Computer Science Technical Report Archive

USC Computer Science Technical Reports, no. 753 (2002)

(USC DC Other)

USC Computer Science Technical Reports, no. 753 (2002)

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content State Analysis and Aggregation Study for Multicast-based Micro Mobility
Ahmed Helmy
Electrical Engineering – Systems Department
University of Southern California, Los Angeles, CA 90089
helmy@ceng.usc.edu
Abstract- We propose intra-domain multicast-based mobility as a
solution for IP mobility. Our architecture addresses serious
problems with existing IP mobility proposals ; mainly scalability
and handoff performance. In our scheme mobility proxies are
used to allocate per-domain multicast addresses to mobiles for
use in micro mobility. State aggregation is studied as an essential
element to improve scalability of our approach. We introduce a
simple, yet very efficient aggregation algorithm, based on bit-
wise lossy aggregation. An important result obtained indicates
that state tends to be concentrated in less than 20% of the nodes
and that our scheme is extremely efficient in reducing the state in
those nodes. We show that our scheme achieves much higher
aggregation gain than conventional prefix-based aggregation.
I. I NTRODUCTION
IP mobility addresses the problem of changing network point-
of-attachment transparently during movement. Mobile
IP [4] [5] is the current IP mobility standard. However, several
studies [1] [3] [7] have shown that Mobile IP has poor
performance during handoff due to communication overhead
with the home agent. Micro-mobility techniques attempt to
improve handoff performance by either using per-domain
foreign agents [7] [26] [27] (or hierarchical approaches) or by
using complex caching and forwarding techniques between
the previous location and the new location [5] [24] [25] . In this
paper, we introduce a new multicast-based mobility scheme
for micro-mobility and show that it outperforms other micro-
mobility approaches while, at the same time, providing a
simpler solution.
In multicast-based mobility each mobile node is assigned
a multicast address to which it joins through base stations it
visits throughout its movement. Handoff is performed using
standard join/prune mechanisms. Multicast-based architecture
for inter-domain mobility suffers from serious scalability
problems concerning multicast state growth with the growth
in number of mobile nodes. The architecture also requires
ubiquitous multicast deployment and more complex security
measures. To alleviate these problems, we propose an intra-
domain multicast-based mobility solution, in which a mobile
node is assigned a multicast address within a domain that it
uses for micro mobility . The allocated multicast address is
locally scoped (i.e., unique only domain-wide). This allows
for a domain-wide address allocation scheme, in which a
group of mobility proxies allocate multicast addresses for
visiting mobiles. These addresses are locally- scoped and are
used temporarily by the mobiles for micro mobility while
moving within the domain. Mobile proxies perform inter-
domain mobility on behalf of visiting mobiles, then
multicast- tunnel the packets to the mobile. The multicast
address of a mobile does not change while moving in - domain.
Since the multicast addresses are locally- scoped and the
joins go through the mobility proxy, the multicast address
allocation scheme is performed per-domain (as opposed to
requiring an inter-domain architecture). Also, this provides
potential for multicast state aggregation opportunities. As an
main contribution of this paper we thoroughly study and
evaluate various multicast aggregation techniques. Our
analysis shows that our new bit-wise lossy aggregation
achieves aggregation gain much higher than the traditional
prefix-based aggregation schemes. We observe that multicast
state distribution in our case is non-uniform among network
nodes, and that our scheme achieves substantial state
reduction for nodes with high state concentration. This study
is the first to address state aggregation for IP mobility and
one of the very few to address multicast state aggregation.
The rest of this paper is organized as follows. Section II
gives an overview of multicast-based mobility, its promise
and its problems. Section III presents our inter-domain
multicast-based architecture for micro-mobility. Section IV
discusses state aggregation, while Section V presents
simulation results and analysis. Related work is discussed in
Section VI and Section VII concludes.
II. Multicast-based Mobility
In multicast-based mobility, each mobile node (MN) is
assigned a multicast address. The MN, throughout its
movement, would join this multicast address through the
locations it visits. Nodes wishing to send to the MN send
their packets to a multicast address, instead of sending their
packets to a unicast address. Because the movement will be
to a geographical vicinity, it is highly likely that the join from
the new location (to which the mobile has recently moved)
will traverse a small number of hops to reach the already-
established multicast distribution tree. Hence, performance
during handoff will be improved drastically. An overview of
this architecture is given in Figure 1 . As the MN moves, it
joins to the assigned multicast address through the new base
station. Once the MN starts receiving packets through the
new location, it sends a prune message to the old base station
to stop the flow of the packets down that path. Thus
completing the smooth handoff process.
Handoff performance is function of the number of links
traversed by control messages to bring data packets to the
new location. As shown in Figure 2 , in mobile IP (MIP) [4] , registration request is sent to the HA; i.e., traverses path ‘B’ , whereas in MIPv6 with route optimization [5] , the binding
updates are sent to the CN; i.e., path ‘C’ . Another class of
protocols uses what is called seamless handoff [24] [25] , in
which packets to the new location are forwarded by the
previous location traversing ‘P’ links, as in Figure 2 (b). In
our multicast-based mobility approach join messages need to
reach the multicast tree traversing ‘L’ links. Besides being the
simplest of the above approaches, our approach achieves the
best handoff performance where average B/L and C/L are
between 2.3 and 4.1, and average P/L is above 1.25
1 . CN
Join
Prune
CN
CN
(a) ( b) (c)
Figure 1 . Multicast-based mobility. As the MN moves, as in (b) and (c), the
MN joins the distribution tree through the new location and prunes through
the old location.
Home Agent (HA)
Correspondent
Node (CN)
Mobile Node (MN)
A B C
2 CN
1 3 (a) ( b)
Figure 2 . (a) Triangle Routing . (b) As the MN moves from node 1 to 2,
added links ‘ L’ is 3 and links to previous location ‘ P ’ (dashed lines) is 2. As
it moves from 2 to 3, L =0 , P =2.
In spite of such promise, many compelling issues need to
be properly addressed to realize multicast-based mobility in
today’s Internet. These issues raise major concerns about the
practicality and applicability of multicast-based mobility,
including scalability of multicast state, multicast address
allocation, requiring ubiquitous deployment of multicast, and
security overhead during handoff. We discuss these issues
and present an architecture offering a common solution to
alleviate and hopefully eliminate these problems.
Scalability of Multicast State. Each mobile node is assigned a
multicast address to which it joins throughout its movement.
The state created in the routers en-route from the MN to the
sender is source-group ( S ,G ) specific state. With the growth
in number of mobile nodes, and subsequently, number of
groups ( G ), the number of states kept in the router increases.
In general, if there are ‘ x’ MNs, each communicating with ‘ y’
senders on average, with average path length of ‘ l ’ then
routers in the network should create ‘ x.y.l’ ( S ,G) states. This
does not scale.
Multicast Address Allocation. The problem of multicast
address allocation is a research problem in the Internet

