Routing and Traffic Engineering in Multi-hop

Wireless Networks:An optimization based

approach Vinay KolarPh.D. Candidate

SUNY, Binghamton

Advisor: Dr. Nael Abu-Ghazaleh

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Multi-hop Wireless Networks (MHWNs) Wireless nodes co-operate to

forward traffic.

Minimal infrastructure demands

Extensive applications: Mesh Networks, Vehicular

Networks, Sensor Networks, Ubiquitous computing, …

Figure 1

Figure 2

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New challenges

Vagaries of wireless channel Complex interference

patterns, sparse bandwidth

Self-configuration Mobility, energy-constrained

Delivering packets across multiple wireless hops – The Routing Problem

Figure 3

Figure 4

A B C

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High-level motivation

Theory

Systems

Formal MHWN models Heuristic solutions

Complex problem domain + Idealistic

assumptions

Incomplete characterization of parameter space

GOAL

Practical, theoretically

grounded models

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High-level motivation

Deriving protocol behavior from mathematical models has been proven to be effective in wired networks (e.g. FAST-TCP)

However, main challenge in MHWNs Modeling interference

At wireless channel – Physical layer At neighborhood – MAC layer Across end-hosts – Routing layer

Substantially different from the wired network models

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Goals of my work

Develop practical interference-aware models for MHWNs Efficient routes Accounting for realistic effects (CSMA scheduling) Low-complexity

Applications: Develop near-optimal distributed protocols Performance analysis of existing protocols Insightful for the analysis of MHWNs QoS, Resource allocation, Provisioning,… Some are long term…

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Background

Physical layer How does the signal propagate?

Signal fading with distance

MAC layer How to send packet to neighbors?

Routing layer How to route across multiple hops?

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Background – MAC Protocol

Carrier Sense Multiple Access/ Collision Avoidance (CSMA/CA)

Primary Issues: Hidden-terminals

Packet collision Interference effect

Exposed terminals Conservative transmissions

Under-utilization of channel capacity

A B C

AB C D

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Background – IEEE 802.11

Prominent MAC protocol standard Handshake – Basic, RTS/CTS Rules

Wait for certain time before sending Exponential backoff on packet collision

In Summary: All CSMA issues not prevented Subtle changes in node positions can lead to

drastically different results [Garetto05]

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Background – Routing layer

Transmit packet from source to destination Possibly across multiple hops

First generation routing protocols Choose path with shortest number of hops But …

Shortest number of hops longer hop length Higher probability for errors

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Background – Second generation Routing Link quality aware routing

Transmit packets across stronger links Lesser packet collisions Efficient use of

channel Greater performance

Is this enough? Do they estimate other parameters?

Channel capacity, greedy forwarding

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Overview

Motivation and Related work Contributions

Interference Aware Routing Decomposition based Routing model

Scheduling Effects Interaction representations and Contention fairness

Conclusions Future Work

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Motivation

Recall: MHWN model is … Insightful for analyzing MHWNs Applications of Traffic-engineering models Development of near-optimal distributed protocols

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Motivation – An example

Routes may not be interference separated (even with link-quality aware protocols)

Blue nodes suffer interference from 2 connections Can greedy approaches lead to optimal routes?

Figure 10

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Motivation – Practical Design

Some layers are harder to modify than others Physical (?), MAC (?)

Approach: Reverse-engineering MAC and Physical

Capture the behavior Forward-engineering Higher Layer protocols

Optimize them

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Related work – Routing models Interference separated routes

A network-flow based model [Jain03, Kodialam03]

The focus is to calculate network capacity

Shortcomings: Optimal scheduler

Scheduling effects due to IEEE 802.11 Split route Interaction among multiple connections

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Related work – Scheduling models Models to capture scheduling details [Garetto05]

Detailed stochastic models

But… Input is a set of active links Cannot directly calculate routes

Can we use it inside a routing model to evaluate candidate solutions? Iterative in nature – Long run times

