CEDAR a CoreExtraction Distributed Ad ho c Routing algorithm

Raghupathy Sivakumar Prasun Sinha Vaduvur Bharghavan

Co ordinated Science Lab oratory

University of Illinois at UrbanaChampaign

Emailfsiva prasun bharghavgtimelycrhcuiucedu

Abstract

In this pap er wepresent CEDAR a CoreExtraction DistributedAdhocRouting algorithm for

QoS routing in ad ho c network environments CEDAR has three key comp onents a the estab

lishment and maintenance of a selforganizing routing infrastructure called the core for p erforming

route computations b the propagation of the linkstate of highbandwidth and stable links in the

core through increasedecrease waves and c a QoS route computation algorithm that is executed

at the core no des using only lo cally available state

Our p erformance evaluations showthatCEDAR is a robust and adaptive QoS routing algorithm

that reacts quickly and eectively to the dynamics of the network while still approximating link

state p erformance for stable networks

Intro duction

An ad ho c network is a dynamic multihop wireless net work that is established by a group of mobile

hosts on a shared wireless channel by virtue of their proximity to each other Since wireless trans

missions are lo cally broadcast in the region of the transmitting host hosts that are in close proximity

can hear each other and are said to be neighbors The transitive closure of the neighborhood of all

the hosts in the set of mobile hosts under consideration forms an ad ho c network Thus eachhostis

potentially a router and it is p ossible to dynamically establish routes bychaining together a sequence

of neighboring hosts from a source to a destination in the ad ho c network Such networks nd ap

plicability in military environments wherein a plato on of soldiers or a eet of ships may establish an

ad ho c network in the region of their deployment Military network environments typically require

quality of service for their missioncritical applications Hence the fo cus of this pap er is to provide

quality of servicerouting in ad hoc networks

In particular we seek to compute unicast routes that satisfy a minimum bandwidth requirement

from the source to the destination Of course since the network is highly dynamic and transmissions

are susceptible to fades interference and collisions from hiddenexp osed stations we cannot provide

bandwidth guarantees for the computed routes Rather our goal is to provide routes that are highly

likely to satisfy the bandwidth requirement of a route

Given the nature of the network and the requirements of the applications the following are the

highly robust and should degrade key goals of our routing algorithm First the algorithm must be

gracefully up on sudden link or host failures or changes in the network top ology Second the routing

algorithm must generate an admissible route ie a route that satises the bandwidth requirement

with high probabilitysolongassuch a route is available Third only some of the hosts in the network

should be involved in route computation b ecause this involves monitoring and reacting to changes

in the network top ology However every host should have quick access to routes when it seeks to

establish connections with other hosts Fourth the routing algorithm at the selected subset of hosts

which p erform route computation must require only lo cal computation and involve mostly lo cal state

Finallyeachhostmust only maintain routes for those hosts that it cares ab out ie those hosts with

whom it communicates and must not have to up date state often even if the network top ology is highly

dynamic so long as the routes it cares ab out are stable

In order to achieve the ab ove goals we prop ose a CoreExtraction Distributed Ad hoc Routing

CEDAR algorithm for QoS routing in ad ho c networks CEDAR has three key comp onents a the

establishment and maintenance of the core of the network for p erforming the route computations b

propagation and use of bandwidth and stability information of links in the ad ho c network and c

the QoS route computation algorithm BrieyCEDAR dynamically establishes a core of the network

and then incrementally propagates link state of stable high bandwidth links to the no des of the core

Route computation is ondemand and is p erformed by core hosts using lo cal state only We prop ose

CEDAR as a QoS routing algorithm for small to medium size ad ho c networks consisting of tens to

hundreds of no des The following is a brief description of the three key comp onents of CEDAR

Core extraction We dynamically extract the core of the network by approximating a minimum

dominating set of the ad ho c network using only lo cal computation and lo cal state Each host

in the core then establishes a virtual link via a tunnel to nearby core hosts Each core host

maintains the lo cal top ology of the hosts in its domain and also p erforms route computation on

b ehalf of these hosts The core managemen t up on change in the network top ology is a purely

lo cal computation thus the core adapts eciently to the dynamics of the network

Link state propagation QoS routing in CEDAR is achieved by propagating the bandwidth

availability information of stable links in the core subgraph The basic idea is that the information

ab out stable links with large available bandwidths can b e made known to no des far away in the

network while information ab out dynamic links or low bandwidth links should remain lo cal

The propagation of linkstate is achieved through slowmoving increase waves which denote

increase of bandwidth and fastmoving decrease waves which denote decrease of bandwidth

The key questions to answer in link state propagation are when to initiate increasedecrease

waves how far can a wave propagate dep ending on the available bandwidth of the link and

how fast can a wave propagate dep ending on the nature of the wave and the stability of the

link

Route computation Route computation rst establishes a core path from the domain of the

source to the domain of the destination This initial phase involves probing like DSR though

it is only p erformed on the core graph The core path provides the directionality of the route

from the source to the destination Using this directional information CEDAR iteratively tries

to nd a partial route from the source to the domain of the furthest p ossible no de in the core

path which then b ecomes the source for the next iteration which can satisfy the requested

bandwidth using only lo cal information Eectively the computed route is a shortestwidest

furthest path algorithm using the core path as the guideline

Robustness rather than optimality is the primary concern of CEDAR Core host computations

are purely lo cal and the algorithm to reestablish the core up on change in the network top ology is

also lo cal Each core host knows only ab out nearby core hosts and has no idea ab out the entire core

subgraph Unlike the spine architecture there is no notion of a virtual backb one tree in the core

subgraph Core paths are dynamically established for connection requests and route computation is

only p erformed when a route is requested unlike distance vector or link state algorithms The only

information that is kept in a core host is the lo cal top ology nearby core hosts and information obtained

ab out remote links from increasedecrease waves The nature of propagation of the increasedecrease

waves ensures that unstable links and low bandwidth links do not cause top ology up dates further away

from their lo cations Thus CEDAR adapts very quickly to dynamics in the top ology At the same

time it computes go o d but not optimal routes and satises bandwidth requirements of connection

requests with high probability so long as such routes exist In fact weshow through simulations that

CEDAR has approximately the same probability of generating admissible routes as a global knowledge

shortestwidest path algorithm for ad ho c networks in the average case In other words using only lo cal

computations and mostly lo cal state CEDAR can provide go o d routes in dynamic ad ho c networks

Of course CEDAR do es not compute optimal routes because of the minimalist approach to state

management but we b elieve that our tradeo of robustness and adaptation for optimality is well

justied in ad ho c networks

The rest of this pap er is organized as follows Section describ es the network mo del terminology

and the goals of CEDAR Section describ es the computation and dynamic managementofthecore

of the network Section describ es the link state propagation through the core using increase and

decrease waves Section describ es the route computation algorithm of CEDAR and puts together

the algorithms describ ed in the previous sections Section analyzes the p erformance of CEDAR

through simulations Section compares CEDAR to related work and Section concludes the pap er

Network Mo del and Goals

In this section we rst describ e the network mo del then the terminology used in this pap er and

nally the goals of CEDAR

Network Mo del

We assume that all the hosts communicate on the same shared wireless channel For FHSS this implies

that all hosts have the same frequency hopping pattern while for DSSS this implies that all hosts have

the same pseudorandom sequence For an ad ho c network this is a p erfectly valid assumption We

assume that each transmitter has a xed transmission range and that neighb orho o d is a commutative

prop erty ie if A can hear B then B can hear A Because of the lo cal nature of transmissions

hidden and exp osed stations ab ound in an ad ho c network A hidden station is a host that is within

the range of the receiver but not the transmitter while an exp osed station is within the range of the

transmitter but not the receiver We assume the use of a CSMACA like algorithm suchasMACAW

or FAMA for reliable unicast communication and for solving the problem of hiddenexp osed

stations Essentiallydata transmission is preceded by a control packet hando and the sequence of

packets exchanged in a communication is the following RTS from sender to receiver CTS from

from receiver to sender The RTS and receiver to sender Data from sender to receiver ACK