1 This was obtained through extensive simulations not shown here for
brevity. For more detail see [1] . This is not the focus of this paper, however.
community [10] . This problem is exasperated by requiring
each MN to have a globally-unique multicast address. Aside
from the fact that the multicast address space is restricted for
IPv4, using a global multicast address for each MN may be
wasteful and requiring uniqueness may not be practical.
Ubiquitous Multicast Deployment. In order to implement
inter-domain multicast-based mobility, inter-domain
multicast routing needs to be in place. Unfortunately, this
requirement restricts the applicability of our inter-domain
mobility architecture.
Security Overhead. Security is critical for mobility support,
where the continuous movement and change of attachment
point is part of the normal operation. Such setting is prone to
remote redirection attacks, where a malicious node redirects
to itself packets that were originally destined to the mobile
node. In general, authentication should be used with any
message revealing information about the mobile node. The
problem is even more complex with multicast, where any
node may join the multicast address as per the IP-multicast
host model. These security measures are complex and may
incur a lot of overhead. If such measures are invoked with
every handoff, however, it may overshadow the benefits of
efficient handoff mechanisms . To alleviate these problems, we propose an intra-domain
multicast-based mobility solution.
III. Intra-domain Architectural Overview
In our intra-domain architecture, a mobile node is
assigned a multicast address to which it joins while moving.
The multicast address, however, is assigned only within a
domain (e.g., autonomous system) and is used for intra-
domain micro mobility. While moving between domains, an
inter-domain mobility protocol is invoked (e.g., Mobile IP).
We do not assume a specific protocol for inter-domain, only
that such a protocol exists. For the sake of illustration, we
take MIP as an example, when needed.
2 . 3.b
M P MN
BS
1 3.a
1) M obile contacts bast station (BS)
2) BS sends request to mob ility
proxy ( M P)
3.a) M P performs inter-do ma in
mob ility handoff
3.b) M P sends reply to BS with the
assigned multicast address
Figure 3 . Sequence of actions as the mobile node moves into a domain.
When a mobile node moves into a new domain, it
contacts the entry point base station (the first base station it
encounters). This entry point base station (BS) performs the
necessary per-domain authentication and security measures,
then assigns a unicast care-of-address ( CoA) for the mobile
node to use in that subnet. As shown in Figure 3 , the BS then
sends a request message to the mobility proxy (MP) to obtain
a multicast address for the visiting MN. The request message
includes the home address of the mobile node and its home
agent’s address. Upon receiving the request the MP performs
two tasks. The first is to execute the inter-domain handoff on
behalf of the MN. In the case of Mobile IP, for example, this
means that the MP registers its own address with the MN’s
home agent. The second task is for the MP to assign a
multicast address for the visiting MN, send a reply message
to the BS and keep record of this mapping. The mapping is
used for packet encapsulation later on.
Once this step is complete, the visiting MN joins the
assigned multicast address ( G ). The joins are sent to ( MP ,G ) and are processed as per the underlying multicast routing
2 . The MN continues to move within the same domain using the
same multicast address. The assigned multicast address is
locally scoped to the domain. Handoff is performed using
standard join/prune mechanisms and only lightweight intra-
domain security is required in this case.
When packets are sent to the MN, they are forwarded to
the MP using inter-domain mobility. The packets are then
encapsulated by the MP, based on the mapping, and multicast
to the MN. For example, in Mobile IP the home agent
encapsulates the packets and sends them to the MP. The MP
looks into the inner header to know the home address of the
destination, performs the mapping, strips off the outer header
and encapsulates the inner packet with multicast header. The
packets flow from the MP down the multicast tree to the MN.
Architectural Discussion
We would like to point out how our scheme addresses
design issues previously mentioned. In terms of scalability,
our scheme attempts to address the limitations of the inter-
domain multicast-based mobility. In terms of multicast state
scalability we note that the multicast state growth is O( G ) for
the architecture presented in this study, as opposed to O( S x G ) in [1] [2] . However, there is still some concern for state
concentration on certain paths (i.e., in certain routers) in the
network. To further improve scalability of multicast state we
investigate several aggregation techniques in the next section.
We believe this is quite essential to achieve a scalable
solution. Address allocation is performed by the mobility
proxies on a per-domain basis, the multicast address
assignment is now a local mechanism, and the multicast
addresses are locally scoped within the domain. This
facilitates address allocation and provides per-domain privacy
as the multicast packets are not forwarded out of the domain.
With regards to incremental multicast deployment, our
architecture allows for incremental deployment of multicast,
based on per-domain approach. This way, the best handoff
performance can be attained using our architecture without
requiring inter-domain multicast. Security overhead during
handoff is reduced by using lightweight intra -domain security
mechanisms while moving within a domain.

