Lo cal Error Recovery in SRM
Comparison of Two Approaches
Ching Gung Liu Deb orah Estrin Scott Shenker and Lixia Zhang
Memb ers use propagation delays to schedule their request Abstr act SRM is a framework for reliable multicast de
livery In order to maximize the collab oration among the
and reply timers each memb er detecting a loss waits for a
group memb ers in error recovery b oth retransmission re
random time p erio d b efore sending the retransmission re
quests and replies are multicast to the entire group While
quest and similarly each memb er receiving a retransmis SRM e ectively uses random timers to suppress duplicate
requests and replies the global nature of the request and
sion request waits for a random time p erio d b efore sending
replies means that every packet loss results in at least one
the reply When memb ers receive a retransmission re
request and reply message sent to the entire group
quest or resp ectively a reply message while waiting to To further improve the scalability of SRM one must lo
calize the scop e of error recovery tra c In this pap er we
send one of their own they cancel their scheduled trans
present two approaches to lo cal recovery hop based scop e
mission This enables SRM to suppress duplicate requests
control and use of lo cal recovery groups The rst approach
and replies and thus avoid the request and reply message uses hop count to limit the distribution of requests and
replies whereas the second approach con nes error recov
implosion problem
ery tra c using separately addressed lo cal recovery groups
However each packet loss will result in at least one re
The lo cal recovery groups and hop count settings are au
quest and one reply message b eing sent to the entire mul tomatically created and dynamically adjusted based on ob
served loss patterns We use simulation exp eriments to ex
ticast group This limits the scalability of SRM as network
amine the p erformance of b oth approaches
and group size increases As suggested in the
premise of this pap er is that the error recovery mechanism
should isolate error recovery tra c to the required scop e
Intro duction
In this pap er we present two di erent mechanisms to lo
calize the scop e of error recovery tra c The hop scop ed
Scalable Reliable Multicast SRM is a framework
error recovery mechanism uses hop count to limit the dis
for reliable multicast delivery it guarantees data deliv
tance that request and reply messages can travel In con
ery to all memb ers in a multicast session The mecha
trast the group scop ed error recovery mechanism con nes
nisms needed to achieve this reliability can b e decomp osed
the propagation of error recovery tra c by distributing it
into two parts session message exchange and receiver
to subsets of the group using separate multicast addresses
initiated error recovery Memb ers p erio dically exchange
Simulation results of these mechanisms suggest that they
session messages to rep ort current group state e g the
b oth reduce error recovery tra c without intro ducing sig
highest received sequence numb er from each source so that
ni cant overhead
losses can b e detected and to determine the propagation
Note that lo cal recovery is a p erformance optimization
delays b etween each pair of memb ers The error re
thus the mechanisms do not have to achieve the optimal or
covery mechanism is receiver initiated and NAK based
precise degree of lo cality the more lo cal the recovery the
receivers are resp onsible for detecting data losses and re
less recovery tra c overhead there is In b oth mechanisms
questing retransmissions These retransmission requests
that we prop ose here a memb er may o ccasionally send
and the resulting replies are multicast to the entire group
its requests and replies to an inappropriate scop e While
such mistakes have a slight impact on the p erformance
Ching Gung Liu is with the Fujitsu Lab oratories of America Inc e
mail charley a fujitsu com
of SRM in terms of the volume of error recovery tra c
Deb orah Estrin is with the Computer Science Department Information
they have no impact on its correctness since all data losses
Sciences Institute University of Southern California e mail es
trin usc edu
are eventually recovered
Scott Shenker is with the Xerox Palo Alto Research Center e mail
The pap er is organized as follows Sections and de
shenker parc xerox com
Lixia Zhang is with the Computer Science Department University of
scrib e the hop scop ed error recovery and the group scop ed
California Los Angeles e mail lixia cs ucla edu
error recovery mechanisms resp ectively Section presents
This research was supp orted in part by the Advanced Research Pro jects
Agency monitored by Fort Huachuca under contracts DABT C
the simulation mo dels and analyzes the simulation results
and by the National Science Foundation under grant award No
and Section reviews related work We conclude in Section
NCR The views expressed here do not re ect the p osition or
p olicy of the U S government
with a short summary
1
Although the delay from a memb er and the delay to a memb er may b e
di erent SRM assumes the path b etween a pair of memb ers are symmet
2
SRM assumes most or all session memb ers not only the data source
ric thus each memb er determines its one way delay to another memb er by
save all the application data If some memb ers do not save the data
taking half of its measured round trip delay This symmetry assumption
requested they simply do not participate in the error recovery pro cess
should cause no p erformance p enalty even when the paths are asymmet
3
ric b ecause the delay b etween memb ers is mainly used to di erentiate The discussion of lo calizing session messages is outside the scop e of
memb ers in setting their retransmission timers this pap er More information can b e found in
Request Hop Count Hop Scop ed Error Recovery
Each requester simply sets its request hop count large
The simplest way to control the scop e of requests and
enough to reach at least one memb er that is closer to the
replies is to limit the numb er of hops they travel We
source This memb er do es not necessarily share the same
wish to use the minimum hop counts p ossible in requests
data delivery path with the requester all that matters is
and replies To minimize the hop limit for request mes
that the upstream memb er is closer to the source than
sages our design takes the approach that a memb er p s re
the requester Hence the hop count to reach an upstream
quest extends just far enough to reach some other memb er
memb er for a memb er p regarding a source s in a session
q who is closer to the source If the loss o ccurred b etween
s
G d can b e set to
p
q and p then q will b e able to retransmit the lost packet
If the loss o ccurred elsewhere so that q missed the packet
s
d minfh j q G h h g
pq sq sp
p
as well then we only need to make sure that q will send
a request further up towards the source All that matters
where h is the distance in terms of the numb er of hops
pq
is that at least one request makes it across the lossy link
from p to q
i e link where the loss o ccurs and it is likely that this
comes from the closest memb er b ehind the lossy request r
ste
h memb er s request timer is set prop ortional link since eac e qu
source re requester
to the measured delay from the source replier re requester In the original SRM design a request is used to suppress qu e
st
as well as to ask for repair While lim
duplicate requests er
iting the hop count of the request message limits the over
head it generates it also diminishes its ability to suppress
Figure Multicasting requests with a limited hop count
other memb ers from sending the same requests Fortu
reduces the e ectiveness of request suppression Scenario
nately the request hop count in our mechanism generally
in the string top ology
sp eaking is relatively small compared to the distance in
terms of the numb er of hops to the source Thus the re
Because a limited hop count reduces the e ectiveness of
quest overhead p er loss is acceptable even though multiple
request suppression multiple requests regarding the same
requests for retransmitting the same data are generated
loss may b e generated In particular two scenarios illus
Moreover b ecause request timers are based on the propa
trated in Figure and are known to cause duplicate re
gation delay from the source a memb er far b ehind the lossy
quests In Figure the hop count to an immediate up
link may receive a reply b efore sending its own request
stream memb er is smaller than the hop count to an im
While we greatly limit the scop e of requests we require
mediate downstream memb er Therefore requests sent by
that a reply have su cient scop e to reach all memb ers who
upstream memb ers can not reach their downstream mem
share the same loss Since a replier do es not know where
b ers to suppress them from sending out the same requests
a packet is dropp ed it is di cult for the replier to de
In the worst case the request overhead within the loss re
cide how far the retransmission must go However if a
gion can b e two requests p er link one traveling upstream
requester assumes it is immediately b ehind the lossy link
and the other downward We consider such overhead ac
it can determine as we show b elow an upp er b ound on the
ceptable
hop count needed to reach all other memb ers b ehind the