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Overview

Motivation and Related work Contributions

Interference Aware Routing Scheduling Effects

Conclusions Future Work

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Contributions

Interference-aware routing model Maximize throughput, Minimize delay Complexity

An efficient decomposition based model

Interference at MAC/PHY layer Interaction graphs Scheduling-aware routing Accuracy

Contention fairness Throughput under two link interaction

Towards a unified framework for traffic-engineering…

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Overview

Motivation and Related work Contributions

Interference Aware Routing (IAR) Scheduling Effects

Conclusions Future Work

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Interference Aware Routing (IAR)The problem:

Find interference separated routes in a given topology

The approach: Model routing as Network-flow optimization

problem

Multi-commodity flow problem ‘n’ sources and sinks

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IAR model – Goals

Interference separated routes Maximize throughput, minimize delay for all

connections Single path for each route

Model realistic single-path routing protocols No packet splitting, multi-path routing

No path-inflation, connection coupling Single Linear objective

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IAR – Shape of routes Figure 11

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IAR – Results

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From IAR to …

Complexity The model is Mixed Integer Linear Program

An NP-hard problem Cannot analyze medium/large networks

Approximate polynomial time algorithm

Ideal vs. CSMA Scheduling Coarse estimates of busy time does not capture

CSMA behavior under high loads Scheduling aware routing formulation

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Overview

Motivation and Related work Contributions

Interference Aware Routing (IAR) Decomposition based Routing model (d-IAR)

Scheduling Effects Scheduling-aware routing model (SAR)

Improving the accuracy Contention fairness, throughput estimation

Conclusions Future Work

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Decomposition model

The problem: Approximate the NP-hard IAR routing to a

polynomial time algorithm

Why decomposition? Enables routing in larger networks Effective distributed protocol development

[Chiang07] Parallel implementations

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Decomposition – Simulation study Performance

comparable to IAR model

Much better than the “best” DSR routes Under smaller

connections Orders of

magnitude improvement in run time

Figure 14

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IAR d-IAR …?

Complexity has been reduced

But … Ideal v/s CSMA Scheduling The scheduling effects take

a toll as the density of the traffic increases

Higher level abstractions like ‘Commitment Period’ not enough

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Overview

Motivation and Related work Contributions

Interference Aware Routing (IAR) Decomposition based Routing model (d-IAR)

Scheduling Effects Scheduling-aware routing model (SAR)

Improving the accuracy

Conclusions Future Work

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Scheduling Aware Routing (SAR) The problem:

A routing model that is aware of interference and scheduling effects

The approach: Run the d-IAR model Capture detrimental scheduling interactions Rate scheduling + exclude detrimental links Re-Run d-IAR

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Scheduling model

Do we need a new scheduling model?

Why cant existing accurate scheduling models be used? [Garetto06] The scheduling model is

evaluated for each candidate IAR route Iterative

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Capturing scheduling - Interaction graphs

Convert a network scenario to a “Conflict graph” [Vaidya02] Each active link is a node An edge indicates that these nodes can transmit concurrently

Figure 16

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Interaction graphs (IGs)

Pairwise IGs – Insufficient to capture temporal interactions

Concepts : Compute “Maximal

Independent Contention Set (MICS)” A problem of ‘Maximal

independent set’ Red lines indicate

hidden terminals

Figure 17

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Interaction graphs

Figure 16

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Finding scheduling effectiveness Construct the MICS for a given protocol and capture

detrimental interactions IEEE 802.11 with RTS/CTS mode

RTS timeouts and DATA packet collisions

Find the probability of packet drops for each link Need to find out the probability that each MICS will occur

Detailed computation is rigorous

Compute conflicting links and the overall link quality

We refer to these link quality metrics as “Interaction Based Link Rating” (IBLR)

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SAR – Results

Figure 19

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IAR, d-IAR, SAR - What next?