2 This is not a source-group state. Rather, it is for all sources sending to the
MN ( G) . This is similar in concept to the ( * ,G ) tree established towards the
Rendezvous Point (RP) in PIM-SM [9] , but can be achieved using any
multicast routing protocol.
Robustness is crucial to ensure proper operation in the
face of crashes and failures. To avoid single-point-of-failure
scenarios (especially for the mobility proxy) we provide
several mechanisms to enhance our protocol robustness.
Instead of having only one mobility proxy (MP) per-domain,
we propose to have multiple MPs (typically, five to ten per-
domain). These MPs are typically placed/configured at the
border of the domain or at the center of the network
3 . Each
MP sends periodic liveness messages to a well-known
domain-specific group called MP-announcement-group . All
base station routers join this group and receive the liveness
messages. Each such router maintains a live-MP list and
maintains a timer for each MP that is reset by the liveness
message from that MP. When a base station router is first
contacted by a visiting MN, it performs a hash procedure to
select one of the MPs from the MP-list. We use a hash
procedure to avoid distributing explicit mapping (an approach
that does not scale). The hash procedure assigns a weight to
each MP i using hash( MNaddress, MP i ), then selects the
highest weight MP to which it sends the request message.
This scheme has two advantages. First, it distributes the
visiting MNs equally over the MP-list. Second, if a MP fails
only those MNs that hashed to it are re-hashed, other MNs
are not affected. See [21] for more detail. Moreover, if a new
MP is added to the pool of MPs (i.e., the change in the list
was not caused by failure) no re-hashing is done. Failure of a MP is detected by the base station routers when the MP timer
expires. If the router uses the failed MP for some of its MNs,
it does re-hashing for those MNs to select a live MP
4 . IV . State Aggregation
The main problem with multicast-based mobility is
scalability of multicast state with the increase in number of
visiting mobile nodes. This is especially a problem where
state concentration is expected to occur, as in the mobility
proxies. Hence, it is quite crucial to use an effective multicast
state aggregation technique to alleviate such a problem.
Most previous work on state aggregation uses prefix
aggregation ( PxA) . That is, two states can be aggregated only
if they have the same address prefix. For example, the two
addresses 128.125.50.2 and 128.125.50.3 can be aggregated
as one entry as 128.125.50.2/31, where 31 is the mask length.
This has proven to be efficient for aggregating unicast routing
tables in the Internet, since a domain/ subnet has a specific
unicast prefix. It is not clear, however, if this benefit applies
for multicast addresses that are not geographically significant.

3 The center(s) of the network are the nodes with min( max) distance to reach
any node in the network [16] . 4 The MN keeps the multicast and MP addresses. In case of MP failure
during handoff, the new BS gets the multicast and MP addresses from the
MN, and checks if the MP is alive. If not, the BS performs the hashing and
obtains a new live MP to which it sends a request. This mechanism obviates
the need for mapping state replication among MPs. If the MN crashes during
handoff (we assume it is configured with its home address), then the new BS
performs the hashing, gets the MP address and sends a request to the MP.
We propose another kind of aggregation called the bit-
wise aggregation (BA) . As the name suggests BA works with
bits instead of prefixes. For example, 128.125.0.2 and
128.125.1.2 may be aggregated as 128.12.0.2\9, where 9 is
the position of the aggregated bit. Intuitively, BA provides
more opportunity for aggregation , hence we expect it, on
average, to provide better aggregation.
However, a deeper look at the two schemes shows us
scenarios where PxA leads to more aggregation than BA. For
example, a sequence of {0,4,1,2,3} leads to 3 states with BA,
whereas with PxA it leads to 2 states. We perform further
analysis to understand behavior of these schemes. We define
aggregation ratio (AR) as the number of states before
aggregation ( x) to the number of states after aggregation ( y );
i.e. AR=x/y . AR provides a good measure of the state
reduction due to aggregation. In the above example, AR for
BA is 5/3 whereas AR for PxA is 5/2. Figure 4 shows the AR
for in-order numbers, where both schemes have identical AR.
1 10
100
1000
0 100 200 300 400 500 600 700 800 900
Number of Mobile Nodes (MNs)
Aggregation Ratio Figure 4 . Aggregation ratio for in-sequence numbers. Identical gain for bit-
wise and prefix aggregation.
1 10
100
0 100 200 300 400 500 600 700 800 900
Number of MNs
Aggregation Ratio Bitwise
Prefix
Figure 5 . Aggregation ratio for random numbers. Bit-wise aggregation
outperforms prefix aggregation up to 80% of the number population ..
Figure 5 shows the AR when the numbers are random. That
is, out of 0 to 999, distinct numbers are chosen randomly
until the whole number population is covered
5 . The random
arrival of addresses is a more likely scenario, since MNs
arrive at different entry points and experience various
movement patterns. The following table presents the results:

5 We obtained s imilar results with several other number populations . Av. prefix Av. bit-wise Av. bit-wise/prefix
80% population 1.40 1.84 1.32
100% population 2.48 1.98 1.19
N ote the interesting cross-over-point at 80% population . The overall average AR for PxA is ‘2.48’ and for BA is
‘1.98’. Up to 80% of the population, however, BA
outperforms PxA by a factor of 1.32. Hence, we choose bit-
wise over prefix aggregation for our scheme.
We further classify multicast aggregation as perfect (PA)
or lossy aggregation (LA) . A multicast state consists of { Src,
Grp, iif, oifList}, where iif is the incoming interface and oif is
the outgoing interface. Src is t he source of the multicast (the
MP) and iif points towards the MP. In PA, groups can only be
aggregated if the oifList if the same. For LA, however, states
are aggregated even though the interfaces may be different.
LA achieves better aggregation at the expense of extra
network overhead, as the data packets may be sent down an
extra link that does not reach a receiver. We study lossy bit-
wise (LBA) and perfect bit-wise aggregation (PBA).
V. Simulation and Analysis
The first step to solve the scalability problem of
multicast state is to understand the state distribution in the
routers. We then apply aggregation and analyze the state
reduction obtained under the different aggregation
techniques. Aggregation gain, in general, depends on several
factors, including topology, MP placement, number of MNs,
among others. We study and evaluate t his problem across
different dimensions of various network sizes and number of
mobile nodes and mobility proxies.
A. Simulation Setup
We use the network simulator (NS-2) [15] for simulation.
Two sets of simulation scenarios were investigated. In the
first set, called dynamic scenarios , 1000 MNs randomly enter
the domain, and move to random nodes within the domain,
each time joining through the new location and pruning
through the old location, thus capturing the dynamics of the
multicast tree. Up to 250k moves were simulated. In the
second set of scenarios, called snapshot scenarios, MNs enter
the domain at random entry nodes and at random times, but
they do not move. Thus simulating a snapshot of the domain
where nodes may exist at random locations. This approach
allows us to scale our simulations to up to 250k MNs. In both
simulation scenarios, we use up to 4 mobility proxies placed
at well-connected backbone nodes. With every move or new
entry, the MN randomly establishes new connections to the
proxies, and maintains a number of already-existing
connections. We have simulated several topologies likely to
represent intra-domain networks (see Table 1 ).
name nodes links av deg name nodes links av deg
ARPA 47 68 2.89 TS-200 200 372 3.72
TS-100 100 185 3.7 TS-250 250 463 3.72
TS-150 150 276 3.71 TS-300 300 559 3.73
Table 1 Simulation Topologies. TS: transit stub, ARPA : arpanet based on real data.
B. Analysis and Results
We first discuss analysis of a topology with 100 nodes
and 1 MP. This illustrates our analysis method to understand
state distribution and aggregation gains. Then we present
results for various topologies and multiple MPs.
i) 100 Nodes with 1 MP: The first topology used for the
simulation is that given in Figure 6 , with 100 nodes, transit-
stub structure, and one mobility proxy (MP) placed at node 0.
Figure 6 . 100 node transit-stub topology (TS-100)
For dynamic scenarios, Figure 7 shows the multicast
state distribution across the nodes for 40k moves
6 . We notice
that much of the multicast state in the network is concentrated
at the backbone nodes 0,1,2 and 3. In general, we have
noticed that only 17-20% of the nodes hold more than the
average number of states. Also, 40-60% hold less than 1% of
the total number of MNs and 66-71% hold less than 2%. That
is, we observed a very high concentration of states in only a
small fraction of the nodes . 1 10
100
1000
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
Node ID
State Time Figure 7 . State distribution without aggregation