requester p
The upp er b ound is called the proxy hop count
lossy link requester q
the requester acts as request proxy for memb ers
b ecause requester
When a replier receives a request who share the same loss source replier
s r requester
h hops away and the proxy hop count of
from a requester requester
requester is P then the replier s reply hop count
the requester
en by h P Note that the reply hop count is
is giv requester
an upp er b ound and the reply may reach memb ers who do
Figure Multicasting requests with a limited hop count
not share the loss
reduces the e ectiveness of request suppression Scenario
Our hop scop ed error recovery requires a memb er to
in the star top ology
measure its distances in terms of the numb er of hops to
all the other memb ers in the same session The distance
The scenario illustrated in Figure can cause much
is measured by exchanging session messages Since session
higher request overhead All the requesters in Figure
messages are p erio dic the measurement is also p erio dically
cho ose r as the replier They multicast their requests with
refreshed We will discuss the algorithm to determine the
a hop count value that is big enough to reach r but not big
request and proxy hop counts in Section and Section
enough to reach one another for duplicate suppression In
The detailed mechanism is describ ed in Section
the worst case the numb er of requests p er loss within the
4
In a step reply relying hop scop ed error recovery mechanism loss region is equal to the numb er of requesters Therefore
is suggested but without a sp eci c metho d to measure the request and
to minimize the numb er of duplicate requests requesters
reply hop counts Our prop osed mechanism can also b e used to measure
the request and reply hop counts in have to set their request hop counts large enough not only
h the replier but also to reach one another for sup to reac source s
s pression Π
r
The characteristic of this scenario in Figure is that
s
ultiple requesters do not cho ose one another as repliers m π
u u
retransmission For example b oth p and q request r
for r
is closer to the source than p and q Because the who π s v p
w w t in general replier selection is autonomous and indep enden π s
v
memb er will not know the selection of other memb ers a π s
p π s
wever two memb ers p and q are very likely to b e in
Ho q q
the scenario describ ed in Figure if they fall within the
following three conditions
p requests someone closer to the source than q
Figure Request and reply hop counts Thick lines rep
q requests someone closer to the source than p and
resent data delivery path circles represent the regions of
their request scop es overlap with each other
request and reply scop es
To check Conditions and a memb er p needs to cal
culate the distance from source s to its intended replier
s
with p An upstream memb er should b e proxy for its
denoted by d
p
downstream memb ers to request retransmission If q
s s
d minfh j r G h d g
is downstream of p then h h is equal to h
sr pr
sp pq sq
p p
However b ecause a memb er may b e one hop away
s
If d h then p concludes that it do es not request
sq
p
from its rst hop router h h may b e two hops
sp pq
s
memb er q for retransmission Similarly if d h then
sp
q
greater than h We refer to the downstream distance
sq
s
p concludes that it is not the intended replier for memb er
of p regarding a source s as D
p
q For Condition p concludes that their request scop es
s s
s
overlap if d d minfh h g Therefore memb er
pq q p
maxfh j q G D
p q
pq
p
p can compute the required hop count to suppress other
s
requests as d
h h h h h g
p
sp sq sp pq sq
s
p and u are siblings one s path from s is not a subset
d maxfh j q G
pq
p
of the other and u requests p for repair In other
s s s s
d h d h d d minfh h g g
sq sp pq q p
words p is closer to source s than u and p is within the
p q p q
radius of u s request hop count To b e more sp eci c
The request hop count of p regarding source s denoted by
s
s s
s p is within the radius of d Therefore p has to b e
u
is the maximum of d and d i e
p p p
proxy for u as well as u s downstream memb ers We
s
s s
s
refer to this distance as D
maxfd d g
p
p p p
s s s
The request hop count is calculated on a p er source basis
g j u G D maxfh maxfD D
pu
u u p
under the assumption that source sp eci c multicast distri
s
bution trees are used Issues related to the use of other h h h d g
sp su up
u
typ es of multicast trees are left for future study
p and v are also siblings and v is within the radius of
s
p s request hop count h although v is not
pv
p
s
Proxy Hop Count
p s intended replier d h p has to b e proxy for
sv
p
v and v s downstream memb ers b ecause v s requests
A requester sets its proxy hop count so as to reach other
may b e suppressed by p We refer to this distance as
memb ers that share the same loss Since a requester has
s
Since v s requests are suppressed by p p do es D
p
no knowledge of the underlying network top ology it can
s s
in computing D not have to consider D
p v
only estimate an upp er b ound of its proxy hop count
A requester only has to consider memb ers farther away
s s s
g j v G D maxfh maxfD D
pv
v v p
from the source than itself in determining its proxy hop
count There are four kinds of relationship b etween a re
s
s
d h h g
sv pv
p p
quester and a memb er farther from the source They are
demonstrated in Figure by memb er pairs fp q g fp ug
p and w are also siblings but they are not within the
fp v g and fp w g
request hop counts of each other i e w do es not ask
p for repair and its requests are not suppressed by p
p and q have an upstream downstream relationship
Therefore w sends its own requests and p do es not
That is The path from s to p is a subset of the path
need to proxy for w
from s to q In this case q most likely shares losses
The proxy hop count is calculated on a p er source basis
5
Sally Floyd help ed identify this pathological case in an earlier version
of this pap er A requester p can determine its proxy hop count regarding
s s
s
the rst reply and the relayed reply overlap thus memb ers and D source s P by taking the maximum of its D
p p p
s
within the overlapp ed area receive duplicate replies Since from all memb ers from which it hears session messages D
p
the distance b etween a replier and a requester h is rela
s s s
s
g D D P maxfD
tively small in the average case multicasting a reply with a
p p p p
hop count of h P should not intro duce signi cantly more
A replier r determines its reply hop count triggered by
overhead in terms of network bandwidth Therefore in our
s
an incoming request from p regarding a loss from s as
r
hop scop ed error recovery a replier uses h P for its reply
hop count If multiple requests are received the replier
s s
h P
r p
r p
takes the maximum P value received in its calculation
One question regarding the hop scop ed error recovery
mechanism is that the hop scop ed packets may prevent
Mechanism Description and Discus
multicast pruning in dense mo de multicast routing pro
sion
to cols such as DVMRP and PIM DM
Memb ers exchange session messages to measure the dis
As a result hop scop ed multicast packets b ecome lo cally
tance in terms of the numb er of hops from and to other
broadcast i e they reach all the routers within the hop
memb ers Additional information is carried in each session
limit including those that are not on the multicast routing
s s
message In particular memb er p includes h d d
sp
p p
tree However in SRM b ecause each memb er p erio dically
s s
and maxfD D g for each source s and h for each
pq
p p
multicasts global session messages those session messages
memb er q in its session messages The distance b etween
causes branches that reach no memb ers to b e pruned o
each pair of memb ers is used to compute the request and
Therefore hop scop ed request and reply tra c is contained
proxy hop counts
within the multicast tree
The computation of request and proxy hop counts is p er
Ideally a request should reach a few neighb ors who have
formed iteratively A memb er recomputes its request and
the requested data and a reply should reach only those
proxy hop counts when a new session message is received
memb ers who lost the same packet However b ecause
It takes as few as two session cycles for a memb er to
hop scop ed multicast tra c radiates in all directions the
compute its request hop count The computation of the
bandwidth overhead can b e signi cant in some cases es
proxy hop count takes several session cycles to converge
p ecially in case of the reply tra c For example the reply
b ecause it dep ends on results from other memb ers D to
from memb er r in Figure propagates upstream as well as
b e sp eci c In order to capture session dynamics the com
downstream to recover a loss In the next section we con
putation of request and proxy hop counts is timestamp ed
sider the use of separate multicast groups to more precisely
and aged so obsolete results will b e timed out
control the scop e of error recovery tra c
When a packet loss is detected a requester multicasts
a request within the radius of its request hop count The
request message carries distance from the source to the in
Group Scop ed Error Recovery
tended replier d and the proxy hop count d is used
to determine request suppression A memb er suppresses