CTS packets avoid collisions from the exp osed stations and the hidden stations resp ectively Lo cal

broadcasts are not guaranteed to b e reliable b ecause it is unreasonable to exp ect a CTS from every

receiver b efore commencing data transmission and are typically quite unreliable due to the presence

of hidden and exp osed stations

We assume small to medium networks ranging between tens to hunderds of hosts For larger

networks we prop ose a clustering algorithm in a related work and apply CEDAR hierarchically

within each cluster for a cluster of clusters etc We assume that mobility and extended fades are the

main causes of link failures and top ology changes We assume that the change in network top ology

is frequent but not frequent enough to render any sort of route computation useless Sp ecically

we assume that top ology changes are typically followed by at least a short period of stability Note

that we only care ab out the relative mobilityofthe hosts not the absolute mobility of the hosts In

particular even if a plato on of soldiers is moving the ad ho c network would b e considered to b e stable

so long as the neighb orho o d of each soldier do es not change

We assume that the MAClink layer can estimate the available link bandwidth Because all the

hosts in a region share the same channel each host must share the link bandwidth with the hosts in its

second neighborhood In related work on providing QoS in wireless channels wehave provided a

mechanism for each host to fairly access a shared channel and claim at least BN bandwidth where B

is the eectivechannel bandwidth and N is the numb er of hosts lo cally contending for the bandwidth

While details of bandwidth sharing and estimation are b eyond the scop e of this pap er we assume

that each host can estimate the available bandwidth using some linklevel mechanisms

We assume a close co ordination b etween the MAC layer and the routing layer In particular we

use the reception of RTS and CTS control messages at the MAClayer in order to improve the b ehavior

of the routing layer as explained in Section

Finally bandwidth is the QoS parameter of interest in this pap er When an application requests

a connection it sp ecies the required bandwidth for the connection The goal of CEDAR is then to

nd a short stable route that can satisfy the bandwidth requirement of the connection

Graph Terminology

W e represent the ad ho c network by means of an undirected graph G V E where V is the set of

no des in the graph hosts in the network and E is the set of edges in the graph links in the network

th i

The i open neighborhood N x of no de x is the set of no des whose distance from x is not greater

th i

than iexceptnodex itself The i closed neighborhood N xofnodex is N i fxg

A dominating set S V is a set such that every no de in V is either in S or is a neighbor of a

no de in S A dominating set with minimum cardinalityiscalleda minimum dominating set MDS

A virtual link u v between two no des in the dominating set S is a path in G from u to v

Given a MDS V of graph G we dene a core of the graph C V E where E fu v j

C C C C

u V v V u N v g Thus the core is a subgraph of G that consists of the MDS no des V

C C C

and a set of virtual links b etween every twonodesinV that are within a distance of eachotherin

C

G Two no des u and v whichhave a virtual link u v inthecore are said to b e nearby no des

For a connected graph G consider any dominating set S If the diameter of G is greater than

then for each no de v S there must b e at least one other no de of S in N v otherwise there is at

least one no de in G which is neither in S nor has a neighbor in S From the denition of the coreif

G is connected then a core C of G must also b e connected via virtual links

Goals of CEDAR

Ad ho c routing typically has the following goals a Route computation must b e distributed b ecause

centralized routing in a dynamic network is imp ossible even for fairly small networks b Route com

putation should not involve the maintenance of global state or even signicant amounts of volatile

nonlo cal state In particular link state routing is not feasible b ecause of the enormous state propa

gation overhead when the network top ology changes c As few no des as p ossible must b e involved in

state propagation and route computation since this involves monitoring and up dating at least some

state in the network On the other hand every host must have quick access to routes ondemand d

Eachnodemust only care ab out the routes corresp onding to its destination and must not b e involved

in frequent top ology up dates for parts of the network to which it has no trac e Stale routes must

b e either avoided or detected and eliminated quickly f Broadcasts must b e avoided as far as p ossible

b ecause broadcasts are highly unreliable in ad ho c networks g If the top ology stabilizes then routes

must con verge to the optimal routes and h It is desirable to haveabackup route when the primary

route has b ecome stale and is b eing recomputed

QoS routing for ad ho c networks is relatively unchartered territory We have the following goals

for QoS routing in ad ho c networks a Applications provide a minimum bandwidth requirementfor

a connection and the routing algorithm must eciently compute a route that can satisfy the band

width requirement with high probability b If there exists a route that can satisfy the bandwidth

requirement then the routing algorithm must compute an admissible route with high probability c

The amount of state propagation and top ology up date information must be kept to a minimum In

particular every change in available bandwidth should not result in up dated state propagation d

Dynamic links either unstable or low bandwidth links must not cause state propagation throughout

the network Only stable high bandwidth link information must be propagated throughout the net

work e As the network b ecomes stable the routing algorithm should start providing nearoptimal

routes and f The QoS route computation algorithm should b e simple and robust Robustness rather

than optimality is the key requirement

In summary our goal is to compute go o d routes quickly and react to the dynamics of the network

with only small amounts of state propagation As a result we sacrice optimality of routes However

weshow in Section that by virtue of our algorithm design CEDAR is able to approximate the state

intensive shortestwidest path algorithm in the a verage case though it still adapts very eciently to

the network dynamics

CEDAR Architecture and the Core

The QoS routing architecture in CEDAR has three key comp onents a the establishment of the

core in the ad ho c network to manage top ology information and p erform route computation b the

propagation of the linkstate of high bandwidth and stable links in the core subgraph through increase

and decrease waves and c the route computation algorithm at core no des using only lo cal state In

this section we rst describ e the overall CEDAR architecture and then fo cus on the establishment

and maintenance of the core

ximating the minimum dominating set Brieywe extract the core of the ad ho c network by appro

MDS of the ad ho c network The no des in the MDS comprise the core no des of the network Each

core no de establishes a unicast virtual link via a tunnel with other core no des a distance of or

less away from it in the ad ho c network The core no des then collect lo cal top ology information and

p erform routing for the no des in their domain or immediate neighb orho o d Each no de that is not in

the core cho oses a core neighbor as its dominator ie the no de which p erforms route computations

on its b ehalf The core is merely an infrastructure for facilitating route computation and is itself

indep endent of the routing algorithm In particular it is p ossible to use anyofthewell known ad ho c

routing algorithms such as DSR LMR TORA DSDV etc in the core subgraph

While it is p ossible to execute ad ho c routing algorithms using only lo cal top ology information

at the core no des CEDAR propagates the linkstate corresp onding to stable highbandwidth links

among the core no des The motivation for this approach is that the linkstate of dynamic and low

bandwidth links should not be made known to core no des far away since the linkstate is likely to

change frequently but the linkstate of stable highbandwidth links can be used by remote core

no des to compute b etter routes for connections passing through them As we describ e in Section

we propagate linkstate via the use of two waves a slow moving increase wave that denotes an

increase in the available bandwidth of a link and a fast moving decrease wave that denotes a decrease

in the available bandwidth of a link For unstable links whichcomeupandgodown frequently the

decrease wave up on link failure will quickly overtake and kill the increase wave that corresp onds to a

previous link establishment thereby ensuring that the link state for unstable links remains within the

lo cality of the link On the other hand the increase wave of a stable link slowly propagates throughout

the core subgraph thereby enabling remote core no des to take advantage of this link We constrain

the maximum distance to which the increase wave of a link can propagate dep ending on its available

bandwidth Thus only stable highbandwidth links are made known to core no des far away While

the details of the link state propagation algorithm are deferred to Section it is imp ortant to note

that the state available at a core no de comprises the uptodate lo cal top ology and p ossibly outdated

information ab out stable highbandwidth links far away This is the state that is used bya core no de

for route computation

The route computation in CEDAR follows two steps First we establish a core path from the

dominator of the source no de to the dominator of the destination no de The basic idea of establishing

a core path is to provide the directionalityfromthe source to the destination Once the core path is

established the dominator of the source no de tries to nd the shortest admissible path to the domain

of the furthest p ossible no de in the core path using its lo cal state The route computation is then

iteratively carried out at the furthest reachable no de in the core path Each core no de only uses lo cal

state and an admissible route is established in a single pass and carried in the route request packet

