Tradeoff Based Network Management for Wireless Networks

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

2. Ch4 Ch3 Ch5 Ch1 Ch2 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 I: Background Part II:Contents Part III:Summary

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 802.11s IEEE 802.11a/b/g

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

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

7. Ch4 Ch3 Ch5 Ch1 Ch2 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 I: Background Part II:Contents Part III:Summary

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 Demand points MNRAP Tabu search OTOBJ Chosen placement points and its channel assignement

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. 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 Matrix Association Matrix

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

16. Ch4 Ch3 Ch5 Ch1 Ch2 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 I: Background Part II:Contents Part III:Summary

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] 1Mbps 5.5Mbps 11Mbps 802.11 DCF designed to give the same chance to for all MNs

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. 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. 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 Rm λ∈[0,1]: tradeoff weighting factor Efficiency: maximizing all throughputs Maximizing Fairness: maximizing the lowest throughputs Nonlinear Integer Problem

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. Evaluation Results Good tradeoff

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

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

27. Evaluation Results: Efficiency and Fairness

28. NS2 Simulation Results

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

30. Testbed Prototype: Measured Result

31. Ch4 Ch3 Ch5 Ch1 Ch2 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 I: Background Part II:Contents Part III:Summary

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. 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. Ch4 Ch3 Ch5 Ch1 Ch2 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 I: Background Part II:Contents Part III:Summary

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. 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, "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. 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. Dynamic Load Balancing through Association Control of Mobile Users in WiFi Networks, submitted to IEEE Transcation of Consumer & Electronics

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. 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).