Self-Optimising Millimeter Mesh Networks: Architecture ... · Self-Optimising Millimeter Mesh...

Sterbenz, et al.ITTCWeather Disruption-Tolerant

Self-Optimising Millimeter Mesh Networks:Architecture, Routing Protocols, Performance

22 April 2009

James P.G. Sterbenz,Abdul Jabbar, Justin Rohrer, Egemen Çetinkaya,

Bharatwajan Raman, Victor Frost

Department of Electrical Engineering & Computer ScienceInformation Technology & Telecommunications Research Center

The University of Kansas

[email protected]

http://www.ittc.ku.edu/~jpgshttp://wiki.ittc.ku.edu/resilinets

© 2007–2009 Sterbenz

22 April 2009 Weather Disruption-Tolerant Mesh Networks 2

Sterbenz, et al.ITTC

WDTN Mesh NetworksAbstract

With growing demand for high-speed access to mobile handheld devices, there is a significant cost benefit in deploying fixed wireless-mesh networks for backhaul access. However, enabling reliable broadband access over high-frequency radios (such as millimeter-wave networks) posses a fundamental challenge due to weather disruptions in general and rain attenuation in particular. In this paper, we present an analysis of the impact of precipitation on millimeter-wave mesh networks based on radar measurements of real storms in the Midwest US. Furthermore, we compare two novel algorithms that use physical-layer information to optimize routing at the network layer: P-WARP (Predictive Weather-Assisted Routing Protocol) and XL-OSPF (Cross-Layered Open Shortest Path First). Finally, we present simulation studies to compare the performance of the proposed protocols and evaluate the dependability of the end-user service during weather disruptions.



WDTN Mesh NetworksOutline

• Introduction and motivation• Millimeter-wave mesh networks• Impact of weather disruptions• WDTN algorithms• Simulation model and performance analysis



MotivationWireless vs. Fiber Optic Links

• Wireless data access for untethered network access– supported by wired backbone– fiber optic cables provide high-speed reliable connections⇒ increasingly wireless access to an optical core (MAN+WAN )



MotivationWireless vs. Fiber Optic Links

• Wireless data access for untethered network access– supported by wired backbone– fiber optic cables provide high-speed reliable connections⇒ increasingly wireless access to an optical core (MAN+WAN )

• Deployment barriers to fiber– extremely expensive: ~ $100K/mile– lack of extensive market: rural regions

or– construction and regulatory issues: metropolitan cities

• barrier to new service providers• expensive to lease competitor’s capacity for backhaul



MotivationBroadband Wireless Links

• Wireless alternative to traditional fiber-optic links– point-to-point wired link replacement in MANs and WANs– mesh between base stations and cell towers

• Potential use– backhaul of 3G (and eventually 4G) data– Internet expansion to new areas, such as rural– alternative to add capacity in congested areas, such as cities



MotivationMillimeter-Wave Wireless Links

• Millimeter-wave communication links– exploit recently available commercial radios

• frequency band: 70 – 90 GHz• lightly licensed by FCC in US

– higher data rate potential than microwave links• very-high data rate: 1 – 10 Gb/s• potential replacement for 1/10 GbE and OC-14/192

– significantly cheaper to deploy than fiber



MotivationMillimeter-Wave Wireless Links

• Disadvantages of millimeter-wave links– highly susceptible to weather

• significant rain-based attenuation

⇒unreliable high-speed links• do not meet carrier requirements for backhaul and distribution• reliability, delay, 50 ms restoration



MotivationWeather Disruption-Tolerant Routing

• Problem: slow recovery from rain-based attenuation– many frames lost before detection– no inherent restoration equivalent to SONET/SDH APS

• Proposed solution: compensate at network layer– new routing mechanisms

• predictive• nearly-instantaneous reactive

– exploit weather radar information• permits short-term prediction• weather dynamics longer time scale than network control



WDTN Mesh NetworksMillimeter-Wave Mesh Networks




Millimeter-Wave Mesh Networks Architecture

• Mesh architecture– high degree of connectivity

802.163–4G

CO/POP




• Mesh architecture– high degree of connectivity– alternate diverse paths

• mm wave link to CO/POP• alternate mm links

802.163–4G

CO/POP





• severely attenuated mm wave• alternate mm links• alternate lower-freq. RF• fiber bypass (competitor)

• Proposed solution– route around failures

• before they occur

– avoid high error links

802.163–4G

CO/POP



Millimeter-Wave Mesh Networks Open Research Questions

• Impact of weather on mesh network– correlated link failures, link availability

• Actual weather pattern– storm frequency and intensity– geographic coverage with respect to mesh network

• Feasibility– is this approach feasible?– field measurements on deployed infrastructure



WDTN Mesh NetworksImpact of weather disruptions

• Introduction and motivation• Millimeter-Wave Mesh Networks• Impact of weather disruptions• WDTN algorithms• Simulation model and performance analysis



