1. Quality of Service for Flows in Ad-Hoc Networks SmartNets Research Group Dept of EECS, UC Berkeley NMS PI Meeting, Nov 2004

2. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Group Members Faculty Jean Walrand Pravin Varaiya Venkat Anantharam David Tse Industry David Jaffe (Cisco) Staff Bill Hodge Students Antonis Dimakis Rajarshi Gupta Zhanfeng Jia John Musacchio Wilson So Teresa Tung Alumni Eric Chi Linhai He Jun Shu

3. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Summary Distributed MAC protocols that achieve enhanced throughput and fairness for multi-hop flows Theoretical QoS routing algorithms Graph model of interference Practical QoS Routing mechanisms Suitable clustering decouples interference effects On-line measurements and distributed computation Improved admission ratios

4. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Application Scenario Batallion of tanks Support flows with QoS Video streaming Urgent communications

5. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Overview Capacity Estimation Scheduling Clustering Routing

6. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Overview Capacity Estimation Clique-based Constraints Measurement Approximation Scheduling Clustering Routing

7. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Conflict Graph (CG) Model interference as CG Link in G is represented by vertex in CG Edge in CG if the two links interfere Compute cliques Polynomial approximation Distributed algorithm Localized information ‘Clique’ in CG Clique = set of links that interfere with each other e.g. AED, ADC, ABC Cliques are local structures Only one link in a clique may be active at once

8. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Clique-based Constraints Assuming constant interference range, feasible schedule exists if scaled clique constraints are satisfied on a conflict graph Scale capacity of each link by a constant factor  0.46 Used to determine the available capacity of a link Variance in interference range Model interference range varying between [x,1] Then, need to scale the clique constraints by a smaller factor e.g. if range varies between [0.7,1], need to scale by 0.33 Only pessimistic bounds for networks with obstructions

9. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Measurement Approximation Used in dynamic/mobile environment where underlying graph is difficult to maintain Calculating available bandwidth Instead of summing the rates on links of a clique Every node measures fraction of idle time A link’s available bandwidth is upper bounded by the transmitter node’s idle time X link speed Use link state protocol to share available bandwidth information across network

10. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Overview Capacity Estimation Scheduling Limitations of Local Scheduling Fair Scheduling (Impatient Backoff Algorithm) Multi-Channel MAC Clustering Routing

11. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Limitations of Local Scheduling How to schedule transmissions in an ad-hoc network ? Optimal schedulers require global coordination, so not practical for distributed MAC What is the throughput of local scheduling algorithms ? Idealization: iterated Longest Queue First (iLQF) Nodes with longer backlogs (try to) transmit first Results Achieve maximal throughput in tree conflict graphs Instability in cyclic conflict graphs

12. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Fair Scheduling Exponential backoff (e.g. 802.11) is unfair towards nodes in middle of network Propose new ‘Impatient Backoff Algorithm’ Encourage nodes to be more aggressive upon collision If quiet/collide: decrease backoff time If succeed: increase backoff time Reset all backoffs if any backoff becomes too small Markov analysis shows stability Simulation on random topologies Comparable throughput to exponential backoff Significantly higher fairness

13. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Multi-Channel MAC (McMAC) Protocol Node pairs sending simultaneously on different channels increases capacity Challenge: How do nodes know which channels are being used by neighbors ? Approach Each node hops slowly according to pseudo-random sequence Broadcast seeds of sequences so neighbors can track each other Preliminary Result Random hopping load-balances traffic over all channels Utilizes channels effectively when traffic is uniform

14. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Overview Capacity Estimation Scheduling Clustering Routing

15. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Clustering Benefit: Localizes accounting of network resources Limits the effects of network changes Decouples interference effects Suitable Clusters Nodes within a cluster share a common constraint Minimize interference across clusters Algorithms K-hop Clustering Damped Clustering

16. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Cluster Based Routing Source Dest

17. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Overview Capacity Estimation Scheduling Clustering Routing Ad-Hoc Shortest Widest Path (ASWP) Interference-aware QoS Routing (IQRouting) Measurement-based Routing

18. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Ad-Hoc Shortest Widest Path ASWP design goals: Shortest Widest Path Bellman-Ford type algorithms are sub-optimal Path width Defined by the bottleneck clique Distributed computation of one-hop extended path Done with local clique information ASWP heuristic Bellman-Ford architecture Keep k records at each node

19. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 IQRouting Interference Aware QoS Routing Link state protocol distributes link utilization values Source chooses candidate paths based on local info Source selects path metric (width or utilization) Send probe packets along each candidate path Widest Shortest Path (WSP) WSP complement Shortest Feasible Path OSPF-like weighted path cost (  + used capacity) Shortest Widest Path (SWP) Chosen metric accumulated (min or sum) along path Final path (best metric) confirmed by destination

20. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Measurement-based Routing Trial flow refines estimates of available bandwidth Admission Control Try a flow with constant rate R Trial packets have lower priority (802.11e) Admit if network accommodates rate R Otherwise only fraction p of the packets acknowledged Notify source that at failure, only at most pR available Allow higher priority flows multiple probes Failures provide a more accurate estimate of network resources 12345 R

21. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Comparison of Routing Matlab Simulation Comparison Against Shortest Path (SP) OSPF ILP Improvement over OSPF, ILP Src/Dst Within Small Area <10% improvement over SP Few Path Choices Src/Dst Within Large Area >10% improvement over SP Multiple Path Choices

22. SmartNets Group, U C BerkeleyDARPA NMS PI Meeting, Nov 2004 Contributions Capacity Estimation Clique-based Constraints Measurement Approximation Scheduling Limitations of Local Scheduling Fair Scheduling (Impatient Backoff Algorithm) Multi-Channel MAC Clustering Algorithms Routing Ad-Hoc Shortest Widest Path (ASWP) Interference-aware QoS Routing (IQRouting) Measurement-based Routing

23. Thanks DARPA Project Group Website: http://smartnets.eecs.berkeley.edu