CS626 Data Analysis and Simulation Instructor: Peter Kemper R 104A, phone 221-3462, email:kemper@cs.wm.edu Office hours: Monday,Wednesday 2-4 pm Today: Overview & Introduction 1
Organizational Issues Class materials http://www.cs.wm.edu/~kemper PDF files of slides Homework, projects, … Supplementary material: tutorials, references, … Schedule MWF: 10.00 – 10.50 am Office hours: Monday, Wednesday 2pm-4pm and by appointment No class February 18 - gives extra time for assignments - due to DSN PC Meeting Just ask if you are interested what I am doing there, I am happy to tell you more … 2
References We will use texts from multiple sources! A lot of documents/books are available online in the SWEM library! Data Analysis: Bertholt, Borgelt, Hoeppner, Klawonn, Guide to Intelligent Data Analysis NIST/SEMATECH e-Handbook of Statistical Methods, http://www.itl.nist.gov/div898/ handbook/ P. Dalgaard, Introductory statistics with R, Springer 2002, online at SWEM B. Everitt, A handbook of statistical analyses using R, CRC Press 2010, online at SWEM Simulation: Law/Kelton, Simulation Modeling and Analysis, McGrawHill 3
Big Picture: Model-based Analysis of Systems portion/facet real world perception transfer solution to real world problem real world problem description decision formal model transformation presentation probability model, solution, rewards, stochastic process qualitative and formal / computer aided quantitative analysis properties 4
CS 626 – Focus: Stochastic Models Stochastic models rely on probability theory What is probability? A much beloved topic for students A particular type of functions f : S -> [0,1] A mathematical mean to process something we are not sure about: 5
Calculating with something we are not sure about ? What to give as input ? Separate “system” from “environment”, clarify on interactions Separate “subsystems” and their dependencies within “system” Quantify likelihood of relevant elementary “events” to happen What to know ? How to calculate with probabilities? How to handle dependencies? What to gain ? Information on likelihood of overall behavior, quantified information on expected behavior to evaluate a single system or to compare systems 6
Grading: How to get an „A“ for CS 626 Class participation: 0% Seems useless but is key to meet the other criteria It will be more fun if you actively participate Ask questions, make suggestions, contribute … Homework: 30% About 5-6 How to get an A? Just do it, hand in on time, present your results … Projects: 20% Will require some effort, time and creativity Adheres to „learning by doing“ approach How to get an A? Start early, get things done, hand in on time, reflect what you are doing, present your results … In-class Exams: 50% Midterm: 20% Final: 30% How to get an A? The usual game … 7
Overview – this is the plan Probability Theory Primer & Statistics Concepts Tools: Stochastic Input Modeling - Mobius Different types of stochastic workloads - BioPEPA Relies heavily on data analysis - AnyLogic Simulation Models - R Static - KNIME Dynamic Discrete Event Dynamic Systems Continuous, ODE models Output Analysis Data analysis strikes back again! Verification, Validation, Testing of Simulation Models Data Analysis Classics: Preparation, Finding Patterns, Explanations, and Predictors 8
Probability Theory has its origins in an interest in games Cards, roulette, dices, Gerolamo Cardano, 1501-1576 From Wikipedia: … notoriously short of money and kept himself solvent by being an accomplished gambler and chess player. His book about games of chance, Liber de ludo aleae , written in the 1560s but published only in 1663 after his death, contains the first systematic treatment of probability, as well as a section on effective cheating methods. Pierre-Simon Laplace, 1749-1827 � "It is remarkable that a science which began with the consideration of games of chance should have become the most important object of human knowledge." Theorie Analytique des Probabilite , 1812. � � Other VIPs of probability theory: Andrey Kolmogorov Andrey Markov 9
So where to start? With an application example! My choice: Dependability of a LEO satellite network Reference E. Athanasopoulou, P. Thakker, W.H. Sanders. Evaluating the dependability of a LEO satellite network for scientific applications. In Proc. 2nd int. Conf. Quantitative Evaluation of Systems, pp 95-104, IEEE, 2005. What to learn from this: An impression on what can be achieved with stochastic models Some terminology, techniques and tools we need to give a closer look Please keep in mind: LEO satellite modeling is one application among many Dependability modeling is one application area among many The terms and techniques are a subset of what is known => there is more to learn here, and it is interesting! 10