The linkstate information that is obtained ab out remote links using the increasedecrease w aves is

very useful in terms of both setting up shortest admissible paths as well as reducing the number

of connection rejections Note that for a stable ad ho c network CEDAR converges to an optimal

linkstate routing algorithm at each core no de While it is well known that linkstate algorithms do

not scale well for dynamic networks CEDAR provides an approximation of a linkstate algorithm for

stable networks and provides a robust lowoverhead algorithm to adapt to the dynamics of the network

while still computing good routes with low overhead for small to medium size networks F or large

networks our ongoing work prop oses a hierarchical clustering of the network and the application of

CEDAR at eachlevel of the hierarchy FinallyCEDAR also provides for eective route recomputation

mechanisms for route changes in ongoing connections as describ ed in Section

In the following subsections we rst describ e the motivation for cho osing a corebased routing

architecture then describ e a lowoverhead mechanism to generate and maintain the core of the network

and nally describ e an ecientmechanism to accomplish a core broadcast using unicast transmissions

The core broadcast is used b oth for propagation of increasedecrease waves and for the establishment

of the core path in the route computation phase

Rationale for a Corebased Architecture in CEDAR

Many contemp orary prop osals for ad ho c networking require every no de in the ad ho c network to

p erform route computations and top ology management However CEDAR uses a core

based infrastructure for QoS routing due to two comp elling reasons

QoS route computation involves maintaining lo cal and some nonlo cal linkstate and monitoring

and reacting to some top ology changes Clearly it is b enecial to have as few no des in the

network p erforming state management and route computation as p ossible

Lo cal broadcasts are highly unreliable in ad ho c networks due to the abundance of hidden

and exp osed stations Top ology information propagation and route prob es are

inevitable in order to establish routes and will of necessity need to b e broadcast if every no de

p erforms route computation While the adverse eects of unreliable broadcasts are typically not

considered in most of the related work on ad ho c routing we have observed that ooding in

ad ho c networks is highly lossy On the other hand if only a core subset of no des in the

ad ho c network p erform route computations it is p ossible to set up reliable unicast channels

between nearby core no des and accomplish both the top ology up dates and route prob es much

more eectively

The issues with having only a core subset of no des p erforming route computations are threefold First

no des in the ad ho c network that do not p erform route computation must have easy access to a nearby

core no de so that they can quickly request routes to b e setup Second the establishmentof the core

must be a purely lo cal computation In particular no core no de must need to know the top ology of

the entire core subgraph Third a change in the network top ology may cause a recomputation of the

core subgraph Recomputation of the core subgraph must only occur in the lo cality of the top ology

change and must not involve a global recomputation of the core subgraph On the other hand the

lo cally recomputed core subgraph must still only comprise of a small number of core no des otherwise

the b enet of restricting route computation to a small core subgraph is lost

We have previously develop ed the spine architecture for ad ho c routing in which we establish a

tree among the no des of an approximate minimum connected dominating set of the ad ho c network

While the spine architecture provides some of the b enets of the core architecture it do es not

handle the last two issues mentioned ab ove In particular the spine computation is a global algorithm

each spine no de is required to know the entire spine top ology and disconnection of the spine due to

top ology changes can result in a global recomputation in the worst case Unlike the spine architecture

the core architecture requires purely lo cal computations and lo cal state to generate and maintain the

coreeach core no de only has lo cal information ab out the core and there is no explicit tree structure

that is established in the core We b elievethatthe core provides the b enets of the spine architecture

without incurring the high maintenance overhead

Generation and Maintenance of the Core in CEDAR

Ideally the core comprises of the no des in a minimum dominating set V of the ad ho c network

C

G V E However nding the MDS is a NPhard problem that is also hard to approximate The

b est known distributed algorithm for MDS approximation is a greedy algorithm that requires O D

steps and has a comp etitive ratio of log j V j where D is the diameter of the network However this

algorithm requires global computation ie the result of step i at no de u can aect the computation of

step i at node v While we can use the greedy algorithm to generate the b est known approximation

for the MDS wehavechosen to use a robust and simple constant time algorithm which requires only

lo cal computations and generates go o d approximations for the MDS in the average case

Consider a no de u with rst op en neighborhood N u degree du j N u j dominator

domu and eective degree d u where d u is the number of its neighb ors who have chosen u as

their dominator The core computation algorithm work as follows at no de u

Perio dically u broadcasts a b eacon which contains the following information p ertaining to the

core computation ud ududomu

If u do es not have a dominator then it sets domu v wherev is the no de in N u with the

largest value for d v dv in lexicographic order Note that u may cho ose itself as the

dominator

u then sends v a unicast message including the following information ufw domw jw

N ug v then increments d v

If d u then u joins the core

Essentiallyeach no de that needs to nd a dominator selects the highest degree no de with the maxim um

eective degree in its rst closed neighborhood Ties are broken bynodeid

When a no de u joins the core it issues a piggybacked broadcast in N u A piggybacked broad

tr av er sed cast is accomplished as follows In its b eacon u transmits a message u DOM path

nul l When no de w hears a beacon that contains a message u D O M i path tr av er sed it

piggybacks the message u DOM i path tr av er sed w in its own b eacon if i Thus

the piggybacked broadcast of a core no de advertises its presence in its third neighborhood As shown

in Section this guarantees that each core no de identies its nearby core no des and can set up

tr av er sed eld in the broadcast messages The state that virtual links to these no des using the path

is contained in a core no de u is the following its nearby core no des ie the core no des in N u

N u the no des that it dominates for eachnodev N u w N v wdomw Thus

each core no de has enough lo cal top ology information to reach the domain of its nearby no des and

set up virtual links However no core no de has knowledge of the core subgraph In particular no

nonlo cal state needs to b e maintained by core no des for the construction or maintenance of the core

Maintaining the core in the presence of network dynamics is very simple Consider that due to

mobility a no de loses connectivity with its dominator After listening to b eacons from its neighb ors

the no de either nds a core neighbor whichitnow nominates as its dominator or nominates one of its

neighbors to join the core or itself joins the core If a no de loses connectivity with all its dominated

no des or discovers by monitoring the b eacons of its dominated no des that its eective degree has

b ecome it leaves the core by tearing down virtual links with its neighb ors and nds a dominator in

the core

Core Broadcast and its Application to CEDAR

As with most existing ad ho c networks CEDAR requires the broadcast of route prob es to discover

the lo cation of a destination no de and the broadcast of some top ology information in the form of

increasedecrease waves While most current algorithms assume that o o ding in ad ho c networks

works reasonably well our exp erience has shown otherwise In particular wehave observed that

o o ding prob es which causes rep eated lo cal broadcasts is highly unreliable b ecause of the abundance

of hidden and exp osed stations Thus we provide a unicast based mechanism for achieving a core

broadcast Note that it is reasonable to assume a unicast based mechanism to achieve broadcast in the

core b ecause each core no de is exp ected to have few nearby core no des Besides our core broadcast

mechanism ensures that each core no de do es not transmit a broadcast packet to every nearby core

no de CEDAR uses a close co ordination between the medium access layer and the routing layer in

order to achieve ecient core broadcast

Recall that a virtual link is a unicast path of length or Recall also that CSMACA proto cols

use a RTSCTSDataACK handshake sequence to achieve reliable unicast packet transmission Our

goal is to use the MAC state in order to achieve ecient core broadcast using O j V j messages where

j V j is the num b er of no des in the network

In order to achieve ecient core broadcast we assume that each no de temp orarily caches every

RTS and CTS packets that it hears on the channel for core broadcast packets only Each core broadcast

message M that is transmitted to a core no de i has the unique tag Mi This tag is put in the

RTS and CTS packets of the core broadcast packet and is cached for a short period of time by any

no de that receives or overhears these packets on the channel Consider that a core no de u has heard

a CTS M v on the channel Then it estimates that its nearby no de v has received M and

do es not forward M to no de v Now supp ose that u and v are a distance apart and the virtual

channel u v passes through a no de w Since w is a neighbor of v w hears CTSMv Thus

when u sends a RTS M v to w w sends back a NACK back to u If u and v are a distance

apart using the same argument we will have atmost one extra message Essentially the idea is to

prob e the RTS and CTS packets in the channel in order to discover when the intended receiver of a

core broadcast packet has already received the packet from another no de and suppress the duplicate

transmission of this packet

In the ad ho c network shown in Figure when no de is the source of the core broadcast

would not be sending a message to as it would have heard a CTS from when was receiving