Impact of Weather Disruptions Link

• Impact of weather on individual links• ITU-R P.530 and Crane models

– relates link attenuation to a homogenous rain rate • long term statistics of precipitation probabilities

– cannot predict availability easily of a particular link– does not address correlated link failures of a mesh network– not useful for tolerance of specific events on specific links



Impact of Weather Disruptions Approach

• Evaluate impact of real storms on actual mesh links– collect data on observed storms at a given location– translate storm data to link attenuation

• using geometric model of actual storm

– characterise link behavior and availability during an event



Impact of Weather DisruptionsGeometric Storm Modeling

• Storm model is abstraction of precipitation intensity– cells modelled as ellipses moving along a trajectory– links modelled as line segments– effective attenuation calculated based on link & cell overlap

A B

C

green

red

red

yellow

red

Rs

R1

R2 R3

R4

A B

Cgreen

red

yellow



Impact of Weather Disruptions Actual Observations

• Radar reflectivity data from national weather service• Evaluate effect of real storms on potential networks• Collected and analyzed data

– ~ 30 storms over the Midwest U.S– 3 hypothetical mesh networks in the same region



Impact of Weather Disruptions Observed Storm1 – Rain Distribution

• Millimeter-wave grid location– 38.8621N, 95.3793W

• Storm observed at: – 20:39:26Z 30 Sep 2008



Impact of Weather Disruptions Observed Storm2 – Rain Distribution


• Storm observed at: – 05:04:11Z 22 Apr 2008



Impact of Weather Disruptions Link Characterization

• Channel error rate for links of a 4×4 grid – 10 sec intervals– few links

severelydegraded

– large numbereitherpartiallydegradedor normal



Impact of Weather DisruptionsLink Availability

• Link availability after FEC– Reed Solomon (204,188)

• Link states– three states1: BER < 5×10–8

2: 5×10–8 < BER < 5×10–5

3: BER > 5×10–5

• Modelled asMarkovprocess



Impact of Weather Disruptions Link State Characteristics

• Link state characteristics– state transition probability matrix– state probabilities:

probability of being in each state at given time

State 1 2 3State

Probability0.00046 0.37461

0.40449

0.22091

0.01063

0.98891

0.00459

0.98963

0.00578

1 0.99554

2 0.00417

3 0.00029



WDTN Mesh NetworksWDTN Algorithms




Weather Disruption-Tolerant Routing Alternative Algorithm Types

• Reactive– frames in transit are lost– time to converge on new route

• time to detect frame loss > 50 ms restoration• use weather radar to react instantaneously

• Predictive– re-route traffic before link failure– use weather radar to predict the link condition– advance warning in the order of minutes is sufficient



12 13 14 15

8 9 10 11

4 5 6 7

0 1 2 3

Weather Disruption-Tolerant RoutingPredictive Routing Concept



Weather Disruption-Tolerant Routing New Algorithms

• P-WARP: predictive weather-assisted routing prot.– uses weather-radar data to forecast future link conditions– precipitation modelled as {none/light, moderate, heavy}– effective BER calculated and used to adjust link cost

• XL-OSPF: cross-layered OSPF– uses radar to instantaneously estimate attenuation– conventional OSPF but without dead interval detection



Weather Disruption-Tolerant Routing P-WARP

• Predicting link conditions– predictive routing algorithm using weather radar data– effective BER is calculated from radar reflectivity data

• modelled in real-time using the geometric model

– processing done at a core or a subset of nodes• edge nodes with external (Internet) access

• Cost metric– per link cost calculated as

• Cij = cost of link i ↔ j• P = average frame size• BERij = predicted BER• γ = sensitivity factor

Cij = P ×BER ij × γ




• Link status updates– WLSUs: weather-based link status updates– contains the predicted cost of all links (incremental)

• WLSUs are flooded in to the network by core nodes– single update for all links– generated only when one or more link costs change– significantly reduces protocol overhead




• Route recomputation– nodes recompute routes using shortest paths first algorithm

• individual nodes do not generate separate LSAs

– network reroutes traffic ahead of the disruption– weather predictions in the order of seconds are sufficient

• route reconvergence is sub second

• Route sensitivity– route flaps avoided using thresholds and hysteresis– changes in BER below BERthresh are ignored– Hthresh is the minimum change in cost that triggers an update



Weather Disruption-Tolerant Routing XL-OSPF

• Standard OSPF– dynamic link state algorithm

• Reacts too slowly causing end-to-end packet loss– link costs do not reflect physical link status– dead interval needed to detect failed links– route convergence is slow




• Cross-layered OSPF– cost metric is proportional to bit error rate

• several mechanisms exist to achieve this reactively– e.g. packet error estimation at the receiver

• could use current weather data to calculate link BER

• Route computation– nodes aware of the quality of all their links– LSAs from other nodes give the complete network status– shortest paths calculated based on link metric– reroute traffic reactively