A local recovery group consists of a set of memb ers who
its scheduled request if the received request is intended to
share common data losses to at least some degree Mem
reach someone closer to the source Otherwise the sched
b ers share the same losses b ecause they share one or more
uled request should b e sent The replier uses the proxy
lossy links along the data delivery path from a source
hop count to determine the reply hop count Normally
Because we assume source sp eci c multicast distribution
at least one request from memb ers b ehind the lossy link
trees the creation of lo cal groups is on a p er source basis
reaches a memb er with the requested data and triggers a
However our mechanism do es not limit memb ers to a sin
reply However if no reply is received due to packet loss
gle lo cal group p er source Multiple lo cal groups can b e
or underestimated request hop count the requester sends
asso ciated with a source where each group is resp onsible
a second request globally and the corresp onding reply will
for error recovery for one or more lossy links For a sp eci c
b e sent globally as well
source the relationship among these lossy links is either
A di erent approach to reply scop e control would b e
ancestor descendant or siblings so that these lo cal recov
that when a replier resp onds to a request it multicasts its
ery groups are either p erfectly nested or totally disjoint as
reply with a hop count h where h is the distance to the
shown in Figure
original requester After receiving the reply the original
Our group scop ed error recovery follows a basic SRM
requester relays the reply to other memb ers downstream
design principle of each memb er b eing an autonomous en
within the radius of its proxy hop count P However
tity That is each memb er makes its own decisions on
this step reply relaying scheme intro duces additional de
whether to join or leave a lo cal group There is no central
lay in reply propagation which may cause additional du
ized co ordination among memb ers Memb ers use the error
plicate requests b eing sent Furthermore the scop es of
ngerprints to measure the degree of loss sharing with a
6
A session cycle time is the p erio d b etween two consecutive session lo cal group An error ngerprint is the sequence numb ers
messages sent by a memb er Since all memb ers send their session messages
of the last f losses in a lo cal group For example a mem
at the same rate a memb er should receive a session message from each
memb er during a session cycle time b er p shares losses with a lo cal group G if p lost more
to discover existing lo cal groups thus reduces the overhead
group creation On the other hand if the
source of unnecessary
waiting p erio d is short a new group can b e created quickly
and the overhead of the global error recovery is reduced
at the exp ense of group creation overhead Since an er
ror ngerprint is required for prop osing a lo cal group the
waiting p erio d b efore a memb er prop osing a group has to
b e long enough to cover f losses
Figure Memb ership of lo cal recovery groups are either
A memb er prop oses a new lo cal group in its request mes
p erfectly nested or totally disjoint
sage by including the prop osed multicast address and the
error ngerprint Since the prop osed lo cal group is not yet
created the memb er uses the sequence numb ers of its own
than half of the packets sp eci ed in G s error ngerprint
losses as the initial group error ngerprint The request
At the b eginning of a session data losses are recovered
prop osing a lo cal group is multicast globally to suppress
globally A memb er who su ers noticeable data losses from
other group prop osals If a memb er has joined any lo cal
a source prop oses the creation of a lo cal recovery group in
group it is not allowed to prop ose additional lo cal groups
its retransmission request sent globally together with its
However it may join other groups as appropriate
error ngerprint The creation of the lo cal group is granted
A replier grants the creation of a new lo cal group in
by a replier in its reply Since the reply is sent globally
its reply The reply message includes the address and the
other memb ers who have a matching error ngerprint join
error ngerprint of the granted lo cal group This message
the lo cal group when they receive the reply These mem
is multicast globally to solicit memb ers who share the same
b ers are called the regular members or simply memb ers
losses Furthermore the replier joins the lo cal group as a
of the group and their subsequent requests are sent to the
help er which assures that there is at least one help er in
lo cal group Other memb ers selectively join a lo cal group
the new group
to help error recovery They are called helpers of the group
When a help er receives a request from a memb er of a lo cal
group the help er sends its reply to that same lo cal group
Joining a Lo cal Group as a Regular
A regular memb er in a lo cal group measures the extent
Memb er
to which it shares losses with that group It stays in the
group if the degree of loss sharing is high otherwise it
A memb er joins a lo cal group if it shares more than of
leaves the group If a regular memb er has joined multiple
the losses with the group where When
nested groups it always sends retransmission requests to
a reply granting a new lo cal group is received a memb er
the innermost group rst In cases the loss actually o c
joins the group if the similarity of its own losses and the
curred in an outer group another memb er who sees the
error ngerprint of the granted group exceeds
outer group as its innermost group should have detected
If a memb er joins multiple lo cal groups these groups
the loss and requested retransmission
must b e nested That is the memb ership of an inner group
Our group scop ed error recovery follows the soft state
is a subset of the memb ership of an outer group It is im
approach Memb ership solicitation and loss sharing mea
p ortant that all memb ers maintain a consistent view of
surement are p erio dically refreshed to capture session dy
group order so they can exercise these nested groups in
namics The mechanism is describ ed in detail in the fol
the same fashion and pro duce correct loss sharing mea
lowing sections In particular we discuss the criteria for
surement The group order is also used in error recovery
prop osing granting joining and leaving lo cal groups
pro cess since a memb er always sends its requests to its in
nermost lo cal group rst One simple way to determine the
order of a lo cal group is by the sequence numb er of the re
Prop osing and Granting a Lo cal
ply granting the lo cal group The sequence numb er of the
Group
reply granting a lo cal group is called the order number of
A memb er prop oses a lo cal group if its error rate exceeds
the lo cal group To b e precise an order numb er consists of
where In the extreme case we can cho ose
the sequence numb er of the reply in the high order p ortion
to encourage all error recovery to b e handled by lo cal
and the lo cal group address in the low order p ortion We
groups If a memb er decides to prop ose a lo cal group it
assume a lo cal group address is unique within a session If
waits a p erio d of time b efore prop osing in order to learn of
multiple lo cal groups are created with the same sequence
existing lo cal groups If there is an existing lo cal group the
numb er their order numb ers are still unique Generally
memb er joins the existing lo cal group instead of prop osing
sp eaking a lo cal group granted later has a larger group
a new one Section discusses in more detail the joining
order numb er and a larger scop e Note that the original
pro cess The waiting p erio d can b e measured in terms of
session group is always the outermost group even though
time numb er of losses or numb er of received data packets
it do es not have an order numb er
A longer waiting p erio d increases the chance for a memb er
The order of nested groups may not re ect their phys
ical scop es at a particular p oint of time a transient phe
7
A similar approach was prop osed in to solve the implosion of
multicast congestion feedback nomenon that will b e xed after the requests and replies
t If it receives multiple requests that are sent to dif
source source source source sen
ferent groups the replier addresses its reply to the group
umb er p p p p with the largest order n
G2 G2
Leaving a Lo cal Group
G1 G1 G1 G1
A memb er measures the degree of loss sharing in each lo
group it joins by the ratio of the numb er of its total
(a) (b) (c) (d) cal
losses over the numb er of received replies from the group
Figure Evolution of misplaced nested lo cal groups
For example the loss sharing can b e measured every m
replies received in a lo cal group To prevent oscillation
exp onentially weighted moving average is adopted If a
disseminate completely For example in Figure a new
memb er s loss sharing ratio is smaller than it leaves the
memb er p may prop ose a new group G b efore learning of
lo cal group
the existing lo cal groups G Figure a and b p will
A help er leaves a lo cal group if its last k consecutive
b e solicited to join G later and then it will use G as the
scheduled replies for the lo cal group are suppressed As a
innermost lo cal group Figure c At this p oint of time
result there are at most k help ers in a lo cal group