Similarly would not b e sending on the tunnel to as would have heard the the message from

CTS from and hence would send a NACK when sends an RTS to Also on the tunnel from

to the message would b e senttobutwould not b e able to forward it any further b ecause of

having heard CTS from and hence receiving NACK from Thus the example illustrates that

a duplicate message can b e avoided on tunnels of length and but a duplicate message will travel 1 3 11 36

2 11 10 8 4 Core broadcast with node 10 1 as source 1 5 9 3

111610

6 8 78 Core broadcast with node 3 as source

Ad hoc Network Topology

Figure Example of a core broadcast No des in blackare core no des Solid lines denote links in the

ad ho c network Dotted pip es denote virtual links in the core subgraph

one extra hop for tunnels of length

Note that our core broadcast has the following features

The core no des do not explicitly maintain a sourcebased tree However the core broadcast

dynamically and implicitly establishes a sourcebased tree which is typically a breadthrst

search tree for the source of the core broadcast

The numb er of messages is O V intheworst case and O V intheaverage case In particular

C

the only case we transmit extra messages is when twonearby no des are a distance apart

Since the trees are not explicitly maintained dierent messages may establish dierent trees

Likewise changes in the network top ology do not require any recomputation However the

co ordination of the MAClayer and the routing layer ensures that the core broadcast establishes

a tree and that a core no de typically do es not receive duplicates for a core broadcast

While our approach for the core broadcast is very lowoverhead and adapts easily to top ology changes

the RTS and CTS packets corresp onding to a core broadcast need to be cached for some time after

their reception

Core broadcast nds applicabilityintwo key asp ects of CEDAR discovery of the core path and

propagation of increasedecrease waves The discovery of the core path is broadcast b ecause the sender

may not know the lo cation of the receiver It initiates a core broadcast to nd the lo cation of the

receiver and simultaneously discover the core path

QoS State Propagation in CEDAR

Section describ ed the core routing infrastructure of CEDAR Since each core no de uses only the

lo cally cached state to compute the shortestwidest furthest path along the core path in the route

computation phase wenow turn our attention to the nature of state that is stored in each core no de

At one extreme is the minimalist approach of only storing lo cal top ology information at each core no de

This approach results in a poor routing algorithm ie the routing algorithm may fail to compute

an admissible route even if such routes exist in the ad ho c network but has a very lowoverhead for

dynamic networks At the other extreme is the maximalist approach of storing the entire link state

of the ad ho c network at each core no de This approach computes optimal routes but incurs a high

networks and potentially computes stale routes based on state management overhead for dynamic

outofdate cached state when the network dynamics is high

The problem with having only lo cal state is that core no des are unable to compute go o d routes in

the absence of linkstate information ab out highbandwidth and stable remote links while the problem

of having global state is that it is useless to maintain the link state corresp onding to lowbandwidth

and highly dynamic links that are far away b ecause the cached state is likely to be stale anyway

Fundamentally each core no de needs to have the uptodate state ab out its lo cal top ology and also

the linkstate corresp onding to relatively stable highbandwidth links further away Providing for

such a linkstate propagation mechanism ensures that CEDAR approaches the minimalist lo cal state

algorithm in highly dynamic networks and approaches the maximalist linkstate algorithm in highly

stable networks Weachieve the goal of having stabilit y and bandwidth based linkstate propagation

using increase and decrease waves as describ ed in this section

The basic idea of having our increasedecrease wave approach for up dating linkstate is the follow

ing There are twotyp es of waves aslowmoving increase wave that denotes an increase of bandwidth

on a link and a fastmoving decrease wave that denotes a decrease of bandwidth on a link For unsta

ble links that come up and go down frequentlythe fast moving decrease wave quickly overtakes and

kills the slower moving increase wave thus ensuring that the linkstate corresp onding to dynamic links

is lo cal For stable links the increase wave gradually propagates through the core Each increase wave

also has a maximum distance it is allowed to propagate Low bandwidth increase wav es are allowed

only to travel a short distance while high bandwidth increase waves are allowed to travel far into the

network Essentially the goal is to propagate only stable highbandwidth linkstate throughout the

coreandkeep the lowbandwidth and unstable linkstate lo cal

We rst describ e the mechanics of the increase and decrease waves and then answer the three

key questions p ertaining to these waves when should a wave be generated how fast should a wave

propagate and how far should a wave propagate

Increase and Decrease Waves

For every link l a b the no des a and b are resp onsible for monitoring the available bandwidth

on l and for notifying the resp ective dominators for initiating the increase and decrease waves when

the bandwidth changes by some threshold value These waves are then propagated by the dominators

core no des to all other core no des via a core broadcasts Each core no de has two queues the ito

queue that contains the p ending core broadcast messages for increase waves and the dtoqueue that

contains the p ending core broadcast messages for decrease waves For each link l ab out whicha core

no de caches linkstate the core no de contains the cached available bandwidth b l

av

The following is the sequence of actions for an increase wave

When a new link l a b comes up or when the available bandwidth ba b increases b eyond

a threshold value then the two endp oints of l inform their dominators for initiating a core

broadcast for an increase wave itoabdomadombba bttlb

where ito increase to denotes the typ e of the wave a b identies the link doma denotes the

dominator of a domb denotes the dominator of b ba b denotes the available bandwidth on

the link and ttl b is a timetolive eld that denotes the maximum distance to which this wave

can be propagated as an increase wave The ids of the dominators of the link endp oints are

required by the routing algorithm ttl b is an increasing function of the available bandwidth as

describ ed in Section

When a core no de u receives an ito wave itoabdomadombba bttl

a if u has no state cached for a b

b a b ba b

av

if ttl then add itoabdomadombba bttl to the itoqueue

b else if u has cached state for a b and ttl

i if b a b ba b

av

b a b ba b

av

delete any p ending itodto message for a b from the itoqueue and dtoqueue

add itoabdomadombba bttl to the itoqueue

ii else if b a b ba b

av

b a b ba b

av

delete any p ending itodto message for a b from the itoqueue and dtoqueue

add dtoabdomadombba bttl to the dtoqueue

c else if u has cached state for a b and ttl

b a b ba b

av

delete any p ending itodto message for a b from the itoqueue and dtoqueue

add dto abdoma domb to the dtoqueue

The itoqueue is ushed p erio dically dep ending on the sp eed of propagation of the increase wave

The following is the sequence of actions for a decrease wave

When a link l a b go es down or when the available bandwidth ba b decreases beyond

a threshold value then the two endp oints of l inform their dominators for initiating a core

broadcast for a decrease wave dto a b domadombba bttlb where dto decrease

to denotes the typ e of the wave and the other parameters are as dened b efore

When a core no de u receives a dto wave dtoabdomadombba b ttl

a if u has no state cached for a b and ba b

the wave is killed

b else if u has no state cached for a b and ba b

b a b ba b

av

if ttl then add itoabdomadombba bttl to the itoqueue

c else if u has cached state for a b and ttl

i if b a b ba b

av

b a b ba b

av

delete any p ending itodto message for a b from the itoqueue and dtoqueue

add itoabdomadombba bttl to the itoqueue

ii else if b a b ba b

av

b a b ba b

av

delete any p ending itodto message for a b from the itoqueue and dtoqueue

add dtoabdomadombba bttl to the dtoqueue

d else if u has cached state for a b and ttl

b a b ba b

av

delete any p ending itodto message for a b from the itoqueue and dtoqueue

add dto abdoma domb to the dtoqueue

The dtoqueue is ushed whenever there are packets in the queue

There are several interesting p oints in the ab ove algorithm First the way that the itoqueue and

the dtoqueue are ushed ensures that the decrease waves propagate much faster than the increase

waves and suppress state propagation for unstable links Second wa ves are converted b etween ito and

dto onthey dep ending on whether the cached value for the available bandwidth is lesser than the

new up date ito wave generated or not dto wave generated Third after a distance of ttl which

dep ends on the current available bandwidth of the link the dto a b domadomb

message ensures that all other core no des which had state cached for this link now destroy that state

