1. Tradeoff Based Network Management for Wireless Networks Huazhi Gong NetMedia Lab@GIST 20036075 hankgong@gist.ac.kr Date 2008/05/26 Ph. D Pre-Defence

2. 2 ► Ch. 1: Introduction ► Ch. 2: Background and Related Work ► Ch. 3: WLAN Planning Framework Based on Tabu Search ► Ch. 4: Association Management for Wireless Networks ► Ch. 5: Network Monitoring Based on Network Coding ► Ch. 6: Conclusion Part II: Contents Part III: Summary Part I: Background Ch1 Ch3 Ch2 Ch4 Ch5 Ch6

3. 3 Current Wireless Networks Wireless Local Area Network (WLANs): widely deployed  IEEE 802.11a/b/g Wireless Mesh Network (WMNs): popular for research  IEEE 802.11s standard is still not finished  INTEL and CISCO are active in this area IEEE 802.11a/b/g IEEE 802.11s

4. 4 General Network Management Architecture Normally centralized for wired network For wireless network, distributed or hybrid management is better

5. 5 Motivations More complexity at the network edges Distributed v.s. centralized Relatively high loss rates on links Fairness v.s. efficiency QoS demands on mobile clients Scalable network planning Distributed association management Realtime link monitoring

6. 6 Network Management Architecture for Wireless Networks Wireless network: single-hop (WLANs), multi-hop (WMNs) Network management: WLAN planning, association management, and network monitoring WLAN Planning Association Management Network monitoring

7. 7 Ch1 Ch3 Ch2 Ch4 Ch5 Ch6 ► Ch. 1: Introduction ► Ch. 2: Background and Related Work ► Ch. 3: WLAN Planning Framework Based on Tabu Search ► Ch. 4: Association Management for Wireless Networks ► Ch. 5: Network Monitoring Based on Network Coding ► Ch. 6: Conclusion Part II: Contents Part III: Summary Part I: Background

8. 8 AP Placement and Channel Assignment Modeling the channel assignment and QoS satisfication Closed-form formulations: Minimizing Number of Required AP (MNRAP) and Optimizing Tradeoff Objective (OTOBJ) Tabu Search based optimization framework to solve the formulation Demand Points QoS demand investigation MNRAP OTOBJ Chosen placement points and its channel assignement Tabu search Demand points

9. 9 Related Work Different objectives: previous work only consider one aspect or another  Finding the minimum number of APs to meet the specific QoS requirements of wireless users In [Bejerano2002] and [Chandra2004], the objective is to find the minimum number of gateways to relay traffic between the wired backbone network and the multi-hop wireless networks  Placing the given number of APs to achieve a specific optimal performance This objective can be the sum of the signal strength levels on all mobile users [Rodrigues2000] Minimizing the maximum loads on all APs [Lee2002] A tradeoff objective considering efficiency and fairness [Ling2005] Solving method  Most of heuristic algorithms are based on greedy strategy  State-of-art optimization software: CPLEX

10. 10 Airtime Usage Model for Single Channel Case Interference model  Communication range, interference range  Two communication pairs should not be in interference range of each other Airtime usage (QoS demand/bit rate): Interference MatrixAssociation Matrix

11. 11 Closed Form for Multiple Channels The airtime occupied by the RPs inside its interference range no matter which AP they are associated with the airtime occupied by the APs inside a's interference range used to satisfy the QoS demands of the MUs associated with them Part 1 and Part 2 share some DPs

12. 12 Define Two Optimization Problems Minimizing Number of Required AP (MNRAP) Optimizing Tradeoff Objective (OTOBJ): minimizing F Additional assumption: best-RSSI-based association Both of them are NP-hardness So we focus on using meta heuristic algorithm to find the solution

13. 13 Tabu Search Kind of meta heuristic algorithm like Genetic Algorithm or Simulated Annealing Give chance to loop out of local optima OpenTS (open source tabu search) library is used for my implementation The initial solutions are calculated by greedy-based heuristic algorithm