the physical scop e of G is larger than that of G Even
If there is no error recovery tra c in a lo cal group the
tually G will b e timed out and disapp ear Figure d
lo cal group should b e timed out to reduce group main
The group timeout scheme is discussed in Section and
tenance overhead Both help ers and memb ers determine
the memb ership solicitation scheme is discussed in Section
when a lo cal group is dormant and leave the group The
timeout p erio d can b e measured in terms of seconds or the
The threshold de nes the tradeo s b etween the num
numb er of received data packets
b er of nested lo cal groups and the error recovery p erfor
mance For large value more nested lo cal groups are
created and each group has a higher loss sharing ratio and
Soliciting New Memb ers
achieves greater e ciency for retransmission As a result
Since an error ngerprint is a snapshot of the group losses
the group maintenance overhead is higher and the error
a memb er who shares the ma jority of losses with a lo cal
recovery p erformance is b etter On the other hand for
group may unfortunately decide not to join when it learns
small fewer nested lo cal groups are maintained but the
ab out the group the rst time Furthermore when a new
loss sharing ratio in each lo cal group is also lower In the
memb er joins an ongoing session it has no knowledge of
extreme case if we cho ose there is only one lo
the existing lo cal groups A scheme to p erio dically solicit
cal group in the session to recover all losses if we cho ose
new memb ers is necessary to capture new memb ers as well
the numb er of lo cal groups is equal to the num
as old memb ers whose snapshots happ ened to b e skewed
b er of lossy links and each group recovers losses over each
A lo cal group solicits new memb ers by p erio dic p olling
lossy link
Memb ers p erio dically send their requests to the next outer
group The group address order numb er and error nger
Error Recovery in a Lo cal Group
print of the inner group are carried in the p olling request
When a loss is detected a memb er sends its request to its to solicit new memb ers and help ers
innermost group rst If there is no reply it will expand Memb ers in the outer group join the inner group based
its request scop e by trying its next outer group until the on the comparison of their own losses and the inner group
loss is recovered As describ ed earlier even if a request error ngerprint Since the p olling requests are sent to
to the innermost group do es not reach a help er memb ers the next outer group a new memb er joins lo cal groups
in the outer group should have detected the loss and sent one at a time in an outside in fashion until it has joined all
their requests Therefore the ma jority of the losses are nested lo cal groups Note that the p erio dic p olling requests
recovered quickly and sending requests to an outer group scheduled by inner group memb ers suppress one another
up on timeout should happ en rarely Since memb ers in the If a requester do es not receive a retransmission in its rst
inner group may rely on memb ers in the outer group to ask try the next repair request addressed to its outer group
for data repair a memb er p s scheduled request should not can also serve the purp ose of memb ership solicitation
b e suppressed by a request from a lo cal group G if p is not Our mechanism requires that a memb er joins an outer
a regular memb er in G p can b e a help er in G to receive a lo cal group b efore an inner lo cal group A help er do es
request sent to G In other words a request addressed to not solicit new comers into a lo cal group since a help er
a lo cal group should only suppress requests of other regular do es not maintain the order of nested lo cal groups If a
memb ers in that group memb er is solicited into an inner group without joining
The order numb er of the addressed group is included in prop er outer groups it cannot received replies triggered
the request message It is used by a replier to determine by requests from outer groups Consequently its losses
the destination group for the corresp onding reply A replier require multiple request iterations to recover resulting in
sends its reply to the lo cal group to which the request was longer recovery delay and higher bandwidth overhead
The same scheme is used to solicit new help ers If a average measured numb er of replies p er loss The recovery
replier in the outer group resp onds to a p olling request it delay is measured in terms of the one way propagation de
joins the corresp onding inner group as a help er However lay from the data source In other words it is the interval
if the request can b e handled by a help er that is already in b etween a memb er s detection of a loss and reception of a
the inner group this help er is closer to the requester and retransmission divided by the one way propagation delay
is most likely to resp ond rst Therefore a new help er will from the data source to the memb er
rarely need to join the inner group unless all help ers in the In our simulations we adopted a random timer adap
inner group have left tation mechanism to optimize the p erformance of the re
covery delay and the numb er of requests and replies p er
loss The general idea is to make the generation of re
Simulation Results and Discus quest and reply timers adaptive to the network and session
environment A memb er estimates the numb er of comp et
sion
ing requesters and repliers by interpreting feedback from a
session and uses the estimated values to tune its request
We b elieve the b ehavior of our prop osed mechanisms can
and reply timer parameters These parameters determine
b e b est understo o d by rst testing a variety of extreme
whether requests and replies are generated aggressively or
settings b efore moving on to more general scenarios In
conservatively Generally sp eaking adopting lo cal error
this section we rst explore our lo cal recovery mechanisms
recovery limits the numb er of comp eting requesters and
in three extreme but simple top ologies star string and
repliers which allows memb ers to send requests and replies
binary tree each with a single data source The star top ol
more aggressively and reduces recovery delays More dis
ogy represents a session where all memb ers have indep en
cussion is in
dent losses The string top ology represents a session where
downstream memb ers share the same losses with their up
Top ologies with Multiple Lossy Links
stream memb ers The binary tree top ology represents a
mixture of shared and indep endent losses in a session
The rst set of simulations assumed that all links have
Each top ology is tested with ve di erent session sizes
uniformly distributed error rates
and We simulated the p erformance of ve
di erent mechanisms for each session size the global er
The Star Top ology
ror recovery the hop scop ed error recovery and the group
scop ed error recovery with three di erent degrees of loss
Figure a b and c show the simulation results in
sharing and We cho ose error
the star top ology Memb ers in the star top ology have inde
threshold to prop ose a lo cal group k threshold for
p endent losses hence there is no loss shared among mem
an inactive help er to leave a lo cal group m num
b ers and approximately one request message p er loss is
b er of losses in a loss sharing measurement p erio d and
generated Since the distances b etween each pair of mem
f numb er of losses in an error ngerprint in all the
b ers are equal the hop scop ed error recovery p erforms ex
group scop ed error recovery simulations Each simulation
actly like global error recovery Note that the numb er of
starts with a warmup p erio d and measures the error recov
available help ers for a sp eci c loss is large in the global
ery activities of the next data losses Data losses are
error recovery and the hop scop ed error recovery there are
generated by assigning an uniformly distributed error rate
multiple replies generated p er loss Figure b
on each lossy link of the simulated top ologies and these
In the group scop ed error recovery each memb er cre
error rates are xed throughout a single simulation The
ates its own lo cal group The requests and replies only
aggregated error rate among all links is of the traf
propagate within individual lo cal groups Because of the
c including data requests replies and session messages
constant numb er of help ers in a lo cal group k the re
The data rate is packets p er second The link delay is
quest and reply tra c decreases by increasing session size
ms for links b etween routers and ms for links con
The numb er of lo cal groups in the group scop ed error
necting group memb ers The session cycle time is
recovery is equal to the numb er of lossy links In general
seconds The warmup p erio d is de ned as each lossy link
if there are n memb ers in a session the numb er of lo cal
exp eriencing at least losses The total simulation time
of total groups is equal to n Each lo cal group recovers
n
is roughly seconds
losses in the session and its scop e is roughly of the session
n
The p erformance is evaluated by three metrics the re
scop e Therefore we can estimate a lower b ound on the
quest tra c the reply tra c and the recovery delay The
request and reply tra c in the group scop ed error recovery
request tra c is the pro duct of the average measured re