However the dtoabdoma domb wave do es not propagate throughout the network

it is suppressed as so on as it hits the core no des whichdonothave link state for a b cached p oint

a As wehave noted b efore the increasedecrease waves use the ecient core broadcast mechanism

for propagation

Essentially the ab ove algorithm ensures that the linkstate information for stable highbandwidth

links gets propagated throughout the core while the linkstate information for unstable and low

bandwidth links remains lo cal which is the goal of the CEDAR state propagation algorithm

Wenowanswer the three key questions p ertaining to the propagation of increasedecrease waves

when should a wave be generated how fast should a wave propagate and how far should a wave

propagate

When should a IncreaseDecrease Wave be Generated

Clearly a wave should not be generated for every incremental change in the available bandwidth of

the link In CEDAR we only generate a wave when the bandwidth has changed by a threshold value

since the last wavewas generated The setting of the threshold parameter is the fo cus of the discussion

in this section

A simple approach would be make the threshold a constant system parameter In other words

a wave is generated when the bandwidth change since the last generation of the wave has exceeded

a xed value say of the raw channel bandwidth While this approach works well when the

available bandwidth has the same order of magnitude as a typical connection request eg achannel

capacity of Mbps and connection requests in the range of Mbps it is very wasteful in terms

of the number of waves generated if the channel capacity is orders of magnitude higher than the

typical connection request eg achannel capacity of Mbps and connection requests in the range of

Kbps In the latter case we prop ose a logarithmic scale for the threshold similar to the algorithm

by This approachismotivated by the following argument if the available bandwidth is Mbps

and the typical request is Kbps then an increase of the channel bandwidth to Mbps or a

decrease of the channel bandwidth to Mbps is not signicant However if the available channel

bandwidth is Kbps and the typical request is Kbps then an increase to Kbps or a decrease to

Kbps is a signicantchange The advantage of the logarithmic up date is that it do es not wastefully

generate increasedecrease waves when the change in link capacity is unlikely to alter the probability

of computing admissible routes

In the CEDAR simulations we currently use the constant threshold approach for simplicity Future

work will migrate to the logarithmic scale for the threshold

How Far do es a IncreaseDecrease Wave Propagate

Our goal is to propagate information ab out stable highbandwidth links throughout the network and

lo calize the state of the lowbandwidth links This is b ecause every core no de that caches information

corresp onding to a link can potentially use the bandwidth of the link and the contention for a link

is dep endenton the number of core no des caching the state of the link For lowbandwidth links it

makes sense to have as few no des as p ossible contending for the link while for stable highbandwidth

e as many core no des as p ossible know ab out the link in order to compute links it makes sense to hav

go o d routes In order words the maximum distance that the link state can travel ie the timetolive

eld is an increasing function of the available bandwidth of the link

Our current CEDAR simulation uses a linear function for computing the ttl However we have

used a uid mo del analysis of an ad ho c network and determined that in general the ttl should b e

k

a function of b where k is a small number ranging from to dep ending on the density of the

uid generated from a source to its neighb ors The details of this analysis are ongoing work and not

presented for lack of space However the summary of our results indicates that the selection of k

ie the constant function works well for the environments under consideration

How Fast do es a IncreaseDecrease Wave Propagate

An increase wave waits for a xed timeout p erio d which is a system parameter that should be

approximately twice the exp ected interarrival time between the generation of two successive waves

for any link in the network at each no de b efore b eing forwarded to its neighbors using the core

broadcast Thus increase waves propagate slowly A decrease wave is immediately forwarded to its

neighbors using the core broadcast Thus decrease waves move much faster and can kill increase

waves for unstable links

An added b enet of waiting at each no de for a timeout p erio d b efore forwarding the wave is that

it naturally leads to the implicit establishment of a sourcebased breadthrstsearch tree for the core

broadcast as describ ed in Section

QoS Routing in CEDAR

In the previous two Sections we have describ ed the core infrastructure ie which no des in the ad

ho c network p erform route computation and how they communicate among themselves and the state

propagation algorithm ie what state do es each core no de contain In this section we complete the

description of CEDAR by sp ecifying how the core no des use the state information to compute QoS

routes

The QoS route computation in CEDAR consists of three key comp onents a discovery of the

lo cation of the destination and establishment of the core path to the destination b establishment

of a short stable admissible QoS route from the source to the destination using the core path as a

directional guideline and c dynamic reestablishment of routes for ongoing connections up on link

failures and top ology changes in the ad ho c network

Briey QoS route computation in CEDAR is an ondemand routing algorithm which pro ceeds

as follows when a source no de s seeks to establish a connection to a destination no de d s provides

for the its dominator no de doms with a s d b triple where b is the required bandwidth

connection If doms can compute an admissible available route to d using its lo cal state it resp onds

to s immediately Otherwise if doms already has the dominator of d cached and has a core path

established to domd it pro ceeds with the QoS route establishmentphase If doms do es not know

the lo cation of d if rst discovers domd simultaneously establishes a core path to d and then

initiates the route computation phase A core path from s to d results in a path in the core subgraph

from doms to domd dom s then tries to nd the shortest widest furthest admissible path along

the core path ie doms uses its lo cal state to nd the shortest widest admissible path to a no de t

in the domain of the furthest p ossible core no de domt in the core path Once the path from s to

t is established domt then uses its lo cal state to nd the shortest widest furthest admissible path

to d along the core path and so on Eventually either an admissible route to d is established or

the algorithm rep orts a failure to nd an admissible path As we have already discussed in previous

sections the knowledge of remote stable highbandwidth links at each core no de signicantly improves

the probability of nding an admissible path so long as such a path exists in the network

In the following subsections we describ e the three key comp onents of QoS routing in CEDAR

Establishment of the Core Path

The establishment of a core path takes place when s requests doms to set up a route to d and

doms do es not know the identityofdomd Establishmentofa core path consists of the following

steps

doms initiates a core broadcast to set up a core path with the following message

cor e path req domsdP nul l

When a core no de u receives the core path request message core path req domsdP it

sets P P fug and forwards the message on its outgoing virtual links according to the core

broadcast algorithm

When domt receives the core path request message cor e path req domsdP it sends

back a source ro oted unicast cor e path ack message to doms along the inverse path recorded

in P The resp onse message also contains P the core path from domstodomd

Up on reception of the cor e path ack message from domd doms completes the core path establish

ment phase and enters the QoS route computation phase

Note that byvirtueofthecore broadcast algorithm the core path request traverses an implicitly

and dynamically established source routed tree from doms whichistypically a breadthrst search

tree Thus the core path is approximately the shortest path in the core subgraph from doms to

domd and hence provides a go o d directional guideline for the QoS route computation phase

QoS Route Computation

After the core path establishment domsknows domd and the core path from doms to domd

Recall from Section that doms has the lo cal top ology which includes all the no des in its domain

and for each dominated no de u the bandwidth of each link incident on u the adjacency list of

u and the dominator of each of the neighbors of u Recall from Section that doms has the

information gathered ab out remote links through increasedecrease waves and for each such link

u v the bandwidth of u v domu and domv doms thus has a partial knowledge of the ad

ho c network top ology which consists of the uptodate lo cal top ology and some p ossibly outofdate

information ab out remote stable highbandwidth links in the network The following is the sequence

of events in QoS route computation

Using the lo cal top ology doms tries to nd a path from s to the domain of the furthest p ossible

core no de in the core path say domt that can provide at least a bandwidth of b bandwidth

of the connection request The bandwidth that can b e provided on a path is the minimum of

the individual available link bandwidths that comprise the path

Among all the admissible paths to the domain of the furthest p ossible core no de in the core

shortest widest path Since doms uses only its lo cal state to p erform path doms picks the

this computation it can use a variant of the Dijkstras single source shortest path algorithm to

compute this path

Let t b e the end p ointofthechosen path and ps t denote the path doms sends domt the

following message sdbPps tdomst where s dandt are the source destination

and intermediate no de in the partially computed path b is the required bandwidth P is the

core path and ps t is the partial path

domt then p erforms the QoS route computation using its lo cal state identical to the computa

tion describ ed ab ove

Eventually either there is an admissible path to d or the lo cal route computation will fail to

pro duce a path at some core no de The concetenation of the partial paths computed by the core

no des provides an endtoend path that can satisfy the bandwidth requirement of the connection

with high probability

The core path is computed in one round trip and the QoS route computation algorithm is computed

in one pass Thus the route discovery and computation algorithm together take passes