6 We only show the first 50 nodes and start the graph at 250MNs for clarity.
1 10
100
1000
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
Node ID
States Time Figure 8 . State distribution with lossy aggregation.
We conducted a similar simulation experiment with lossy
aggregation. The state distribution across the nodes is shown
in Figure 8 . It is clear from the two previous graphs that the
nodes where aggregation is most effective are those nodes
with maximum state; e.g., nodes 0,1 and 2. We take a closer
look at those nodes in Figure 9 . The number of states at node
0 dropped below 10 states. Notable reduction in state was
also observed for nodes 1 and 2. The average AR for the 20%
of nodes with maximum state was 10.07 ( i.e, 90% reduction).
The overall number of states over the 100 nodes is given
in Figure 10 . As shown, lossy aggregation obtains good state
reduction (factor of 2, or 50% reduction, for average number
of states and around 1.5 for 90
th percentile)
7 . Also, we noticed
a significant decrease in variance of states across the nodes.
1 10
100
1000
Node 0 w/ agg Node 1 w/ agg Node 2 w/ agg
Node 0 w/o agg Node 1 w/o agg Node 2 w/o agg
Time
States Figure 9 . Number of states (w/o agg: without aggregation, w/ agg: with aggregation)
0 10
20
30
40
50
60
70
80
90
Time
Number of States 90th w/o agg
90th w/ agg
Avg. w/o agg
Avg. w/ agg
Figure 10 . Overall average and 90
th
percentile.

7 Without aggregation, in case of random movement, the average number of
states= MNs.PL/Nodes, where MNs is the number of mobile nodes (1000), PL
is the average path length in the topology (4 in our case), and Nodes is the
number of nodes in the topology (100). I.e., average number of states is 40.
For snapshot scenarios , with 250k MNs, the state
distribution across time is given in Figure 11 (data is shown
for 50 nodes and starts from 10k MNs, for clarity). Again, we
see concentration of the state at nodes 0 through 3. We also
observe surges in other nodes (the darker areas of the graph).
A closer look at the state distribution at the end of
simulation (i.e., the last snapshot) is given in Figure 12 . The
average state per node is 10,830 states
8 . However, only 20%
of the nodes had 10k or more states, and around 60% of the
nodes have around 2500 states (i.e., 1% of the total number of
MNs). This is consistent with our earlier findings and is a
strong indication that the state distribution is skewed, with
potential for efficient aggregation in nodes with large number
of states, where state reduction is mostly needed.
0 4 8 12
16
20
24
28
32
36
40
44
48
1E+1
1E+2
1E+3
1E+4
1E+5
1E+6
State Node ID
Time
Figure 11 . Distribution of state across nodes and time, for 250k MNs . 1E+1
1E+2
1E+3
1E+4
1E+5
1E+6
0 10 20 30 40 50 60 70 80 90
Node ID
States Figure 12 . Number of states indexed by the node ID after 250k MNs.
To further understand the aggregation performance, we
apply both lossy and perfect aggregation techniques to the
snapshot scenarios (up to 40k MNs). For both techniques, we
measure the average AR, 90
th percentile and maximum state
ratios
9 . As shown, these ratios increase with the increase of
number of MNs. Also, it is clear that the lossy aggregation
achieves better ratios than perfect aggregation. For lossy
aggregation the average AR approaches 2 for large number of
MNs, whereas for perfect aggregation AR approaches 1.4.