of the tra c in the global error recovery Since this as
n
quest scop e and the average measured numb er of requests
estimated request and reply tra c is a lower b ound it
p er loss The request scop e is a fraction of the global scop e
represents the greatest degree of savings p ossible The
and it is measured in terms of the numb er of hops that a
estimated values are plotted as gray curves in Figure a
request propagates For example in the global error recov
and b
ery the request scop e is equal to the global scop e since each
8
Upp er b ound estimates would need to take several other factors into
request is multicast to the entire session The reply tra c
account For example the numb er of help ers and memb ership dynamics
in a lo cal group is the pro duct of the average measured reply scop e and the
| | | | | | | | | | | | | | 125 | | 225 | 6.0 |
|
200 | | 100 175 | 5.8 |
| 150 |
| 75 5.6 125 | | 100 |
50 | 5.4 75
|
| | 50 25 5.2 | 25 | | | 0 | | | | || | | | |0 | | | | || | | | | | | | |5.0 | | | | | || | | | | | 0 35 70 105 140 0 35 70 105 140 0 35 70 105 140 (a) session size (b) session size (c) session size
reply traffic % Star top ology recovery delay
request traffic %
| | | | | | | | | | | | | | 150 | | 150 | 4 |
| |
125 125 | 3 | | 100 100 | | | 75 75 2 | | 50 50
|
1 25 | 25 | | | | 0 | | | | || | | | | |0 | | | | | || | | | | |0 | | | | | || | | | | 0 35 70 105 140 0 35 70 105 140 0 35 70 105 140 (d) session size (e) session size (f) session size
reply traffic % String top ology recovery delay
request traffic %
| | | | | | | | | | | | | | 150 | | 200 | 6 |
|
| 175 125 | | 150 5 | 100 |
125
| | | 75 100 4
| | 75 50 | | 50 3 | 25 | 25 | | | 0 | | | | || | | | | |0 | | | | | || | | | | | | |2 | | | | | || | | | | 0 35 70 105 140 0 35 70 105 140 0 35 70 105 140 (g) session size (h) session size (i) session size
reply traffic %
Tree top ology recovery delay request traffic %
β Global error recovery Group-scoped error recovery : = 33% Group-scoped error recovery : β = 50% Hop-scoped error recovery Group-scoped error recovery : β = 100%
represents the estimated lower bound of the group-scoped error recovery
Figure Simulation results of all links with uniformly distributed error rates
The String Top ology numb er of requests p er loss is sub linear in terms of the
session size the average request tra c decreases with the
In the string top ology downstream memb ers share losses
session size As describ ed earlier even if multiple requests
with all their upstream memb ers A downstream memb er
p er loss are presented in the hop scop ed error recovery the
can rely on its upstream memb ers to ask for repair in the
overall request tra c is improved b ecause the scop e of each
hop scop ed error recovery As a result request tra c is
request is small and a memb er far b ehind a lossy link may
reduced signi cantly Figure d
receive a reply b efore even sending a request
Topology Star topology String topology Tree topology Topology Star topology String topology Tree topology Session size 8 16 32 64 128 8 16 32 64 128 8 16 32 64 128 Session size 8 16 32 64 128 8 16 32 64 128 8 16 32 64 128 Requests per loss 1.01 1.03 1.03 1.03 1.03 2.27 3.24 4.00 5.17 7.90 1.21 1.80 2.03 2.28 2.60 Requests per loss 5.21 6.52 9.40 14.55 27.40 8.65 16.18 31.71 61.35 119.5 5.49 6.81 8.62 10.44 11.36 Request hops 16 32 64 128 256 6.26 6.71 6.86 6.92 6.95 9.17 9.06 9.68 10.18 9.99 Request hops 1.00 1.00 1.00 1.00 1.00 1.42 1.65 1.95 2.34 2.93 1.23 1.32 1.46 1.51 1.62 Request traffic % 101.3 102.9 103.3 103.2 103.1 88.81 67.82 42.87 27.96 21.46 69.58 50.86 30.72 18.12 10.14
Request traffic % 32.56 20.36 14.69 11.35 10.69 77.02 83.28 96.41 112.2 136.6 42.30 28.16 19.64 12.33 7.17
Table The numb er of requests p er loss and the numb er of
Table The numb er of requests p er loss and the num
hops that a request travels in the hop scop e error recovery
b er of hops that a request travels in the group scop e error
recovery with
Table shows the average numb er of requests p er loss
and the average numb er of hops that a request message
In the group scop ed error recovery request messages travels in the hop scop ed error recovery The numb er of
propagate to all downstream memb ers for suppression and requests p er loss in the string top ology increases with the
a limited numb er of upstream neighb ors for retransmis session size but the numb er of hops that a request mes
sion A request may not reach any help ers if the scop e of sage travels remains constant However the increase in the
Therefore the estimated error recovery tra c T can b e the request do es not cover the lossy link where the packet
P
n
i
was dropp ed When the length of the string increases the
calculated as T where i is the size of
i
n n
i
th
numb er of nested lo cal recovery groups increases and it is
the i lo cal group For the string top ology i n
more likely that requests from inner groups do not reach
i 1
log n log
2 2
The es For the tree top ology i
any help ers Therefore request tra c go es up with the
timated lower b ounds of the error recovery tra c in string
session size as shown in Figure d Table shows the av
and tree top ologies are
erage numb er of requests p er loss and the average numb er
of hops that a request message travels in the group scop ed
n
X
error recovery with The numb er of requests p er
n
T T
str ing tr ee
loss and the average hops of a request in the string top ol
n i n
i
ogy increase with the session size thus the average request
tra c increases with the session size
The estimated values are plotted as gray curves in Figure
In terms of reply tra c since the hop scop ed error re
d e g and h
covery do es not regulate the direction in which the reply
messages propagate the hop scop ed error recovery should
p erform much worse than the group scop ed error recovery
Top ologies with Randomly Selected
However in the group scop ed error recovery a requester
Lossy Links
sends it second request to its outer group if the rst one
did not reach any help er Since an outer group memb er
In the second set of simulations randomly selected
may already ask for repair the second request from the
of the links have uniformly distributed error rates which
inner group memb er is very likely to trigger a duplicate re
makes one lossy link in the no de top ologies two lossy
ply Consequently the numb er of replies p er loss increases
links in the no de top ologies and so on The simulation
and the improvement limited in comparison with the hop
results are shown in Figure Both the hop scop ed and
scop ed error recovery Figure e
group scop ed error recoveries outp erform the global error
The recovery delay in the hop scop ed error recovery de
recovery in terms of the request and reply tra c except
creases with the session size since request messages only
for the hop scop ed error recovery in the star top ology
reach a small numb er of memb ers As a consequence mem
Generally sp eaking the hop scop ed error recovery p er
b ers send requests more aggressively and the recovery delay
forms b etter than the group scop ed error recovery in terms
is reduced see the discussion of dynamic timer adaptation
of the request tra c if memb ers have an upstream
in Section On the other hand memb ers have large esti
downstream relationship i e they share losses Down
mated timer parameters in b oth the global error recovery
stream memb ers can rely on the requests from their up
and the group scop ed error recovery since requests reach all
stream memb ers to ask for retransmission therefore the
memb ers who share the loss However in the group scop ed
hop scop ed error recovery generates less request tra c in
error recovery only those memb ers whose innermost lo cal
the string and tree top ologies than the group scop ed error
group covers the lossy link are eligible to request retrans
recovery Figure d and g
mission therefore the average delay increases with the ses
On the other hand since the hop scop ed error recovery
sion size Figure f
do es not regulate tra c direction it do es not p erform well
in terms of the reply tra c if the degree of connectivity
in the top ology is high For example in the star and tree
The Tree Top ology
top ologies the reply tra c generated by the hop scop ed
The tree top ology is a mixture of the star and string top olo
error recovery p opulates in a much larger region than the
gies The average request tra c decreases with the session
reply tra c generated by the group scop ed error recovery
size in the group scop ed error recovery b ecause the num
Figure b and h Note that in the no de string
b er of nested lo cal groups is much smaller than it is in
top ology the reply tra c in the hop scop ed error recovery
the string top ology Figure g As shown in Table
is close to b ecause the randomly selected lossy links
b oth the numb er of requests p er loss and the request hops
are in the middle of the top ology Figure e
increases much more slowly with the session size
Interestingly the group scop ed error recovery with small
The numb er of lo cal groups in the group scop ed error re
e g not only generates more request and re
covery in the string and tree top ologies is prop ortional to
ply tra c but also pro duces longer recovery delay than
For example if each lo cal group is resp onsible
the group scop ed error recovery with large e g