Note that while the QoS route is b eing computed packets maybesent from s to d using the core

path The core path thus provides a simple backup route while the primary route is b eing computed

Dynamic QoS Route Recomputation for Ongoing Connections

Route recomputations may b e required for ongoing connections under two circumstances the end host

moves and there is some intermediate link failure p ossibly caused by the mobilityofanintermediate

router End host mobility can be thought of as a sp ecial case of link failure wherein the last link

fails

CEDAR has twomechanisms to deal with link failures and reduce the impact of failures on ongoing

ows dynamic recomputation of an admissible route from the p ointof failure and notication back

to the source for sourceinitiated route recomputation These two mechanisms work in concert and

enable us to provide seamless mobility

QoS Route Recomputation at the Failure Point Consider that alinku v fails on the path of

an ongoing connection from s to t The no de nearest to the sender u then initiates a lo cal

route recomputation similar to the algorithm in Section Once the route is recomputed u

up dates the source route in all packets from s to t accordingly If the link failure happ ens near

the destination then dynamic route recomputation at the intermediate no de works very well

b ecause the route recomputation time to the destination is exp ected to be small and packets

inight are rerouted seamlessly

QoS Route Recomputation at the Source Consider that a link u v fails on the path of an ongo

ing connection from s to t The no de nearest to the sender u then noties s that the link u v

has failed Up on receiving the notication u stops its packet transmission initiates a QoS route

computation as in Section and resumes transmission up on the successful reestablishment

of an admissible route If the link failure happ ens near the source then sourceinitiated re

computation is eective b ecause the source can quickly receive the linkfailure notication and

temp orarily stop transmission

The combination of these two mechanisms is eective in supp orting seamless communication inspite

of mobility and dynamic top ology changes Basically we use sourceinitiated recomputation as the

longterm solution to handling link failure while the shortterm solution to handle packets inight

is through the dynamic recomputation of routes from the intermediate no des Recomputation at the

failure p oint is not really eective if the failure happ ens close to the source but in this case the

number of packets in ight from s to u is small

Performance Evaluation

We have evaluated the p erformance of CEDAR via both implementation and simulation Our im

plementation consists of a small ad ho c network consisting of six mobile no des that use a Photonics

Data Technology Mbps Infrared network We have customized the Linux kernel to build

our ad ho c network environment written partly in user mo de and partly in kernel mo de While the

testb ed shows a pro of of concept and has exp osed some of the practical diculties in implementing

CEDAR our detailed p erformance evaluation has been using a simulator that faithfully implements

the CEDAR algorithms

For our simulations wemake the following assumptions ab out the network environment a The

channel capacity is Mbps b It takes time for a no de to successfully transmit a message over a

single link where is the degree of the no de c The dynamics of the top ology are induced either

by link failure or mobility d Packets are source routed e The transmission range for each no de

is a by unit square region with the no de at the center of this region we generate our test

graphs by randomly placing hosts in a by square region and vi eachCEDAR control packet

transmission slot has a p erio d of ms

We present three sets of results from our simulations The rst set of results characterizes the

p erformance of CEDAR in a b esteort service environment The goal is to isolate the characterization

of the basic routing algorithm from the eects of QoS routing for this set of results The second set

of results evaluates the p erformance of QoS routing in CEDAR The third set of results evaluates the

p erformance of CEDAR for ongoing connections in the presence of mobility Essentially the rst two

sets of results evaluate the p erformance of CEDAR in coming up with new routes in an ad ho c network

while the third set of results evaluates ho wCEDAR cop es with link failures for ongoing connections

In the rst set of results presented in Tables we compare CEDAR to an optimal shortest path

routing algorithm in a b esteort service environment Our p erformance measures are the following

i average path length APL ii message complexity for route computation MC and iii time

complexity for route computation TC In addition we present the core usage CU which is the

average numb er of virtual links used in a route Note that for the b est eort environment wedonot

have a concept of QoS for connections and the increasedecrease waves essentially carry only link

updown information

In the second set of results presented in Tables we evaluate the QoS routing algorithm

of CEDAR We use bandwidth as the quality of service parameter Tables and compare the

p erformance of CEDAR against the p erformance of an optimal shortestwidest path algorithm in terms

of the the path length hops and the maximum available bandwidth bw for computed routes Tables

and compare the acceptreject ratio for CEDAR with and without increasedecrease waves and

an optimal shortestwidest path algorithm

In the third set of results presented in Tables and weevaluate the p erformance of CEDAR for

ongoing connections up on top ology change induced by link failures and host mobility We consider

the following parameters i lo cation of the link failure relative to the source Relative Link Position

RLP ii number of packets sent iii number of packets received iv number of packets lost v

number of packets rerouted and vi minimum delay exp erienced by packets in the ow once the

source receives notication ab out the link failure

In all our simulations the notation CEDAR stands for a simulation run of CEDAR at time t

t

increasedecrease waves would havethus b een propagated up to time t

Performance of CEDAR without QoS Routing

We use randomly generated graphs for the results in this section The graphs are of sizes

and resp ectively The signican t parameters for the graph number of nodesn number of

edgesm number of core no desC diameter of the corediam average degree are shown in

C

the caption of the table containing the results for that particular graph For each graph we measure

as mentioned ab ove the average path length APLinnumb er of hops message complexity for route

computation MC route computation time TC in seconds and the core usage ratio CU also in

number of hops These measurements are taken for b oth optimal shortest path routing and CEDAR

For CEDAR we measure these parameters at dierent p oints of time to study the impact of the

propagation of ito waves The time d used in the tables is the constant time for which ito waves are

delayed at each hop

As can be seen from the results CEDAR p erforms reasonably well b efore the intro duction of

itodto waves but converges very fast to a near optimal p erformance once these waves are intro duced

The ideal value for the CU should b e zero as weseektoavoid using the virtual tunnels for data owin

ordertoprevent it from b ecoming a b ottleneck CEDAR exhibits a low CU b ecause we preferentially

avoid using the virtual tunnels a virtual tunnel edge is chosen only if the lo cal state at the core no de

p erforming the route computation is inadequate to forward the prob e into a farther domain towards

the destination

The counterintuitive increase in APL MC and TC with increase in time in these simulations are

due to the fact that we are able to preferentially bypass the core as indicated by the decrease in CU

as more top ology information b ecomes available Thus the results shown in Tables and indicate

the near optimal nature of CEDAR with increase in network stability

50 "config.gnu" 27 45 12

40

35 16 20 19 5 22 30 9 7 2 18 25 0 6 10 28 1 17 20 14

23 13 15 24 21 11 3 10 8 4 15 26 5 25 29

0

0 5 10 15 20 25 30 35 40 45 50

Figure Graph used for Performance Evaluation Simulations

APL MC TC CU

optimal NA

CEDAR

t

CEDAR

t d

CEDAR

t d

CEDAR

t d

Table Performance of CEDAR compared to an optimal approach nmC diam Av g deg

C

Performance of QoS Routing in CEDAR

Bandwidth is the QoS parameter of interest in CEDAR We rst compare QoS routing in CEDAR with

an optimal shortest widest path algorithm with resp ect to two parameters the available bandwidth

bw along the computed path and the path length in hops The time eld in Tables and Failure point(u) Source node (s)

start of transmission first packet routed through u

link failure

packets dropped source new route notification recomputed packets rerouted source notification received. (stop transmission) last packet time routed though u packets experiencing delay due to link failure new route recomputed

at source

Figure Eect of a link failure on an ongoing ow

APL MC TC CU APL MC TC CU

optimal NA optimal NA

CEDAR CEDAR

t t

CEDAR CEDAR

t d t d

CEDAR CEDAR

t d t d

CEDAR CEDAR

t d t d

CEDAR CEDAR

t d t d

CEDAR CEDAR

t d t d

a b

Table Performance of CEDAR compared to an optimal approach a nmC diam Av g deg

C

b nmC diam Av g deg

C

represents the time at which the QoS route request was issued Once the route is computed each link

lo cks the sp ecied amount of resources along that route b efore pro cessing the next connection request

