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Optimization and Control in Wireless and Computer Networks (Ph.D defense) 26 th November, 2008 Dinesh Kumar

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Outline Wireless Access Networks – WLAN & 3G UMTS Hybrid Cell: User-Network Association –Non-cooperative control in MAC –Fountain Codes based Transport in WLAN –Channel Switching Policy in 3G UMTS Wireless Ad Hoc Networks –MANET: Capacity Optimizing Hop Distance –VANET on highway: Route Lifetime based Optimal Hop Selection Real Time Performance Modeling

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UMTS NodeB WLAN AP User-Network Association

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Global Optimality Decision aims on maximizing global network utility for the network operator Utility can be composed of a financial revenue component and global network throughput component Network Operator dictates decision of nodes choosing a network

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Model Characteristics WLAN AP UMTS NodeB Packet throughput of each user (Fluid Model) Session throughput of all users (Discrete Model)

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Model Characteristics WLAN AP UMTS NodeB financial revenue earned by network operator

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Semi-MDP Control formulation Using Uniformization technique and introducing virtual departures, the recursive Dynamic Programming (DP) equations are:

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WLAN AP Downlink Throughput WLAN AP

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UMTS NodeB Downlink Throughput UMTS NodeB

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Numerical Example AP-AP setup WLAN AP WLAN AP-2

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Optimal policy in AP-AP setup AP - 2 AP - 1

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NodeB-NodeB setup UMTS NodeB-2 UMTS NodeB-1

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Optimal policy in NodeB-NodeB setup NodeB-2 NodeB-1 NodeB-2 NodeB-1

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AP-NodeB setup WLAN AP UMTS NodeB

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Optimal policy in AP-NodeB hybrid cell NodeB AP

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Individual Optimality: Non-Cooperative Game Utility can be for eg., average response time Nodes can have QoS requirements Nodes choose a network if QoS is met or leave the system if not We study Individual Optimality in a framework that extends that of an Altman et al. paper User can join a shared server with PS (WLAN) or any one of several dedicated servers (dedicated DCH channels in UMTS) Each node takes decision to maximize its own utility

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Model WLAN AP UMTS NodeB

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Formulation Define an (L,q)-threshold policy u as: It has been proved in Altman et al. paper that optimal best response policy against is given as: Let Then, if the best-response policy is otherwise its: where q* is the unique soltn. of :

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Formulation (contd.) For computing analytically, if then it is solution of following set of c i linear equations:

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Formulation (contd.)

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Numerical Results

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Numerical Results (contd.)

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Non-Cooperative PHY Rate Control in WLAN PHY rate selection in IEEE WLAN is user/vendor configurable Question: Which rate among 6,9,12,..., 54 Mbps to choose so as to optimize power and throughput performance ?

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Payoff Function

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Cooperative Approach: Max-Min Fair

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Cooperative Approach: Global Multirate

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Non-Cooperative Game

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Linear Power Consumption Cost

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Exponential Power Consumption Cost

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Main Conclusions DCF protocol in an WLAN under a non- cooperative setting is inefficient Under non-cooperative rate assignment, single node throughput degrades very fast with increasing number of users Under cooperative rate assignment, single node throughput performance improves –from 11% to 200% for linear cost approximation –from 12% to 100% for exponential cost approximation

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Fountain Codes based Transport in a Single Cell WLAN Fountain Codes are rateless erasure codes (e.g. LT Codes) For a file of size N p packets at sender side, it is sufficient to have N p (1+e) packets at receiver side to decode the file with success probability 1-f, where, f <= 2^(-eN p ) Simple STOP message return by receiver to indicate sufficient packets or successful file decoding

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Numerical Results

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Channel Switching Policy in 3G UMTS Downlink One FACH channel shared among all TCP sources FACH is a time-division multiplexed channel FACH channel has both TCP and Signaling traffic (higher priority) FACH transmits TCP traffic using Round-Robin TCP connection is put on FACH if no DCH is available initially Multiple DCH channels; each is user-dedicated (high speed and low delay) Switching between FACH & DCH is costly in time and signaling (250 ms switching time)

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Simulation Comparison: N tcp =2, N dch =1

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Simulation Comparison: N tcp =3, N dch =1

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Dense MANET: Capacity Optimizing Hop Distance Consider a dense multi-hop network of IEEE nodes employing CSMA/CD based DCF with RTS/CTS control signalling. Assume a periphery limited mobility scenario and define periphery hop distance d as shown below:

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Background & Problem Objective

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Numerical Results

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Main Conclusions Neither shortest path routing, nor smallest hop distance routing may be optimal for a dense MANET. Depending on average network power, non-trivial optimal hop distance may be obtained.

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Optimization Parameters for Optimal Hop Selection

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Optimization over: –Number of Relay Nodes –Inter-Node distances –Speeds of Intermediate Nodes

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Optimization Solution & Properties (L=2)

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Optimization Solution & Properties (L>2)

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Performance Modeling for Workload Independent Parameters: Inferencing Does performance model fitting based on average values from measurement data and least-squares error minimization Service time and CPU overhead estimation of transaction based distributed applications deployed over an arbitrary network architecture

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Inferencing... (Example)

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Enhanced Inferencing Workload-dependent service times and CPU overheads to take care of non-stationary workloads Different natural-form dependencies of type polynomial, exponential and logarithmic, etc. to take care of overheads due to complex modular design and other factors Empirically we have observed that dependency on sum-workload instead of the entire workload vector is sufficient

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Enhanced Inferencing... (Ex. revisited)

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Case Study: Web/CQ together Overall utilization for Web/CQ scales as an exponential function of arrival load Overhead at Web/CQ: * exp (sum arrival workload) Web/CQ together can support 950 sessions/hour

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Case Study: Web and CQ separately Overall utilization for Web & CQ separately scales as quadratic function of arrival load Overhead at Web server: * (sum arrival workload) 2 Overhead at CQ server: * (sum arrival workload) 2 Web & CQ separately can support 1680 sessions/hour

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Real Time Performance Modeling How to modify/extend Inferencing for service time estimation based not on minimization of estimation error in means, but on minimization of variance of estimation error ? How to dynamically update the performance model with real-time measurements ? How to eliminate unpredictable noise in measurement data ?

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Use Filtering Optimal estimation & tracking of state parameters of a dynamically evolving system based on a state evolution model, an observation model and real-time measurement data Accounts for history of estimation error and goodness of fit Minimizes mean, variance and higher order variations in estimation error Well known filters like Kalman and H ∞ -optimal filters are extensively used in other fields like guided missile systems, image processing, robotics, etc. Have also been applied to time-out estimation in TCP, wireless channel estimation, sensor networks, etc. Woodside et al. have done a partial study in application to performance modeling

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Results: Single Class, Single Server

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Step-Increasing Non-Stationary Workload / Workload Dependent Service Time Estimated Utilization Estimated ServiceTime Measured Utilization Measured ResponseTime Measured ArrivalRate

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Results: Multiple Classes, Single Server

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Random Non-Stationary Workload / Workload Dependent Service Time

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Highly Non-Stationary Workload / Abrupt Switch in Service Time

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Publications (1)

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Publications / Patent (2)

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Thank You! Questions ?

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