A Low Earth Orbit Satellite Network The story: University of Illinois student-developed satellite network Based on Illinois Observing Nanosatellite (ION) Purpose: collect scientific data E.g. natural disaster monitoring, earthquake monitoring, mapping of Earth’s magnetic field, measuring radiation flux for space weather … In particular: measurement of light emissions from oxygen chemistry in the atmosphere Several mission objectives: Testing new thrusters, a new processor for small satellites, a new CMOS camera, demonstration of attitude control on a CubeSat Some issues from the list of challenges: What minimum radiation shielding is necessary ? What level of redundancy is necessary ? … to achieve a 6 mth target lifetime with COTS components … How does sharing resources through a network could improve communication with the ground ? 11
Dependability Assessment Properties of interest, goals of study Reliability of network R(0,t), probability of no permanent critical system failures during time interval [0,t] Interval availability A(0,t), fraction of time system delivers proper service during time interval [0,t] System vs Environment System: Ground station, 45° inclination, northern hemis., repairable failures 4 satellites 7 critical subsystems (5V/9V regulators, battery, solar panel, comm hardware, processor, telemetry) + experiment hardware with temp and permanent failures Orbits: Sat 1-3: 90 min period, Sat 4: 720 min period Inclination: Sat 1 90° , Sat 2 90° orth, Sat 3 45° , Sat 4 0° Environment: “the rest” with a foreseen influence based on Lightning storms etc cause preamp failures at ground station Radiation causes failures for satellite processors and CMOS devices 12
Communication Communication Intersatellitelink (ISL) Satellite - satellite: Gateway Link if within communication range (GWL) GWL Satellite - ground station: if within footprint We discretize orbits, identify matching periods Footprint ε Elevation: Angle wrt to center of radiation cone and earth surface 13
Clarification: Inclination Plane of satellite orbit Satellite orbit Closest point to earth δ Inclination δ Equatorial plane 14
More input data Radiation dose, shielding and its mapping to failure rates, 0.007 failures per year for processor and CMOS components 0.0001 failures per year for discrete components Scale factor r=1 for 1mm shielding for higher orbit Consider several model configurations! Communication: Data collection rate: 2 GB/yr while memory available all data lost at failure Uplink communication considered negligible Simple routing mechanism ISL communication rate: 115 kbps with 50% overhead, 226665 MB/yr GWL rate: double ISL rate 15 ISL with commercial satellite networks
Where are the probabilities ? Dependability study: Ground station: rates of failure / repair actions Satellite subsystems: rates of failure / repair actions are modeled with a random variable that follows a negative exponential distribution with a given rate. Rate 5.0 means on average 5 events per time unit. Total Ionizing Dose (TID): is taken into account by a scaling factor r towards failure rates of components. What is then analyzed Reliability and availability For different levels of radiation shielding For different levels of redundancy of components What type of analysis is used Transient analysis of Markov chains For single satellite design Discrete event simulation of stochastic models valid alternative, used for evaluation of overall network 16
Outcome of calculations Satellite reliability in time interval [0,t] with t in months for different levels of radiation shielding Base radiation rate r = 1 (matches 1 mm of shielding) Increased shielding: r = 0.4, r = 0.2 Reduced shielding: r = 2, r = 5 Probability of no permanent critical system failures during [0,t] 17
Some more results wrt reliability Different levels of redundancy for Batteries Note, failures are unrelated to radiation Two batteries seem ok Regulators Redundancy does not improve TID immunity since processor or communication system fail due to TID much earlier 18
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