Next we present the improvementinthe p erformance of CEDAR with the advent of the ito and

dto waves We use the constant threshold approach to decide when to generate a wave The ttl eld in

awave is set using a linear function of the advertised bandwidth and while ito waves travel from one

hop to another with a constant delay dto waves travel are propagated from one hop to another with

no delay The parameter weusetoevaluate the p erformance is the acceptreject ratio for connection

requests As can b e seen once the itodto waves are intro duced the p erformance of CEDAR is close

to that of an optimal algorithm

For the results in this section we again use the no de graph in Figure with link bandwidths

randomly set to either units or units Note from Tables and that CEDAR approximates the

optimal algorithm for the scenarios simulated Further from Tables and we can see the utilityof

the ito and dto waves to CEDAR In these tables the column headers accept accept

OP T CEDARwaves

and accept represent whether the connection request was accepted in the optimal algo

CEDAR
pl ain

rithm CEDAR with waves and CEDAR without waves resp ectiv ely

APL MC TC CU APL MC TC CU

optimal NA optimal NA

CEDAR CEDAR

t t

CEDAR CEDAR

t d t d

CEDAR CEDAR

t d t d

CEDAR CEDAR

t d t d

CEDAR CEDAR

t d t d

CEDAR CEDAR

t d t d

CEDAR CEDAR

t d t d

CEDAR CEDAR

t d t d

a b

Table Performance of CEDAR compared to an optimal approach a nmC diam Av g deg

C

b nmC diam Av g deg

C

time sour ce destn bw hops bw hops bw

req CEDAR CEDAR OP T OP T

t



t



t



t



t



t



t



t



t



t



Table Performance of CEDAR compared to an optimal approach nmC diam Av g deg

C

with connection results issued as shown

Eect of Link Failures on Ongoing Flows in CEDAR

While the previous sets of results evaluated the p erformance of CEDAR in terms of generating initial

routes wenow turn our attention to the abilityofCEDAR to provide seamless connectivityinadhoc

networks inspite of the dynamics of the network top ology

The following is the sequence of events that o ccurs on a link failure

Link u v fails on path from s to d

u sends back notication to source and starts recomputation of route from u to d

For each subsequent packet that u receives it drops the packet if the recomputation of the

previous step is not yet completed Otherwise u forwards the packet along the new route with

the mo died source route

Up on receiving a link failure notication s stops sending packets for that ow immediately and

starts recomputation of the route from s to d

Once the recomputation of the previous step is complete the source once again starts sending

packets for that ow along the new route

This sequence of events is also illustrated in Figure

time sour ce destn bw hops bw hops bw

req CEDAR CEDAR OP T OP T

t



t



t



t



t



t



t



t



t



t



Table Performance of CEDAR compared to an optimal approach nmC diam Av g deg

C

with connection requests issued at times shown

star ttime endtime sour ce destn bw accept accept accept

req OP T waves

pl ain

yes yes yes

yes yes no

yes yes yes

yes yes yes

yes yes no

yes yes yes

yes yes no

yes yes yes

no no no

yes yes yes

Table Performance improvement of CEDAR with the adventofito and dto waves The acceptreject

ratio for optimal CEDAR with waves and CEDAR without waves are and resp ectively

nmC diam Avgdeg

C

Figure shows a no de graph that is used for evaluating the p erformance of CEDAR in the

presence of link failures For an arbitrary ow we bring down links that are progressively farther

aw ay from the source and weshow the impact of that link failure in terms of numb er of packets lost

number of packets rerouted and delay for subsequentpackets

As can be observed from Tables and the relative lo cation of the link failure with resp ect to

the source has a signicant impact on the ab ovementioned parameters In particular

If the link failure is very close to the source the recomputation time at the no de b efore the

failure is large and hence a considerable number of packets can potentially be lost But the

source notication message describ ed earlier in Section reaches the source almost immediately

and hence prevents a large number of packets from getting dropp ed

is very close to the destination the recomputation time at the no de b efore If the link failure

the failure is very small and hence very few packets get dropp ed But the source notication

message reaches the source with some delay and hence the numb er of packets that get rerouted

is large

If the link failure is somewhere midway between the source and destination both mechanisms

route recomputation and source notication fail to react fast enough to prevent lossofpackets

star ttime endtime sour ce destn bw accept accept accept

req OP T waves

pl ain

yes yes yes

yes yes yes

yes yes no

yes yes yes

yes yes yes

yes yes yes

yes no no

yes yes no

yes yes yes

yes yes yes

Table Performance improvement of CEDAR with the adventofito and dto waves The acceptreject

ratio for optimal CEDAR with waves and CEDAR without waves are and resp ectively

nmC diam Avgdeg

C

RLP sent recvd dr opped rerouted del ay RLP sent recvd dr opped r er outed del ay

a b

Table Performance of CEDARs recovery mechanism on a link failure nmC diam Av g deg

C

with link failure on path for ow from no de to a input trac generated using

Poisson distribution b input trac generated using MMPP distribution

and hence the number of packets lost and rerouted is relatively large

For the generation of packets in a ow we use both Poisson and MMPP markov mo dulated

p oisson pro cess distributions in our simulations

Related Work

We present a brief survey of related work in twoareas routing in ad ho c networks and QoS routing in wireline

networks QoS routing in ad ho c netw orks is still relatively unchartered territory

Routing in ad ho c networks

Most ad ho c routing algorithms that we are aware of generously use o o ding or broadcasts for route computation

As wehavementioned b efore our exp erience has b een that o o ding in ad ho c networks do es not work well due

to the abundance of hidden and exp osed stations

The ad ho c routing algorithms in provide a single route in resp onse to a route query from a

source these algorithms havelowoverhead but sometimes use suboptimal and stale routes uses o o ding

in the worst case for nding routes considers signal strength as a metric for routing uses additional

criteria to judge routes the relaying load or numb er of existing connections passing through an intermediate

no de and lo cation stability as measured in asso ciativityticks uses a spine structure for route computation

and maintenance It provides optimal or near optimal routes dep ending up on the nature of information stored

in the spine no des but incurs a large overhead for state and spine management

RLP sent recvd dr opped rerouted del ay RLP sent recvd dr opped r er outed del ay

a b

Table Performance of CEDARs recovery mechanism on a link failure nmC diam Av g deg

C

with link failure on path for ow from no de to a input trac generated using

Poisson distribution b input trac generated using MMPP distribution

Previous work on tactical packet radio networks had led to many of the fundamental results in ad ho c

networks uses minimumhop distancevector routing To extend routing to larger networks hierarchical

routing has b een prop osed with either distancevector routing or linkstate routing used within

each cluster

Other shortestpaths routing algorithms incorp orate measures of delay or congestion into the path weights

but these algorithms usually have some centralized computation of the delay and congestion metrics In

a dierenttack neuralnetworkbased computation is used to select routes other than via minimum hop count

with some measure of success in reducing congestion

The multipath routing algorithms are more robust than the single route on demand algorithms at a cost of

higher memory and message requirements In a source may learn of more than one route to a destination

hence the routing decision is exible and fault tolerant The hybrid routing algorithm in combines the

robustness of multipath routing with the lowoverhead of single route on demand when no de mobility is high

is used when no de mobility is low is used Finally the temp orallyordered routing algorithm in

improves on by using time tags to lo calize top ology changes

As is apparent from our work wehaveusedmany of the results from contemp orary literature The notion

of ondemand routing use of stability as a metric to propagate linkstate information clustering and the use

of clusterheads for lo cal state aggregation hav e all b een prop osed in previous work in one form or the other

We b elieve that our contribution in this pap er is to prop ose a unique combination of several of these ideas

in conjunction with the novel use of the core increasedecrease waves core broadcast and lo cal statebased

routing in the domain of QoS routing Consequentlywe are able to compute go o d admissible routes with high

probability and still adapt eectively with lowoverhead to the dynamics of the network top ology

