Methodology - Simulation Simulation as a research tool Research in simulation Focus here is on simulation of discrete event dynamic systems

Simulation as a Research Tool What is the role of simulation in the research? 1. Used as the tool to understand general system dynamics and generate insights? 2. Comparison or validation of an approximation or heuristic? What is the role of simulation in the buffer allocation paper (Conway paper)?

Simulation as a Research Tool – Case 1 Why simulation? The system of interest has a performance function f that transforms controllable system parameters into system performance. The function f is unknown or computationally intractable.  Markov chain that approximate the throughput function have too many states  Analytical models do not exist. What is f in the buffer allocation paper (Conway et al.)? What are the input parameters?

Simulation as a Research Tool – Case 2 Why simulation? A heuristic or analytical approximation has been developed to model some system performance measure. The development of the approximation requires simplifying assumptions/approximations. The conjecture is that the analytical model is still a reasonable representation of the real system. Simulation is being used to support or refute this conjecture. What are the simplifying assumptions/ approximations used in the Nagarajan paper?

Simulation as a Research Tool What is the real system being simulated? Is there evidence or research precedent indicating that the simplified model being simulated characterizes real systems? Is this generally known or do references need to be cited? What’s being simulated in the buffer paper?

Simulation as a Research Tool Are the assumptions applied in the simulation clearly stated? Distributions used. Operational protocols, e.g., blocking, etc. Correlation? Can you simulate the same system? Steady State vs. Terminating Number of runs Length of runs  Some models take a long time to “settle down” Warm-up

Simulation as a Research Tool Verification & validation Mainly applies to studying a real system or a detailed representation How was this conducted?  Results compared to an existing system?  Comparisons made to existing analytical results?  Extreme cases tested?

Simulation as a Research Tool Experimental design How was the “parameter space” explored? Experimental design? Random systems? Worse case systems? Standard problem library? The importance of this depends on the way the simulation was used If simulating to understand a system and gain insight, these issues become more important.

Simulation as a Research Tool Output analysis Were proper statistical procedures applied to the output?  e.g., confidence intervals What is the variance around the average results?

Research in Simulation Research into the various aspects of simulation mechanisms or conducting simulation studies Generating random numbers Generating random variates Use of distributions in simulations Dealing with extremely large simulation models Optimization of systems using simulation etc., … See general topics for the WSC

Research in Simulation Simulation optimization The system of interest has a performance function f that transforms controllable system parameters into system performance. The function f is unknown or computationally intractable. Simulation is used to approximate f How can simulation be used to optimize f subject to constraints and costs?

Research in Simulation Examples of simulation optimization research Perturbation analysis Use of global optimization heuristics (e.g., genetic algorithms) Vergara paper.

Research in Simulation Simulation modeling methodology How can different systems be modeled? Distributed/parallel simulation Methods for verification/validation Short cuts/approximations Where is the Sharma paper ?

Conclusions Many of the items discussed today are things to look for when evaluating research and when conducting research. Often the importance of each item will depend on the circumstances, the existing body of research, and the objectives of the research.