14. 14 Numerical Evaluation: Validity For regular small topology, it takes 10 mins for optimization software to calculate the optimal solution, the proposed algorithm use 10 secs to get the same results

15. 15 Numerical Evaluation: Scalability Relaxed formulation (ILP) solved by GLPK

16. 16 Ch1 Ch3 Ch2 Ch4 Ch5 Ch6 ► Ch. 1: Introduction ► Ch. 2: Background and Related Work ► Ch. 3: WLAN Planning Framework Based on Tabu Search ► Ch. 4: Association Management for Wireless Networks ► Ch. 5: Network Monitoring Based on Network Coding ► Ch. 6: Conclusion Part II: Contents Part III: Summary Part I: Background

17. 17 Association Management in Wireless Networks Association Management also can be called as AP Selection Control Let each mobile user choose a suitable access point: mostly load balancing issues Default association scheme in IEEE 802.11a/b/g  Best signal strength (RSSI) Performance anomaly problem for multi-rate WLANs [Huesse2003] 11Mbps 5.5Mbps 1Mbps 802.11 DCF designed to give the same chance to for all MNs

18. 18 Related Work Centralized schemes  Bejerano et al. formulate the AP selection for max-min fairness of MU throughput based on integer linear programming and solve it by relaxation and approximation [MobiCom2004]  Kumar et al. have studied AP selection for proportional fair sharing relying on optimization software [NCC2005] Distributed schemes  Fukuda et al. propose a distributed selection scheme that balances the load according to the number of MUs associated with the APs without rate information [VTC2005]  Takeuchi et al. and Siris et al. propose distributed fair algorithms by incorporating the multi-rate information based on IEEE 802.11e protocol [WCNC2006]

19. 19 Two Tiers of Multiple Channel Multiple Interface WMN Backbone (backhaul) layer: wireless mesh AP (MAP), gateway AP is called as mesh portal (MP) Local service layer: mobile nodes associate with MAP’s wireless interface

20. 20 Association Management Formulation for WMN Assuming the maximum uplink rate of each MAP can be measured by itself Through of MAP can not be more than the uplink rate Each MN’s throughput is the simple average of AP’s throughput Formulation of AP selection problem RmRm Efficiency: maximizing all throughputs Fairness: maximizing the lowest throughputs λ ∈ [0,1]: tradeoff weighting factor Maximizing Nonlinear Integer Problem

21. 21 Performance Evaluation The solution can be found by some advanced algorithm like genetic algorithm (GA) etc. I run Lingo to calculate a medium size problem (upto 9 APs and 50 MNs)  Configured with multiple random start seed  Run for 30 mins Fairness is evaluated by The position of MNs are randomly generated

22. 22 Evaluation Results Good tradeoff

23. 23 Distributed Association Management For wireless networks, distributed association management is more preferable  Wireless link is not stable  Centralized management need additional hardware deployment

24. 24 Define the Metric of AP Load AP load: the aggregate period of time that takes AP a to provide a unit of traffic volume to all its associated users Periodical operation on APs

25. 25 Distributed Association Scheme MN AP Probing Reply with current load Estimate load if associated Association Stability

26. 26 Numerical Evaluation Realistic measurement trace from Dartmouth University website The MNs has human mobility

27. 27 Evaluation Results: Efficiency and Fairness

28. 28 NS2 Simulation Results

29. 29 Testbed Prototype Testbed prototype is based on laptop installed with Madwifi-ng AP and MN are modified differently

30. 30 Testbed Prototype: Measured Result

31. 31 Ch1 Ch3 Ch2 Ch4 Ch5 Ch6 ► Ch. 1: Introduction ► Ch. 2: Background and Related Work ► Ch. 3: WLAN Planning Framework Based on Tabu Search ► Ch. 4: Association Management for Wireless Networks ► Ch. 5: Network Monitoring Based on Network Coding ► Ch. 6: Conclusion Part II: Contents Part III: Summary Part I: Background

32. 32 Introduction to Network Coding Generalization of traditional store & forward on router  Information can be operated on in network, not just transported  At beginning, it was proposed to improve multicast traffic