8 Theoretically the average is 250k x 4 hops/100nodes = 10k states.
9 Max state ratio=Max State Before Aggregation/Max State After Aggregation , and
similarly for the 90
th
percentile ratio.
1 1.5
2 2.5
3 3.5
4 4.5
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
Avg. Perfect 90th Perfect Max Perfect
Avg. Lossy 90th Lossy Max Lossy
Mobile Nodes (1000s)
Aggregation Ratio Figure 13 . Aggregation ratios for lossy and perfect aggregation techniques.
ii) Various Topologies with Multiple MPs: We now
investigate lossy and perfect aggregation techniques over
several topologies. We also analyze aggregation trends with
multiple mobility proxies. We simulated snapshot scenarios
with 10k MNs. AR results for lossy aggregation are shown in
Figure 14 , and are summarized in the following table:
MPs/Nodes 50 100 150 200 250 300
1 1.99 1.84 1.71 1.71 1.64 1.63
2 1.48 1.40 1.36 1.36 1.36 1.32
3 1.38 1.33 1.31 1.31 1.29 1.28
4 1.33 1.29 1.27 1.27 1.26 1.25
As shown, the average AR per node ranges from 1.25
(for 300 nodes 4 MPs) to 1.99 (for 50 nodes with 1 MP). We
note several important trends; for the same number of MNs,
as the number of nodes in the topology increases, the state
concentration in the nodes decreases and the AR decreases.
Also, as the number of MPs increases, the concentration of
states in the nodes decreases and the AR decreases.
1 1.2
1.4
1.6
1.8
2 1 2 3 4 50 100 150 200 250 300
Number of Nodes
MPs
Aggregation Ratio Figure 14 . Aggregation ratio for lossy aggregation with various topologies
and multiple MPs
Simulation results for the perfect aggregation are given
in Figure 15 , and are summarized in the following table:
MPs/Nodes 50 100 150 200 250 300
1 1.43 1.32 1.25 1.27 1.23 1.23
2 1.27 1.18 1.16 1.17 1.15 1.16
3 1.21 1.17 1.15 1.16 1.15 1.15
4 1.19 1.15 1.14 1.14 1.14 1.14
The average aggregation ratio ranges from 1.14 (for 300
nodes with 4 MPs) to 1.43 (for 50 nodes with 1 MP).
Evidently, lossy aggregation achieves better AR. The trends
for both aggregation techniques are quite similar.
1 1.1
1.2
1.3
1.4
1.5
1 2 3 4 50 100 150 200 250 300
Number of Nodes
Aggregation Ratio MPs
Figure 15 . Aggregation ratio for perfect aggregation with various topologies
and multiple MPs . VI. RELATED WORK
Several architectures have been proposed to provide IP
mobility support. In Mobile IP (MIP) [4] , every mobile node
(MN) is assigned a home address and home agent (HA) in its
home subnet. When the MN moves to another foreign subnet,
it acquires a care-of-address (COA) through a foreign agent
(FA). The MN informs the HA of its COA via registration . Packets destined to the MN are sent to the HA, then are
tunneled to the MN. This is known as triangle routing . Route optimization [6] attempts to avoid triangle routing by sending
binding updates , containing the current COA of the MN to
the sender. Overhead during handoff, however, renders this
scheme unsuitable for micro mobility. In [8] a scheme based
on dynamic DNS updates is proposed. When MN moves, it
obtains a new IP-address and updates the DNS mapping for
its host name . This incurs handoff latency due to DNS update
delays and is not suitable for delay bounded applications.
Also, this scheme is not transparent to the transport protocol
that is aware of the mobility. In [3] the HA tunnels packets
using a pre-arranged multicast group address. The access
router, to which the MN is currently connected, joins the
group to get data packets over the multicast tree. This
approach suffers from the triangle routing problem; packets
are sent to HA first and then to MN. Multicast-based mobility
is proposed in [1] and [2] . Each MN is assigned a unique
multicast address. Packets sent to the MN are destined to that
multicast address and flow down the multicast distribution
tree to the MN. The sender tunnels the packets using the
multicast address. This approach avoids triangle routing, in
addition to reducing handoff latency and packet loss. The
study in [1] quantifies the superiority of handoff performance
for multicast-based mobility over Mobile IP protocols. These
schemes, however, suffer from several serious practical
issues, including scalability of multicast state, address
allocation and dependency on inter-domain multicast. We
address these issues in our work.
Several approaches have been proposed for micro
mobility [23] . The general approaches include mobile-specific
routing, hierarchical approaches and seamless handoff.
Mobile-specific route approaches include cellular IP [17] and
Hawaii [18] . A domain-gateway registers its address with the
HA and forwards the packets to the MN. The MN’s home
address is used within the domain. These approaches need
special signaling to update mobile-specific routes and require
changes in packet forwarding and unicast routing in all the
routers. In cellular IP [17] , signaling is data-triggered to create
paths by having routers snoop on the data packets.
Hawaii [18] proposes a separate routing protocol and requires
explicit signaling from the mobiles. In a way, these
approaches attempt to create a distribution tree using extra
routing entries for the mobile, similar to what multicast does.
Our approach builds upon existing multicast mechanisms as
opposed to re-creating them. Approaches based on seamless
handoff between old and new access routers, involve fairly
complex signaling, buffering and synchronization procedures.
Router-assisted smooth handoff in MIP [5] , edge mobility [24]
and fast handoff [25] belong to this category. Approaches
using a hierarchy employ a gateway per-domain and need to
keep a location database to map identifiers into locations.
This mapping suffers from scalability and robustness
problems as was noted earlier in this paper. In [7] a hierarchy
of foreign agents is created at the local, administrative
domain and global levels. In [26] a multi-level hierarchy is
used in which packets from the HA arrive at a root FA where
they are tunneled to a lower level FA and then to the MN.
Hierarchical MIP [27] builds a network of tunnels (overlay
network) between FAs. [29] [30] also use a notion of mobility
agent for localized handoff within a domain. During handoff,
MN contacts the domain FA. Our approach clearly
outperforms hierarchical approaches in handoff delay, and is
simpler as it re-uses existing standard multicast mechanisms.
Very little work has been done in the area of multicast
state aggregation. Work in [19] proposes an interface-centric
model for aggregation. This approach, however, benefits
from having a large number of group members, which does
not apply in our case. [29] studies strict, pseudo-strict and
lossy prefix aggregations for wide-area multicast routing.