for the error recovery of a single lossy links The numb er of
For small a memb er s error ngerprint matches
lo cal groups is equal to the numb er of lossy links in the ses
more easily with the error ngerprint of a lo cal group with
sion If a lo cal group is resp onsible for the error
which it do es not share losses If the lossy links are sparsely
recovery of two lossy links and the numb er of lo cal groups
distributed a memb er who joins a non loss sharing group
is equal to half of the numb er of lossy links In general for
by accident is more likely to request retransmission from
a session of size n the numb er of lo cal groups is n the
remote help ers Therefore small pro duces more band
and numb er of lossy links covered by a lo cal group is
width consumption and longer recovery delay than large
the p ercentage of losses recovered by a lo cal group is
Figure f and i
n
| | | | | | | | | | | | | | | | | | 125 225 6.0 |
200 | | 100 175 | 5.8 |
| 150 | 75 | 5.6 125 | | 100 |
50 | 5.4 75 |
| | 50 25 5.2 | 25 | | | 0 | | | | || | | | |0 | | | | || | | | | | | | |5.0 | | | | | || | | | | | 0 35 70 105 140 0 35 70 105 140 0 35 70 105 140 (a) session size (b) session size (c) session size
reply traffic % Star top ology recovery delay
request traffic %
| | | | | | | | | | | | | | | | | | 150 150 4
| | 125 125 3 | | | 100 100 | | | 75 75 2 | | 50 50 |
1
| | 25 25 | | | 0 | | | | || | | | | |0 | | | | | || | | | | |0 | | | | | || | | | | 0 35 70 105 140 0 35 70 105 140 0 35 70 105 140 (d) session size (e) session size (f) session size
reply traffic % String top ology recovery delay
request traffic %
| | | | | | | | | | | | | | 150 | | 200 | 6 |
| | 175 125 150 | 5 | | 100 | 125
| | | 75 100 4 | | 75 50 | | 50 3
|
| 25 25 | | | 0 | | | | || | | | | |0 | | | | | || | | | | | | |2 | | | | | || | | | | 0 35 70 105 140 0 35 70 105 140 0 35 70 105 140 (g) session size (h) session size (i) session size
reply traffic %
Tree top ology recovery delay request traffic %
β Global error recovery Group-scoped error recovery : = 33% Group-scoped error recovery : β = 50%
Hop-scoped error recovery Group-scoped error recovery : β = 100%
Figure Simulation results of of the links with uniformly distributed error rates
An Mb one like Top ology quest and reply tra c and longer recovery delay than the
hop scop ed error recovery
q1 q2 Error recovery Group-scoped Group-scoped Group-scoped mechanisms Global Hop-scoped β = 33% β = 50% β = 100% s2 r1 r2 Request scope % 113.14% 86.77% 55.92% 49.64% 39.24% r3 source Reply scope % 139.75% 97.69% 59.24% 48.82% 40.08% r6 Recovery delay 5.15 4.21 4.47 4.80 4.80 s 1 r r5 7
s r4
able Simulation results of the Mb one like top ology 3 T s4 p 1 p3
p2
Figure shows the average measured request and re
Figure An Mb one like top ology Lossy links are colored
ply scop es of individual memb ers The scop e is measured
in gray and placed at lo cal area networks
in terms of the numb er of hops that requests and replies
travel The hop scop ed error recovery p erforms in b etween
The lo cal error recovery mechanisms were also simulated the global error recovery and the group scop ed error recov
in an Mb one like top ology shown in Figure No des con ery however the improvement in the reply scop e is insignif
nected with thick lines symb olize the Mb one Other no des icant The request and reply scop es in the group scop ed
represent lo cal area networks Session memb ers are repre error recovery go down as go es up A large means
sented by black no des and one of them s is selected as higher degree of loss sharing As a consequence fewer
the data source The lossy links are represented by gray memb ers are in each lo cal group and less error recovery
lines We assume most of the losses are at lo cal area net bandwidth is wasted On the other hand large causes
works The simulation results are shown in Table In more lo cal groups b eing created than small There are
general the group scop ed error recovery generates less re lo cal groups b eing used for error recovery in the sim
| | | | | | | 40 | | 40 |
30 | 30 |
20 | 20 |
10 | 10 | | | 0 | || | |0 | | | | | | | || | | | | s1 s2 s3 s4 p1 p2 p3 q1 q2 r1 r2 r3 r4 r5 r6 r7 average 0 20 40 60 80 100 session member time %
(a) (a)
request hops request hops
| | | | | | | 40 | | 40 |
30 | 30 |
20 | 20 |
10 | 10 | | | 0 | || | |0 | | | | | | | || | | | | s1 s2 s3 s4 p1 p2 p3 q1 q2 r1 r2 r3 r4 r5 r6 r7 average 0 20 40 60 80 100 session member time % reply hops (b) reply hops (b)
Global error recovery Global error recovery Hop-scoped error recovery Hop-scoped error recovery Group-scoped error recovery : β = 33% Group-scoped error recovery : β = 33% Group-scoped error recovery : β = 50% Group-scoped error recovery : β = 50% Group-scoped error recovery : β = 100% Group-scoped error recovery : β = 100%
1% of the simulation time is roughly equal to
Average request and reply scop es of individual
Figure 6.25 simulated seconds and 25 data losses
memb ers in the Mb one like top ology
Figure Error recovery tra c dynamics in the Mb one
like top ology
ulation with lo cal groups in the simulation
with and lo cal groups in the simulation with
and reply scop es are relatively short in the group scop ed
error recovery b ecause once a memb er joined a lo cal group
In Figure a p p and p have relatively large request
its request and reply tra c is reduced The convergence
scop es when is small e g and b ecause their
time dep ends on the numb er of nested lo cal groups and
lo cal group includes remote memb ers in other lo cal area
their sequence of creation If the innermost group is created
networks q and r have relatively large request scop es in
rst the convergence is fast If the group with the largest
b oth the hop scop ed and the group scop ed error recoveries
scop e is created rst this group has to b e shrunk b efore
b ecause their requests have to propagate across the Mb one
the second nested group can b e created Only memb ers
to reach help ers On the other hand q has relatively small
without joining any lo cal groups can prop ose the creation
request scop es b ecause its requests only has to reach q
Therefore it takes longer time to converge In the hop
r r have relatively large requests scop es b ecause they
scop ed error recovery memb ers require several session cy
are involved in the scenario sketched in Figure They
cles to calculate the correct request and proxy hop counts
have to extend their request hop counts to suppress one
Generally sp eaking the convergence time of request scop e
another
is approximately two session cycles The computation of
The reply scop e shown in Figure b dep ends on the
proxy scop e relies on the results from other memb ers the
origin of the request A memb er with a small reply scop e
convergence takes more than two session cycles
means most of the incoming requests are from its lo cal area
| | | | |
| |
work a memb er with a large reply scop e means most of net 40
|
For example the incoming requests are across the Mb one 30
|
s s p p and q have relatively large reply scop es in
20
|
veries b oth the hop scop ed and the group scop ed error reco 10
|
which means their replies resp ond to requests from remote 0 | | | | | || | | | |
0 20 40 60 80 100
b ers across the Mb one On the other hand the reply mem time %
request hops
scop es of q and r are relatively small b ecause they only
Global error recovery
ver losses of their downstream memb ers
resp onsible to reco Hop-scoped error recovery
work Note that r s reply scop e within their lo cal area net β Group-scoped error recovery : = 33%
Group-scoped error recovery : β = 50%
very covers the entire top ology
in the hop scop ed error reco Group-scoped error recovery : β = 100%
the hop scop ed error recovery do es not regulate
b ecause 1% of the simulation time is roughly equal to
direction Resp onse to remote requesters happ ens
tra c 6.25 simulated seconds and 25 data losses
rarely in the group scop ed error recovery As a result the
Figure Request tra c dynamics of r r s state is reset
average reply scop e is much smaller than it is in the global
in the middle of the simulation
error recovery
Figure shows the measured request tra c and reply
To further understand the b ehavior of convergence time tra c during the simulation It is measured in terms of the
of a new memb er joining an ongoing session we manually numb er of hops The convergence p erio ds of the request
|
60 | |
reset the state of memb er r during the next simulation 50 |
|
r starts with global error recovery after its state is reset
40
30 |
It calculates its request hop count based on the incoming 20 |
|
very or learns session messages in the hop scop ed error reco 10 |
0 | || | | | | | |
existing lo cal groups in the group scop ed error re of the src h1 h2 h3 h4 h5 h6 h7 h8 h9 h10 h11 h12 h13 h14 h15 average
session member
very to restore its state The measured request tra c is co (a)
request hops
shown in Figure
| 60 | |
50 |
|
A Random Top ology with Sparsely 40
30 |
|
Memb ership distributed 20
10 | | 0 | || | | | | | | src h1 h2 h3 h4 h5 h6 h7 h8 h9 h10 h11 h12 h13 h14 h15 average session member h4 h reply hops h3 5 (b) h6 h2 Global error recovery h7 h1 Hop-scoped error recovery Group-scoped error recovery : β = 33% h Group-scoped error recovery : β = 50%
src 8 Group-scoped error recovery : β = 100%