QoS Routing

QoS routing algorithms can be mainly classied into two categories distributed and

centralized

croft show that if the total number of indep endent additive and multiplicative QoS Wang and Crow

constraints is more than one then the QoS routing problem is NP complete Assuming that all routers are

using Weighted Fair Queuing scheduling Ma and Steenkiste and Pornavalai et al show that the

relationships b etween various QoS parameters bandwidth delaydelayjitter and buer space can b e utilized

to nd QoS routes in p olynomial time Wang and Crowcroft and Guerin et al prop ose shortestwidest

path A comparison of shortestwidest widestshortest dynamic alternative and the shortest distance path is

presented in Salama et al prop ose a distributed algorithm for nding delay constrained unicast

routes DCUR The algorithm requires every no de to keep a least cost next hop and a least delaynexthop

corresp onding to every destination The worst case time complexityofO V has b een improved to O V by

Sun and Langendo erfer Wehave used the optimal global knowledgebased algorithm for comparison with

our CEDAR algorithm

Ma and Steenkiste prop ose a centralized algorithm for nding the fair share of a b est eort ow The

fair share of a bandwidth can b e used for shortestwidest widestshortest or any other algorithm for routing

the b est eort trac Widyono prop oses a centralized Constrained Bellman Ford CBF algorithm and a

path merging algorithm to nd a minimum cost source ro oted tree to a set of destinations with delay b ounds

Eects of uncertain parameters on QoS routing with endtoend delay requirements is discussed in For a

wide class of probability distributions Lorenz and Orda and Guerin and Orda prop ose ecientexact

solutions to optimal delay partition problem OP and a pseudo p olynomial solution to optimally partitioned

most probable path OPMP This work is currently b eing applied in order to extend the CEDAR approachto

supp ort delay as a QoS parameter in ad ho c network environments

Asimulation based study of the relationship b etween routing p erformance and the amount of up date trac

is rep orted by Ap ostolop oulos et al

Conclusion

In this pap er we havepresented CEDAR a CoreExtraction Distributed Ad ho c Routing algorithm for pro

viding QoS in ad ho c network environments CEDAR has three key comp onents a the establishment and

maintenance of the core of the network for p erforming the route computations b propagation and use of

bandwidth and stability information of links in the ad ho c net work and c the QoS route computation algo

rithm While the core provides an ecient and lowoverhead infrastructure to p erform routing and broadcasts

in an ad ho c network the increasedecrease wave based state propagation mechanism ensures that the core

no des have the imp ortant linkstate they need for route computation without incurring the high overhead of

state maintenance for dynamic links The QoS routing algorithm is robust and uses only lo cal state for route

computation at each core no de

We b elieve that CEDAR is a robust and adaptive algorithm that reacts quickly and eectively to the dy

namics of the network while still approximating linkstate p erformance for stable networks Our simulations

show that CEDAR pro duces go o d stable admissible routes with a high probabilityifsuch routes exist Further

more CEDAR do es not require high maintenance overhead even for highly dynamic networks Ongoing work

on CEDAR is fo cusing on two areas a While wehaveshown that CEDAR is eective for small to medium

size networks we are working on a hierarchically clustered version of CEDAR that can provide QoS routing in

large ad ho c networks b While wehave only considered bandwidth as the QoS parameter in this work we

are extending CEDAR to supp ort also delay as a QoS parameter

References

R Nair B Ra jagopalan Hal Sandick and Eric Crawley A framework for Qosbased Routing in the

Internet May

R Sivakumar B Das and V Bharghavan Spine Routing in Ad ho c Networks ACMBaltzer Cluster

Computing Journal Special Issue on Mobile ComputingTo app ear

J Jubin and J D Tornow The DARPA packet radio network proto cols Proce edings of the IEEE

January

D A Hall Tactical internet system architecture for task force xxi Proceedings of the Tactical Communi

cations Conference Ft Wayne Indiana May

V Bharghavan S Shenker A Demers and L Zhang Macaw A medium access proto col for wireless lans

Proceedings of the ACM SIGCOMM London England August

Chane L Fullmer and JJ GarciaLunaAceves Solutions to hidden terminal problems in wireless networks

Proceedings of ACM SIGCOMM Cannes France Septemb er

Songwu Lu Vaduvur Bharghavan and Rayadurgam Srikant Fair queueing in wireless packet networks

Proceedings of ACM SIGCOMM Cannes France Septemb er

D B Johnson and D A Maltz Dynamic source routing in ad ho c wireless networks Mobile Computing

ed T Imielinski and H Korth Kluwer Academic Publishers

S Corson and A Ephremides A highly adaptive distributed routing algorithm for mobile wireless M

networks ACMBaltzer Wireless Networks Journal February

V D Park and M S Corson A highly adaptive distributed routing algorithm for mobile wireless networks

Proceedings of IEEE Conference on Computer Communications page April

C E Perkins and PBhagwat Highly dynamic destinationsequenced distancevector routing dsdv for

mobile computers Computer Communications Review ACM SIGCOMM

S Murthy and JJ GarciaLunaAceves A routing proto col for packet radio networks Proceedings of

ACM Mobicom Berkeley California September

V Bharghavan Performance of multiple access proto cols in wireless packet networks International Per

formance and Dependability Symposium To app ear

BaruchAwerbuch Yi Du Bilal Khan and Yuval Shavitt Routing through networks with top ology aggre

gation IEEE International Symposium on Computers and CommunicationsAthens Greece June

R Dub e C D Rais KY Wang and S K Tripathi Signal stability based adaptive routing ssa for

works Tech rep CSTR UMIACSTR pages Dept of Computer adho c mobile net

Science Univ of Maryland College Park September

CK Toh Anovel distributed routing proto col to supp ort adho c mobile computing Proceedings of th

IEEE Annual International Phoenix Conference on Computers and Communications pages

N Shacham and J Westcott Future directions in packet radio architectures and proto cols Proceedings of

the IEEE January

P F Tsuchiya The landmark hierarchy a new hierarchy for routing in very large networks ACM

SIGCOMM pages

W T Tsai C V Ramamo orthy W K Tsai and O Nishiguchi An adaptive hierarchical routing proto col

IEEE Transactions on Computers August

R L Hamilton Jr and HC Yu Optimal routing in multihop packet radio networks Proceedings of

IEEE Conference on Computer Communications Infocom pages June

D L Mills Wiretap An exp erimental multiplepath routing algorithm Computer Communications

Reviewvolume pages January

J E Wieselthier C M Barnhart and A Ephremides A neural network approach to routing in multihop

radio networks Proceedings of IEEE Conference on Computer Communications INFOCOM pages

April

S Corson J Macker and S Batsell Architectural considerations for mobile mesh networking May

Z Wang and J Crowcroft QoS routing for supp orting resource reservation IEEE Journal of Selected

Areas of Communications Septemb er

Qingming Ma and Peter Steenkiste Qualityofservice routing for trac with p erformance guarantees Pro

ceedings of IFIP Fifth International Workshop on Quality of Service pages Columbia University

New York May

C Pornavalai G Chakrab orty and NShiratori QoS based routing algorithm in integrated services packet

networks Proceedings of the IEEE International Conference on Network ProtocolsAtlanta USA Octob er

Ro ch A Guerin Ariel Orda and D Williams QoS routing mechanisms and OSPF extensions

H F Salama DS Reeves and YViniotis A distributed algorithm for delayconstrained unicast routing

Technical Rep ort TR Center for Advanced Computing and Communication North Carolina State

University June

Q Sun and H Langendo erfer A new distributed routing algorithm for supp orting delaysensitive appli

eig cations Technical rep ort Institute of Op erating Systems and Computer Networks TU Braunschw

Bueltenweg Braunschweig Germany March

Qingming Ma Peter Steenkiste and Hui Zhang Routing highbandwidth trac in maxmin fair share

networks Proceedings of ACM SIGCOMM pages Stanford California August

Ron Widyono The design and evaluation of routing algorithms for realtime channels Technical Rep ort

TR International Computer Science Institute Berkeley CA June

Dean H Lorenz and Ariel Orda QoS routing in networks with uncertain parameters Proceedings of IEEE

INFOCOM San Francisco California April

Ro ch A Guerin and Ariel Orda QoSbased routing in networks with inaccurate information Theory and

algorithms In Proceedings of IEEE INFOCOM Kob e Japan April

George Ap ostolop oulos Ro ch Guerin Sanjay Kamat and Satish K Tripathi Quality of service routing

A p erformance p ersp ective In Proceedings of ACM SIGCOMMVancouver Canada Septemb er