Modeling the performance of DCF in 802.11 mesh networks

Andrew Symington, DNA Group

Data Network Architectures Group 2

Presentation Outline

• Background

• Wireless Networks and IEEE 802.11

• The Distributed Coordination Function

• Performance Analysis & Modeling

• Modeling the Losses Incurred by DCF

• The IEEE 802.11 Test Bed

• The Bigger Picture

• Questions

Data Network Architectures Group 3

My Background & Future

• Business Science (Computer Science) at UCT

• Currently pursuing an MSc in DNA Group

• Would like to continue to PhD in the UK

• No long-term academic path (yet)

• Plan for the future

– Embedded Systems

– Wireless Telecommunication

– Or a combination of the two!

Data Network Architectures Group 4

DNA Group Research Areas

• Group is fairly new (<2yrs) to wireless networks

• Wireless research focus currently on :

– General Mesh Networks

• 802.16 and wireless Internet delivery

– 802.11 Security

• OLSR - previous talk

– 802.11 Performance Modeling

• DCF

Data Network Architectures Group 5

Wireless Topologies

Possible Topologies

• Point-to-point (line replacements)

• Point-to-multipoint (infrastructure)

• Multipoint-to-multipoint (ad hoc)

– Mobile ad hoc networks (MANETs)

– Mesh networks

Infrastructure

Ad hoc

Data Network Architectures Group 6

IEEE 802.11 Background

• Set of standards guiding WLAN development

• Split OSI Data Link layer into MAC and LLC

• IEEE 802.11n in draft - promises 248 Mbps

– Theoretical vs. Actual rates differ

LLC Flow control & Multiplexing

MAC

MAC-level Security

Distributed

Coordination

Function

Point Coordinator

Function

Enhanced

Distributed Channel

Access

PHY802.11

IR

802.11

FHSS

802.11

DSSS

802.11a

OFDM

802.11b

HR-DSSS

802.11g

OFDM

Data Network Architectures Group 7

The Distributed Coordination Function

• Medium Access Control technique

• Ensures equal, but NOT fair, access to the channel

• A form of CSMA/CA

– CSMA/CD not possible with wireless

– Uses Exponential Binary Back-off

• Inter-frame Spaces define frame priority

• Designed for a Best-Effort service

– No service guarantee

– No QoS for multimedia

Data Network Architectures Group 8

Factors Affecting DCF

• Collisions Recovery

– Hidden Nodes

– Exposed Nodes

• RTS / CTS

– Longer Handshake

– Reduces Error

• Increased Nodes

• Increased Traffic

• Topology

Data Network Architectures Group 9

My Research Focus

• “Modeling the performance of the Distributed Coordination Function in 802.11 mesh networks”

– Performance : Degree of QoS provided

– Acknowledge that DCF is inefficient

• Thus, existence of 802.11e

– Exacerbated by mesh networks

• Number of hidden / exposed nodes

– But, by how much? And can we predict it?

– Pave the way for EDCA analysis

Data Network Architectures Group 10

What is Quality of Service?

“A set of qualities relating to the collective behaviour of one or more objects” - ITU X.605

• More specifically, for IEEE 802.11

– Throughput

– Response time

– Jitter

– Packet loss

• Increasingly NB for multimedia

• IEEE 802.11e and IEEE 802.11T (draft)

– Not widely-adopted

Data Network Architectures Group 11

Modeling Process

• Developing an analytic model is not a trivial process

• It’s NOT Simulation!

• Workloads NB

– Trace

– Synthetic

Data Network Architectures Group 12

Existing Analytic Models for DCF• Bianchi’s Model is widely-adopted

• DCF back-off modeled as Markov process

• Assumes a constant collision probability p

Stage

Back-off Counter

Data Network Architectures Group 13

Bianchi’s Model

• p is a function of the number of contending nodes

• Using the Markov model, Bianchi derives :– The probability of successfully

transmitting in a randomly chosen slot time

– And, using , derives the normalised expected throughput at a node

• Normalised Throughput

– Fraction of the base PHY rate offered

– Indicates losses due to DCF overhead

Data Network Architectures Group 14

Integration with MicroSnap

• Tool written as a DNA Project

• MicroSnap models stochastic queuing networks

• Environment Comprised of

– Service Centres

– Workloads

– Traffic Classes

– Routes (static)

• Program in MicroSnapL interface language

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Fitting Models to MicroSnap

• Convert wireless links service centers

Data Network Architectures Group 16

Workflow Diagram

CONCLUDE

DERIVE MACHINE MODEL FOR MESH DCF

GENERATE TEST CASES (WORKLOADS/METRICS)

EXPERIMENTAL(TEST BED)

COMPARE RESULTS

ANALYTIC(MICROSNAPL)

SIMULATION(OMNET++)

Data Network Architectures Group 17

The Mesh Test Bed

• The DNA Group is currently assembling an 802.11 multi-hop wireless mesh network

• The DNA Group feels that the test bed will

– Compliment research

– Strengthen results

– Provide a means to implement

and test concepts

– Attract students to the field

Data Network Architectures Group 18

Progress on the Test Bed

• Short term goal of 9 nodes (double within a year)

• Already purchased a large portion of the hardware

• Challenges

– PCI 2.1 / 2.2 Incompatibility

– Limiting Signal

• Noise Injectors

• Attenuators

– Interference

• More questions? Speak to me afterwards!

Data Network Architectures Group 19

The Bigger Picture

• DNA Group is developing a suite of tools for wireless network performance modeling

– Analytical

– Simulation

– Experimental (via test bed)

• Paolo Pileggi currently developing the framework as part of Honours project

• The work done in my research will contribute to a module within the IEEE 802.11 MAC

• More specifically, it will assist in generating the machine model for arbitrary mesh networks

Data Network Architectures Group 20

Thank you!

Questions?