Figure Average request and reply scop es of individual
h9
memb ers in the random top ology h15 h10
h14 ws the measured request and reply scop es h h11 Figure sho
13 h12
of individual memb ers Note that memb er h has large
Figure A top ology with rich connectivity and sparsely
request scop e in the group scop ed error recovery b ecause
distributed memb ership The lossy links are colored in gray
it shares losses with all other memb ers Its requests are
are placed around the middle of the top ology
p opulated in a lo cal group which consists of all session
memb ers
Figure shows a random top ology used in our next sim
ulation exp eriment The top ology consists of routers
Discussion
inter router links and memb ers We place lossy
links around the middle of the top ology and one lossy link
In conclusion we found that the group scop ed error recov
close to the source so memb ers have shared losses as well
ery p erforms b etter than the hop scop ed error recovery in
as indep endent losses
terms of the reply tra c The hop scop ed error recovery
p erforms b etter than the group scop ed error recovery in
of the request tra c except in the star top ology Error recovery Group-scoped Group-scoped Group-scoped terms
mechanisms Global Hop-scoped β = 33% β = 50% β = 100%
Since the size of a request message is much smaller than
Request scope % 105.86% 86.05% 40.90% 34.10% 25.00%
the size of a reply message it is more imp ortant to reduce
Reply scope % 128.36% 109.23% 49.40% 37.38% 23.88%
the reply tra c than to reduce the request tra c and so
Recovery delay 6.33 3.63 3.56 3.58 3.35
the group scop ed error recovery app ears to provide a b et
ter solution in terms of the tra c reduction However the
Table Simulation results of the random top ology with
numb er of duplicate requests increases with the numb er of
sparsely distributed memb ership
nested groups issues related to this scenario are left for
future study
The simulation results are shown in Table Since the
If we consider other sources of overhead intro duced by
degree of connectivity of routers are high the request and
these two approaches it app ears that the group scop ed er
reply delivery paths may di er from the data delivery path
ror recovery imp oses more overhead on session memb ers
In other words memb ers is more likely to nd some close
as well as the underlying multicast routing For example
siblings to recover lost packets Rememb er that we adopt a
group scop ed error recovery requires the host to send p eri
dynamic timer adjustment mechanism to optimize the er
o dic IGMP messages to refresh the multicast delivery path
ror recovery timer If requests and replies only propagate
for each lo cal group Issues related to the overhead of
to near neighb ors the recovery timers are smaller than they
multicast group management are left for future study
are in the global error recovery Therefore the recovery de
lays in b oth the hop scop ed and group scop ed error recov
eries are smaller than the recovery delay in the global error
Related Work
recovery Note that the reduction of error recovery tra c
is limited in the hop scop ed error recovery b ecause its re There have b een several other treatments of error recov
quests and replies travel more hops in the top ology with ery for reliable multicast transp ort In con
rich connectivity and sparsely distributed memb ership trast to our prop osal which assumes session memb ers are
autonomous these previous works require various degree wards a lo cal replier Instead of forwarding requests up
of static con guration centralized co ordination or router stream towards the source a router forwards a request to
supp ort wards its lo cal replier if the incoming request is not received
Hofmann prop osed a lo cal group concept from the replier link Since the replier link is a downstream
A session is split into subgroups and each subgroup com link of the router this router is at the turning p oint of the
bines memb ers in a lo cal region A subgroup is represented request delivery path Eventually the request reaches a
by a lo cal group controller which provides lo cal loss retrans replier and triggers a reply The reply is rst unicast to
mission The establishment of lo cal groups is supp orted by the turning p oint and then it is multicast downstream from
a communication service named Group Distance Service the turning p oint
A memb er searches and joins the closest lo cal group If
no suitable group exists the memb er will establish a new
lo cal group and app oint itself as the controller
Conclusion
Towsley et al examined the approach of using sep
arate multicast groups to recover individual losses in re
We prop osed two di erent approaches to reduce error re
liable multicast communication Lost packets are catego
covery tra c in SRM In the hop scop ed error recovery
rized into groups the retransmission of a lost packet is
memb ers calculate the required hop counts for their re
multicast to the group it b elongs to Receivers dynami
quests and replies based on distance information exchanged
cally join and leave those groups to recover packet losses
in session messages Since the information is piggybacked
Holbro ok et al suggested a hierarchic logging server
on their session messages the overhead imp osed by the
structure to reduce error recovery tra c in a multicast
hop scop ed error recovery is relatively small However the
session The distribution and hierarchy of logging servers
hop scop ed error recovery do es not regulate the direction
is statically con gured Receivers contact their lo cal sec
of tra c propagation If the top ology of a session is star
ondary server for retransmission instead of the remote pri
shap ed the hop scop ed error recovery do es not p erform
mary servers to avoid NAK implosion and to minimize
much b etter than the global error recovery
recovery latency and bandwidth A server either unicasts
Group scop ed error recovery b ounds the scop e of error
or multicasts a retransmission based on the numb er of re
recovery tra c by using separate multicast groups Mem
quests it receives
b ers that share the same losses join a lo cal recovery group
TMTP con gures session memb ers in the same sub
thus the error recovery tra c is only distributed within
net into domains and organizes these domains into a hi
the lo cal group Group scop ed error recovery requires in
erarchic control tree to improve the scalability of error re
dividual memb ers to maintain and manage multiple lo cal
covery Memb ers in a domain request the domain manager
groups Therefore more overhead is imp osed on memb ers
for retransmission A domain manager is also resp onsible
as well as on the underlying multicast routing
for error recovery of its children managers in the control
There remain several op en issues In the hop scop ed er
tree The scop e of retransmission is restricted by using
ror recovery maintaining a pair of request and reply hop
the TTL eld The control tree is self organized and it is
counts for individual sources do es not intro duce signi
built dynamically as domain managers join and leave the
cant overhead However maintaining multiple lo cal groups
session
for individual sources in the group scop ed error recovery
RMTP adopts a similar hierarchic structure to
may not b e acceptable Further research should investi
avoid message implosion A set of designated receivers
gate group aggregation across sources A lo cal group is
DR is selected statically in a session DRs are capa
asso ciated with one or more lossy links Sources who share
ble of retransmitting lost data The hierarchy of DRs is
the delivery path e g shared tree multicasting and the
constructed dynamically Each receiver selects its least up
lossy links along the path could b e considered the same in
stream DR as the ACK pro cessor AP and p erio dically
terms of error recovery and so error recovery from these
sends its receiving state to the AP to request retransmis
sources should b e handled by a single lo cal group
sions A retransmission is either unicast or multicast based
Another scenario that we have not fully understo o d is
on the numb er of incoming requests
the convergence time of the group scop ed error recovery
PGM makes use of the router supp ort to maintain a
in the presence of network dynamics For example if
tree hierarchy among routers When a packet is lost a re
the network top ology changes memb ers in a lo cal group
quest is unicast hop by hop upstream towards the source
may not share the same lossy links i e they do not share
Intermediate routers build retransmit states in order to re
losses anymore Another example of network dynamics is
memb er where to forward the corresp onding reply Eventu
tra c congestion Data losses due to congestion changes
ally the request will hit the source or a memb er on the re
the error rates and the lo cations of lossy links in a session
quest forwarding path who has the requested packet which
Since lo cal groups are asso ciated with lossy links changes
triggers a reply multicast hop by hop downstream towards
in error rates and lo cations of lossy links a ect the loss
the requester
sharing b ehavior within lo cal groups Memb ers have to
Papadop oulos et al elab orated further on the router
readjust themselves adaptive to these networks dynamics
forwarding mo del to lo calized the scop e of error recovery