33. 33 Network Monitoring by Network Coding End-to-end network monitoring infers network characteristics by sending and collecting probe packets from the network edges, referred to as Network Tomography Traditional tomography: multicast probing, unicast probing, and per- link monitoring Network coding based approach  More number of links can be identified  Saving network resources by reducing the number of transmissions By observing lots of probing results, maximum likelihood can be applied to estimate the loss rate

34. 34 Ch1 Ch3 Ch2 Ch4 Ch5 Ch6 ► Ch. 1: Introduction ► Ch. 2: Background and Related Work ► Ch. 3: WLAN Planning Framework Based on Tabu Search ► Ch. 4: Association Management for Wireless Networks ► Ch. 5: Network Monitoring Based on Network Coding ► Ch. 6: Conclusion Part II: Contents Part III: Summary Part I: Background

35. 35 Thesis Contributions: Chapter 3 Modeling the channel assignment and QoS demand by airtime usage model A closed-form formulation for two AP placement stages: Minimizing Number of Required AP (MNRAP) and Optimizing Tradeoff Objective (OTOBJ) Proposing Tabu Search based optimization framework to solve the formulation General technique to solve nonlinear optimization problem Plan to use this technique to solve other planning problem, such as wireless sensor network A Tabu Search Based Optimization Framework for IEEE 802.11 WLAN Planning with QoS Guarantees, submitted to COMCOM, Elsevier

36. 36 Thesis Contributions: Chapter 4 Modeling tradeoff between efficiency and fairness in WMN  Analyze the tradeoff and evaluate for fixed and random topologies Distributed scheme  Define AP load metric for multi-rate WLAN for load balancing  Prototype implementation Basically clustering problem Plan to apply it for choosing super node in other type of networks, such as P2P and DTN Distributed multi-hop extension Huazhi Gong, Kitae Nahm and JongWon Kim, Distributed Fair Access Point Selection for Multi-Rate IEEE 802.11 WLANs, IEICE Transactions on Information and Systems 2008, E91-D(4):1193-1196. Huazhi Gong, Kitae Nahm and JongWon Kim, "Access point selection tradeoff for multi-channel multi-interface wireless mesh network," in Proc. of CCNC2007 Huazhi Gong, Kitae Nahm and JongWon Kim, Distributed Fair Access Point Selection forMulti-Rate IEEE 802.11 WLANs, in Proc. of CCNC2008. Dynamic Load Balancing through Association Control of Mobile Users in WiFi Networks, submitted to IEEE Transcation of Consumer & Electronics

37. 37 Thesis Contributions (Intended): Chapter 5 Network tomography based on network coding Monitoring the loss rate of wireless links by sending probing packets Considering the random linear coding feature of wireless networks Still under investigation

38. 38 Publication List Submitted Journals  Dynamic Load Balancing through Association Control of Mobile Users in WiFi Networks, submitted to IEEE Transcation of Consumer & Electronics.  A Tabu Search Based Optimization Framework for IEEE 802.11 WLAN Planning with QoS Guarantees, submitted to COMCOM, Elsevier. International Journals  Huazhi Gong, Kitae Nahm and JongWon Kim, Distributed Fair Access Point Selection for Multi-Rate IEEE 802.11 WLANs, IEICE Transactions on Information and Systems 2008, E91-D(4):1193-1196. International Conferences  Huazhi Gong, Kitae Nahm and JongWon Kim, Distributed Fair Access Point Selection forMulti-Rate IEEE 802.11 WLANs, in Proc. of CCNC2008.  Huazhi Gong, Kitae Nahm and JongWon Kim, "Access point selection tradeoff for multi-channel multi-interface wireless mesh network," in Proc. of CCNC2007.  Huazhi Gong and JongWon Kim, "A multi-channel solution with a single network interface for multi-hop WLAN coverage expansion", in Proc. of ITC-CSCC 2005, Vol. 3, pp815-816, Jun. 2005. (Also presented in Graduate Workshop in KAIST).