Unlike most previous studies, however, we show that the bit-
wise usually achieves better gains than prefix aggregation.
VII. CONCLUSIONS
In this paper, we presented a new intra-domain multicast-
based protocol for supporting micro mobility. Our scheme
uses mobility proxies to assign domain- scoped multicast
addresses to visiting mobiles. A mobile uses its assigned
address during its movement throughout the domain. Our
study shows that such scheme clearly outperforms other IP
mobility approaches in handoff delays . In our architecture we
address serious drawbacks of inter-domain multicast-based
mobility approaches. Particularly, we addresses issues of
multicast state scalability , multicast address allocation,
incremental multicast deployment and overhead of security
during handoff.
We feel, however, that the main contribution of our
paper is the work on multicast state aggregation . Unlike
previous work, our extensive simulations and thorough
analysis shows that, for multicast aggregation, bit-wise
aggregation is a better choice than prefix aggregation.
Furthermore, we observe that multicast state tends to be
distributed unevenly across the nodes in the topology. For one
mobility proxy, for example, 20% or less of the nodes had
more than the average state per node, and up to 60% of the
nodes had states/entries less than 1% of the number of MNs.
Such state concentration facilitates efficient aggregation.
We have shown through extensive simulation over
various topologies and multiple mobility proxies that bit-wise
lossy aggregation obtains the best aggregation gains. Average
aggregation ratios between 1.25 and 2 were obtained in our
simulations. This translates into 20% to 50% reduction in
multicast state. The average ratio goes up to 10 (i.e., 90%
reduction) for the top 20% nodes in state concentration.
Our findings indicate that the aggregation ratio increases
with the increase in number of visiting mobile nodes, the
decrease in number of mobility proxies, and the decrease in
number of nodes in the topology.
Our work is the first to address state aggregation in IP
mobility, and one of the very few to address multicast state
aggregation. We hope that the understanding developed in
this paper will help design more scalable efficient solutions
for IP mobility.
REFERENCES
[1] A. Helmy, "A Multicast-based Protocol for IP Mobility Support", ACM
Second International Workshop on Networked Group Communication
(NGC), Palo Alto, November 2000.
[2] J. Mysore, V. Bharghavan, “A New Multicasting-based Architecture
for Internet Host Mobility”, Proceedings of ACM MobiCom , 1997.
[3] S. Seshan, H. Balakrishnan, R. Katz, “Handoffs in Cellular Wireless
Networks: The Daedalus Implementation and Experience”, Kluwer
Journal on Wireless Networks, 1995.
[4] C. Perkins, “IP Mobility Support” , IETF RFC 2002, Oct 96.
[5] C. Perkins and D. Johnson, “Mobility Support in IPv6” , Proceedings of
MobiCom’96 , November 1996.
[6] C. Perkins, D. Johnson, “Route Optimization in Mobile IP”, Internet
Draft, IETF, February 2000.
[7] R. Caceres, V. Padmanabhan, “Fast and Scalable Handoffs for Wireless
Internetworks” MobiCom ’96.
[8] A. Snoeren, H. Balakrishnan, “An End-to-End Approach to Host
Mobility”, ACM MobiCom, Aug 00.
[9] D. Estrin, D. Farinacci, A. Helmy, D. Thaler, S. Deering, V. Jacobson,
M. Handley, C. Liu, P. Sharma, “Protocol Independent Multicast –
Sparse Mode: Protocol Specification”, IETF RFC 2362, March '98.
[10] S. Kumar, P. Radoslavov, D. Thaler, C. Alaettinoglu, D. Estrin, M.
Handley, “The MASC/BGMP Architecture for Inter-domain Multicast
Routing”, Proceedings of ACM SIGCOMM, August 1998.
[11] B. Cain, S. Deering, I. Kouvelas, A. Thyagarajan, “Internet Group
Management Protocol, version3”, Internet draft of the IETF , March
2000.
[12] K. Calvert, M. Doar, E. Zegura, “Modeling Internet Topology”, IEEE
Comm, p. 160-163, June 1997.
[13] G. Phillips, S. Shenker, H. Tangmunarunkit, “Scaling of Multicast
Trees: Comments on the Chuang-Sirbu scaling law”, SIGCOMM ’99.
[14] P. Radoslavov, H. Tangmunarunkit, H. Yu, R. Govindan, S. Shenker,
D. Estrin, "On characterizing network topologies and analyzing their
impact on protocol design", USC-CS-TR-00-731 , March 2000.
[15] L. Breslau, D. Estrin, K. Fall, S. Floyd, J. Heidemann, A. Helmy, P.
Huang, S. McCanne, K. Varadhan, Y. Xu, H. Yu, “Advances in
Network Simulation”, IEEE Computer, vol. 33(5), p. 59-67, May 2000.
[16] E. Fleury, Yih Huang, P.K. McKinley. “On the performance and
feasibility of multicast core selection heuristics”, Int’l Conference on
Computer Communications and Networks, Page(s): 296 –303, 1998.
[17] A. Campbell, J. Gomez, S. Kim, A. Valko, C. Wan, Z. Turanyi,
“Design, implementation, and evaluation of cellular IP” IEEE Personal
Communications , Volume: 7 Issue: 4 , Page(s): 42 –49, Aug. 2000.
[18] R. Ramjee , T. La Porta, L. Salgarelli, S. Thuel, K. Varadhan, L. Li,
“IP-based access network infrastructure for next-generation wireless
data networks”, IEEE Personal Communications , Volume: 7 Issue: 4 ,
Page(s): 34 –41, Aug. 2000.
[19] D. Thaler, M. Handley, “On the aggregatability of multicast forwarding
state”, IEEE INFOCOM 2000, Page(s): 1654 -1663 vol.3, 2000.
[20] P. Radoslavov, D. Estrin, R. Govindan, “Exploiting the Bandwidth-
Memory Tradeoff in Multicast State Aggregation", USC-CS-TR99-
697, Computer Science Department, USC, July 1999.
[21] D. Estrin, M. Handley, A. Helmy, P. Huang, D. Thaler, "A Dynamic
Bootstrap Mechanism for Rendezvous-based Multicast Routing",
Proceedings of IEEE INFOCOM '99, New York, March 1999.
[22] D. Zappala, “Alternate path routing for multicast”, INFOCOM 2000
[23] A. T. Campbell and J. Gomez IP Micro-Mobility Protocols, ACM
SIGMOBILE Mobile Computer and Communication Review, 2001
[24] A. O’Neill, G. Tsirtsis, S. Corson, Edge Mobility Architecture,
Internet-draft, draft-oneill-ema-02.txt, July 2000.
[25] G. Dommety, et al, “Fast Handovers for Mobile IPv6”, Internet Draft,
draft-ietf-mobileip-fast-mipv6-02.txt, July 2001.
[26] E. Gustafsson, A. Jonsson, C. Perkins, Mobile IP Regional
Registration, draft-ietf-mobileip-reg-tunnel-02, March 2000.
[27] H. Soliman, C. Castelluccia, K. Elmalki, L. Bellier, “Hierarchical
Mobile IPv6”, draft-ietf-mobileip-hmipv6-04.txt, July 2001.
[28] P. Radoslavov, D. Estrin, R. Govindan, “Exploiting the Bandwidth-
Memory Tradeoff in Multicast State Aggregation" ,USC-CSTR, July99.
[29] J. Kempf, P. Calhoun, C. Pairla, Foreign Agent Assisted Handover,
draft-calhoun-mobileip-proactive-fa-01.txt, June 00.
[30] G. Dommety, “Local and Indirect Registration for Anchoring
Handoffs”,draft-dommety-mobileip-anchor-handoff-01, Dec 00