so that the new memb ership in the lo cal group represents
tra c Each router selects a replier link which p oints to
a set of memb ers who share the same losses The study of
the convergence time of memb ership readjustment can help Dante DeLucia and Katia Obraczka A Multicast Congestion
Control Mechanism for Reliable Multicast Proceedings of IEEE
us to b etter understand the tolerance to network dynamics
ISCC
in our group scop ed scheme
Ching Gung Liu Deb orah Estrin Scott Shenker and Lixia
Zhang Timer Adjustment in SRM Technical report USC Finally one might consider combining these two
University of Southern California July
approaches by using hop scop ed request messages in lo cal
Ching Gung Liu Deb orah Estrin
groups since the hop scop ed error recovery pro duces b etter
Scott Shenker and Lixia Zhang Recovery Timer Adaptation in
SRM ftp catarina usc edu charle y pa per s timer ps request tra c reduction However requests in our hop
Ching Gung Liu Error Recovery in Scalable Reliable Multi
scop ed scheme are addressed to the global session group
cast Ph D Dissertation University of Southern California
with a sp eci c hop count and that hop count is determined
Decemb er
S Deering Host extensions for IP multicasting RFC by measuring how far is the closest upstream neighb or If
August
a hop scop ed request is sent to a lo cal group it can only
Brian Neil Levine and J J Garcia Luna Aceves A Comparison
guarantee a resp onse if the requester knows b oth how to
of Known Classes of Reliable Multicast Proto cols Proceeding s
of International Conference on Network Protocols ICNP set the hop count and how to cho ose the appropriate lo cal
Octob er
group Our hop scop ed error recovery only provides the
A Mankin A Romanow S Bradner and V Paxson IETF
former information The requester would analyze session
Criteria for Evaluating Reliable Multicast Transp ort and Appli
cation Proto cols RFC messages determine an appropriate hop count but then
Katia Obraczka Multicast Transp ort Mechanisms A Survey
the target upstream neighb or might not b e a memb er of
and Taxonomy To appear in IEEE Communications Maga
that lo cal group More research has to b e done to ensure
zine
M Hofmann T Braun and C Carle Multicast communi that either the closest upstream neighb or joins the same
cation in large scale networks Proceeding s of Third IEEE
lo cal group or the requester only considers memb ers in the
Workshop on High Performance Communication Subsystems
same lo cal group in computing its request hop count
HPCS Mystic Connecticut August
Markus Hofmann A Generic Concept for Large Scale Multi
cast Proceedings of International Zurich Seminar on Digital
Communication IZS Springer Verlag February
References
Markus Hofmann Adding Scalability to Transp ort Level Mul
ticast Proceeding s of Third COST Workshop Multime
Sally Floyd Van Jacobson Ching Gung Liu Steve Mc
dia Telecommunications and Applications Springer Verlag
Canne and Lixia Zhang A Reliable Multicast Framework for
Barcelona Spain Novemb er
Lightweight Session and Application Layer Framing Proceed
Sneha Kasera Jim Kurose and Don Towsley Scalable Reliable
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Multicast Using Multiple Multicast Groups CMPSCI Techni
Sally Floyd Van Jacobson Ching Gung Liu Steve Mc
cal Report TR Octob er
Canne and Lixia Zhang A Reliable Multicast Framework
Hugh W Holbro ok Sandeep K Singhal and David R Cheriton
for Lightweight Session and Application Layer Framing
Log Based Receiver Reliable Multicast for Distributed Interac
IEEE ACM Transactions on Networking Volume Number
tive Simulation Proceedings of ACM SIGCOMM August
D Clark and D Tennenhouse Architectural Considerations for
R Yavatkar J Gri o en and M Sudan A Reliable Dissemina
a New Generation of Proto cols Proceedings of ACM SIG
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D Clark M Lamb ert and L Zhang NETBLT A High
John C Lin and Sanjoy Paul RMTP A Reliable Multi
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cast Transp ort Proto col Proceedings of IEEE INFOCOM
COMM Pages August
Pages April
Sridhar Pingali Don Towsley and James Kurose A Compari
S Paul K K Sabnani J C Lin and S Bhattacharyya Re
son of sender initiated and Receiver Initiated Reliable Multicast
liable Multicast Transp ort Proto col RMTP To appear in
Proto cols Proceedings of ACM SIGMETRICS
IEEE Journal on Selected Areas in Communications special
J Nonnenmacher M Lacher M Jung E Biersack and G Carle
issue on Network Support for Multipoint Communication
How bad is Reliable Multicast without Lo cal Recovery Pro
Tony Sp eakman Dino Farinacci Steve Lin and Alex Tweedly
ceeding of IEEE INFOCOM March
PGM Reliable Transp ort Proto col Sp eci cation Internet
Suchitra Raman Steve McCanne and Scott Shenker Asymp
Draft draft speakman pgm spec txt January
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C Papadop oulos G Parulkar and G Varghese An Error
SIGMETRICS PERFORMANCE June
Scheme for Large Scale Multicast Application Proceedings of
Puneet Sharma Deb orah Estrin Sally Floyd and Van Jacobson
IEEE INFOCOM March
Scalable Timers for Soft State Proto cols Proceedings of the
Kannan Varadhan Deb orah Estrin and Sally Floyd Impact
IEEE INFOCOM April
of Network Dynamics on End to End Proto cols Case Studies
Puneet Sharma Deb orah Estrin Sally Floyd and Lixia Zhang
in TCP and Reliable Multicast Technical report USC
Scalable Session Messages in SRM Using Self Con guration
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ftp catarina usc edu pub puneetsh pa per s ssm ps
D Waitzman C Partridge and S Deering Distance Vector
Multicast Routing Proto col RFC Novemb er
S Deering Multicast Routing in a Datagram Internetwork
Ching Gung Liu M ACM is currently a memb er of Re PhD Thesis Stanford University Palo Alto CA Decemb er
search Sta of Fujitsu Lab oratories of America Inc Sunnyvale CA
He received his Ph D and M S from the University of Southern Cal
S Deering D Estrin D Farinacci V Jacobson C Liu and
ifornia in and and B S from National Taiwan University L Wei An Architecture for Wide Area Multicast Routing
Taiwan in His research is fo cus on multicast routing transp ort
Proceeding of ACM SIGCOMM
and application He has b een working on the design and implementa
S Deering D Estrin D Farinacci V Jacobson C Liu and L
tion of PIM Proto col Indep endent Multicast and SRM for the past Wei The PIM Architecture for Wide Area Multicast Routing
ve years
IEEE ACM Transactions on Networking Volume Number
Dante DeLucia and Katia Obraczka Multicast Feedback Sup
pression Using Representatives Proceedings of IEEE INFO
Deb orah Estrin S M SM is a Professor of Computer COM
Science at the University of Southern California in Los Angeles where
she joined the faculty in Estrin received her Ph D and
M S from the Massachusetts Institute of Technology and her
B S from U C Berkeley In Estrin received the National
Science Foundation Presidential Young Investigator Award for her
research in network interconnection and security Estrin is a Co PI
on the DARPA Virtual Internet Testb ed VINT pro ject and the
NSF Routing Arbiter pro ject at USC s Information Sciences Insti
tute where she sp ends much of her time sup ervising do ctoral student
research
Estrin is an active memb er of the IETF multicast routing related
working groups and a long time memb er of the End to End research
group Estrin is a memb er of the ACM IEEE and AAAS She has
served on numerous panels for The National Science Foundation The
National Academy of Engineering and served on DARPA s Informa
tion Systems and Technology ISAT advisory b oard from
Estrin has served as an editor for the ACM IEEE Transaction on
Networks and the Wiley Journal of Internetworking Research and
Exp erience and as a memb er of the program committee for many
IEEE INFOCOM and ACM SIGCOMM conferences and she was
program Co Chair of ACM SIGCOMM
Scott Shenker S SM is currently a Principal Scientist at
the Xerox Palo Alto Research Center He received his Sc B
from Brown University his Ph D in theoretical physics from
the University of Chicago and sp ent the academic year at
Cornell University as a Post Do ctoral Asso ciate His most recent
computer science research fo cuses on the design of integrated services
packet networks and the related issues of service mo dels scheduling
algorithms and reservation proto cols His recent economic research
addresses incentive compatibility and fairness in various cost sharing
mechanisms Besides computer networks and theoretical economics
his other research interests include chaos in nonlinear systems critical
phenomena distributed algorithms conservative garbage collection
and p erformance analysis
Lixia Zhang S M SM received the Ph D degree in
Computer Sciences from the Massachusetts Institute of Technology
Cambridge in She is an Asso ciate Professor of Computer Sci
ence at the University of California Los Angeles where she joined
the faculty in January of Prior to that she was a memb er
of Research Sta at Xerox PARC engaged in research on advanced
networking technologies including high p erformance transp ort pro
to cols reliable multicast and integrated services supp ort over the
Internet She is the Co Chair of the IETF RSVP Working Group a
memb er of the IETF Transp ort Area Directorate and was a memb er
of the Internet Architecture Board from to