Can we improve the scheduling model? Low-complexity Realistic physical models

Important results that can be used in the routing model Probability of MICS activation

Unfairness and effect of minimum backoff window Quantify the hidden-terminal effect

Effect of backoff/unfairness

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Overview

Motivation and Related work Contributions

Interference Aware Routing (IAR) Scheduling Effects

Improving the accuracy Contention fairness

Conclusions Future Work

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Scheduling – Contention Fairness The problem:

Even in the absence of Hidden Terminals, CSMA is far away from ideal scheduling

Example: Flow in the middle (FIM) Link B starves due to link A

and/or link C Link B gets only 2 % of the

total throughput!! Why?

Figure 20

Figure 21

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Scheduling – Contention fairnessExample steps : Distribution of the “channel

idle” times Renewal-reward theory

Expected rate of transition between MICS

Get limiting probabilities of MICS Continuous-time Markov

Compute the throughput

Figure 22

Time

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Contention Fairness – Random Topology

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Contention fairness - Results

Can we dynamically alter backoff to avoid starvation?

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Contention Fairness - Protocol Contention-aware

Adaptive Backoff

Communicate contention information

Adapt backoff w.r.t. neighbors contention information

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Overview

Motivation and Related work Contributions

Interference Aware Routing (IAR) Scheduling Effects

Conclusions Future Work

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Conclusions

A routing model for capturing interference MHWN operation as an optimization problem Low-complexity

Capturing scheduling effects Low-complexity Integrated Interference and Scheduling Aware routing

Traffic-engineering under realistic schedulers Improve the accuracy

Interaction graphs Contention fairness Throughput computation for two-flows

A design towards effective distributed solutions

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Overview

Motivation and Related work Contributions

Interference Aware Routing Decomposition based Routing model

Scheduling Effects Interaction representations and Contention fairness

Conclusions Future Work

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Future Work

Long-term Route-planning tool Formally designing distributed protocols [Chiang07].

Short-term Extending hidden-terminal behavior Integrating Contention-fairness and Hidden terminals Unsaturated traffic Capturing the pipelining effect in routes

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Thank you all

Questions/Comments

Email id: vinkolar@cs.binghamton.edu

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References

[Chiang07] Chiang, M., Low, S. H., Calderbank, A. R., and Doyle, J. C. Layering as optimization decomposition: A mathematical theory of

network architectures. In Proceedings of IEEE (2007). [Garetto05]

Garetto, M., Shi, J., and Knightly, E. W. Modeling media access in embedded twoflow topologies of multi-hop wireless networks. In MobiCom ’05 (2005).

[Garetto06] Garetto, M., Salonidis, T., and Knightly, E. W. Modeling per-flow throughput and capturing starvation in CSMA multi-hop wireless

networks. IEEE INFOCOMM (2006). [Jain03]

Jain, K., Padhye, J., Padmanabhan, V. N., and Qiu, L. Impact of interference on multi-hop wireless network performance. In MobiCom (2003).

[Kodialam03] Kodialam, M., and Nandagopal, T. Characterizing achievable rates in multi-hop wireless networks: the joint routing and scheduling

problem. In MobiCom (2003). [Vaidya02 ]

Yang, X., and Vaidya, N. H. Priority scheduling in wireless ad hoc networks. In MobiHoc ’02: Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing (New York, NY, USA, 2002), ACM Press, pp. 71-79.

[Razak07] S. Razak, V. Kolar and N. B. Abu-Ghazaleh, "Modeling and Analysis of Two-Flow Interactions in Wireless Networks", IEEE/IFIP

WONS 2007.

Figure 1: http://ntrg.cs.tcd.ie/undergrad/4ba2.05/group11/roof_top.jpg Figure 2: http://www.dsta.gov.sg/DSTA_horizons/2006/Images/Mobile_Fig1b.jpg Figure 4: http://www.stanford.edu/~zhuxq/adhoc_project/overview.jpg Figure 4a, 4b: http://pdos.csail.mit.edu/roofnet/doku.php?id=interesting Figure 4c: http://www.usenix.org/events/mobisys05/tech/full_papers/youssef/youssef_html/index.html