Quality of Service for Flows in Ad-Hoc Networks SmartNets Research Group Dept of EECS, UC Berkeley...

Quality of Service for Flows in Ad-Hoc Networks

SmartNets Research GroupDept of EECS, UC BerkeleyNMS PI Meeting, Nov 2004

SmartNets Group, U C Berkeley

DARPA NMS PI Meeting, Nov 2004

Group Members

Faculty Jean Walrand Pravin Varaiya Venkat

Anantharam David TseIndustry David Jaffe (Cisco)Staff Bill Hodge

Students Antonis Dimakis Rajarshi Gupta Zhanfeng Jia John Musacchio Wilson So Teresa TungAlumni Eric Chi Linhai He Jun Shu



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



Application Scenario

Batallion of tanks Support flows with QoS

Video streaming Urgent communications



Overview

Capacity Estimation

Scheduling

Clustering

Routing



Overview

Capacity Estimation Clique-based Constraints Measurement Approximation

Scheduling Clustering Routing



Conflict Graph (CG) Model interference as

CG Link in G is represented by

vertex in CG Edge in CG if the two links

interfere

2

31

45

A

CB

E

Dinterference

E

CD

B

A

Connectivity Graph Conflict Graph

2

31

45

A

CB

E

Dinterference

Connectivity Graph

2

31

45

A

CB

E

D

Connectivity Graph

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



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



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



Overview Capacity Estimation

Scheduling Limitations of Local Scheduling Fair Scheduling (Impatient Backoff Algorithm) Multi-Channel MAC

Clustering Routing



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



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



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



Overview

Capacity Estimation

Scheduling

Clustering

Routing



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



Cluster Based RoutingSource

Dest



Overview Capacity Estimation Scheduling Clustering

Routing Ad-Hoc Shortest Widest Path (ASWP) Interference-aware QoS Routing (IQRouting) Measurement-based Routing



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



IQRoutingInterference 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



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

1 2 3 4 5R



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



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

Thanks DARPA

Project Group Website:http://

smartnets.eecs.berkeley.edu

Quality of Service for Flows in Ad-Hoc Networks SmartNets Research Group Dept of EECS, UC Berkeley...

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