Routing and Traffic Engineering in Multi-hop Wireless Networks: An optimization based approach Vinay...
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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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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