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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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Presentation on theme: "Optimization and Control in Wireless and Computer Networks (Ph.D defense)‏ 26 th November, 2008 Dinesh Kumar."— Presentation transcript:

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

2 Outline Wireless Access Networks –802.11 WLAN & 3G UMTS Hybrid Cell: User-Network Association –Non-cooperative control in 802.11 MAC –Fountain Codes based Transport in 802.11 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

3 UMTS NodeB 802.11 WLAN AP User-Network Association

4 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

5 Model Characteristics 802.11 WLAN AP UMTS NodeB Packet throughput of each user (Fluid Model)‏ Session throughput of all users (Discrete Model)‏

6 Model Characteristics 802.11 WLAN AP UMTS NodeB financial revenue earned by network operator

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

8 WLAN AP Downlink Throughput 802.11 WLAN AP

9 UMTS NodeB Downlink Throughput UMTS NodeB

10 Numerical Example AP-AP setup 802.11 WLAN AP-1 802.11 WLAN AP-2

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

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

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

14 AP-NodeB setup 802.11 WLAN AP UMTS NodeB

15 Optimal policy in AP-NodeB hybrid cell NodeB AP

16 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

17 Model 802.11 WLAN AP UMTS NodeB

18 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 :

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

20 Formulation (contd.)‏

21 Numerical Results

22 Numerical Results (contd.)‏

23 Non-Cooperative PHY Rate Control in 802.11 WLAN PHY rate selection in IEEE 802.11 WLAN is user/vendor configurable Question: Which rate among 6,9,12,..., 54 Mbps to choose so as to optimize power and throughput performance ?

24 Payoff Function

25 Cooperative Approach: Max-Min Fair


27 Cooperative Approach: Global Multirate


29 Non-Cooperative Game


31 Linear Power Consumption Cost

32 Exponential Power Consumption Cost

33 Main Conclusions DCF protocol in an 802.11 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

34 Fountain Codes based Transport in a Single Cell 802.11 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

35 Numerical Results





40 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)‏

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

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

43 Dense MANET: Capacity Optimizing Hop Distance Consider a dense multi-hop network of IEEE 802.11 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:

44 Background & Problem Objective


46 Numerical Results



49 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.

50 Optimization Parameters for Optimal Hop Selection


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

53 Optimization Solution & Properties (L=2)‏


55 Optimization Solution & Properties (L>2)‏


57 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

58 Inferencing... (Example)‏



61 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

62 Enhanced Inferencing... (Ex. revisited)‏


64 Case Study: Web/CQ together  Overall utilization for Web/CQ scales as an exponential function of arrival load  Overhead at Web/CQ: 0.2966 + 0.00267 * exp (sum arrival workload)‏  Web/CQ together can support 950 sessions/hour

65 Case Study: Web and CQ separately  Overall utilization for Web & CQ separately scales as quadratic function of arrival load  Overhead at Web server: 0.07851 + 0.008728 * (sum arrival workload) 2  Overhead at CQ server: 0.01251 + 0.000992 * (sum arrival workload) 2  Web & CQ separately can support 1680 sessions/hour

66 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 ?

67 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

68 Results: Single Class, Single Server

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

70 Results: Multiple Classes, Single Server

71 Random Non-Stationary Workload / Workload Dependent Service Time





76 Highly Non-Stationary Workload / Abrupt Switch in Service Time





81 Publications (1)‏

82 Publications / Patent (2)‏

83 Thank You! Questions ?

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