• Cost metric (same as P-WARP)– per link cost calculated as

• Cij = cost of link ij,• P = average frame size• BERij = predicted BER,• γ = sensitivity factor

• Compared to P-WARP– differs in the mechanism to calculate link costs– reactive instead of predictive– higher overhead, generates per link LSAs

Cij = P ×BER ij × γ



WDTN Mesh NetworksSimulation Model and Performance Analysis




SimulationsSimulation Model and Parameters

• ns-2• 16 node mesh: 4×4 grid

– two corner sink nodes connected to Internet (0, 15)– other 14 nodes generate traffic to randomly chosen sink– 2.4 Mb/s CBR over UDP

• Several synthetic storms…– we will look at one example

• Several actual storms…– we will look at one representative example



SimulationsSample Storms

• Synthetic storm– outer ellipse: ~ 30 × 20– four inner ellipses: ~ 5 × 10

• Actual storm– storm 1 from slide 18



SimulationsSynthetic Storm Overlaid on Lawrence, KS

D2

D1

low intensity rain

heavy intensity rain



Synthetic StormPerformance Analysis: Packet Loss

nodeout

linkout

40s OSPF dead interval

10s XL-OSPF HELLO interval



WDTN RoutingPerformance Analysis: Packet Loss

• 100% packet delivery ration with no precipitation• Link attenuation:

– P-WARP reroutes predicatively with no loss– XL-OSPF reroutes with minimal loss after HELLO interval– conventional OSPF must wait for loss detection and recovery

• Node out– attenuation of all links to given node– transit traffic rerouted– but packets sourced and sinked lost until one link returns

• end-to-end recovery necessary



Synthetic StormPerformance Analysis: Cumulative Loss


10s OSPF HELLO interval



WDTN RoutingPerformance Analysis: Cumulative Loss

• Cumulative loss statistics– track and compare overall availability during storm event– P-WARP slightly better than XL-OSPF– P-WARP and OSPF significantly better than standard OSPF– static shows worst-case baseline



SimulationsObserved Storm in Northeast Kansas


• Storm observed at: – 20:39:26Z 30 Sep 2008



Observed StormPerformance Analysis: Packet Loss


node out

link out


10s XL-OSPF HELLO interval



Observed StormPerformance Analysis: Cumulative Loss





Observed StormPerformance Analysis: Delay





WDTN RoutingPerformance Analysis: Delay

• Delay proportional to number of hops– rerouting adds some delay– but not significant within a metro-area mesh

• Congestion avoidance– overprovisioning of mesh essential to avoid congestion– simulation studies based on past precipitation events– drives network engineering for a given network



WDTN Routing Availability


10s OSPF HELLO intervalProtocol Availability

synthetic storm Availability

observed storm

P-WARP 0.9638 0.8834

0.8782

0.7313

0.6872

XL-OSPF 0.9554

OSPF 0.9209

Static 0.7304

• Availability– during storm presence in grid neighbourhood– overall availability much higher

• majority of time no storms are present



WDTN Mesh NetworksConclusions

• Overcomes a fundamental limitation – millimeter wave links in the presence of weather events

• Demonstrates a resilient network architecture– P-WARP slightly better than XL-OSPF

• difference important for loss-sensitive traffic

– XL-OSPF significantly better than conventional OSPF– still affected by node outage (storm cell over tower)

• Real case study based on actual radar measurements• Potential solution for data and Internet access in

– rural areas and– metropolitan cities



WDTN Mesh Networks Future Work

• Model additional topologies– hexagonal-packed cellular networks

• Model additional storm types– hurricanes, nor’easters with thundersnow, tropical cyclones– monsoon rains

• Model link alternatives– alternative lower-rate links– fiber bypass



WDTN Mesh Networks Future Work: Alternative Wireless Links






802.163–4G

CO/POP



WDTN Mesh Networks Future Work: Alternative Fiber Bypass






802.163–4G

CO/POP



Weather Disruption-Tolerant Nets Publications

• Abdul Jabbar, Justin P. Rohrer, Andrew Oberthaler, Egemen Çetinkaya, Victor S. Frost, and James P.G. Sterbenz,“Performance Comparison of Weather Disruption-Tolerant Cross-Layer Routing Algorithms”,Proceedings of 28th IEEE Conference on Computer Communications (INFOCOM’2009 ),Rio de Janeiro, April 2009[thanks to Merkouris Karaliopoulos for presenting]

• Abdul Jabbar, Bharatwajan Raman, Victor S. Frost, andJames P.G. Sterbenz,“Weather Disruption-Tolerant Self-OptimisingMillimeter Mesh Networks”,Third IFIP/IEEE Workshop on Self-Organizing Systems (IWSOS 2008 ),Vienna/Wein, December 2009, LNCS 5343, pp. 242–255



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