Abstract (if available)

Linked assets

Computer Science Technical Report Archive

Conceptually similar

PDF

USC Computer Science Technical Reports, no. 734 (2000)

PDF

USC Computer Science Technical Reports, no. 747 (2001)

PDF

USC Computer Science Technical Reports, no. 769 (2002)

PDF

USC Computer Science Technical Reports, no. 674 (1998)

PDF

USC Computer Science Technical Reports, no. 816 (2004)

PDF

USC Computer Science Technical Reports, no. 757 (2002)

PDF

USC Computer Science Technical Reports, no. 743 (2001)

PDF

USC Computer Science Technical Reports, no. 749 (2001)

PDF

USC Computer Science Technical Reports, no. 763 (2002)

PDF

USC Computer Science Technical Reports, no. 673 (1998)

PDF

USC Computer Science Technical Reports, no. 663 (1998)

PDF

USC Computer Science Technical Reports, no. 727 (2000)

PDF

USC Computer Science Technical Reports, no. 803 (2003)

PDF

USC Computer Science Technical Reports, no. 690 (1998)

PDF

USC Computer Science Technical Reports, no. 752 (2002)

PDF

USC Computer Science Technical Reports, no. 649 (1997)

PDF

USC Computer Science Technical Reports, no. 644 (1997)

PDF

USC Computer Science Technical Reports, no. 755 (2002)

PDF

USC Computer Science Technical Reports, no. 883 (2006)

PDF

USC Computer Science Technical Reports, no. 772 (2002)

Description

Ahmed Helmy. "State analysis and aggregation study for multicast-based micro mobility." Computer Science Technical Reports (Los Angeles, California, USA: University of Southern California. Department of Computer Science) no. 753 (2002).

Asset Metadata

Creator Helmy, Ahmed (author)

Core Title USC Computer Science Technical Reports, no. 753 (2002)

Alternative Title State analysis and aggregation study for multicast-based micro mobility (title)

Publisher Department of Computer Science,USC Viterbi School of Engineering, University of Southern California, 3650 McClintock Avenue, Los Angeles, California, 90089, USA (publisher)

Tag OAI-PMH Harvest

Format 8 pages (extent), technical reports (aat)

Language English

Unique identifier UC16269726

Identifier 02-753 State Analysis and Aggregation Study for Multicast-based Micro Mobility (filename)

Legacy Identifier usc-cstr-02-753

Format 8 pages (extent),technical reports (aat)

Rights Department of Computer Science (University of Southern California) and the author(s).

Internet Media Type application/pdf

Source 20180426-rozan-cstechreports-shoaf (batch), Computer Science Technical Report Archive (collection), University of Southern California. Department of Computer Science. Technical Reports (series)

Access Conditions The author(s) retain rights to their work according to U.S. copyright law. Electronic access is being provided by the USC Libraries, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright.

Repository Name USC Viterbi School of Engineering Department of Computer Science

Repository Location Department of Computer Science. USC Viterbi School of Engineering. Los Angeles\, CA\, 90089

Repository Email csdept@usc.edu

Inherited Values

Title Computer Science Technical Report Archive

Description Archive of computer science technical reports published by the USC Department of Computer Science from 1991 - 2017.

Coverage Temporal 1991/2017

Repository Email csdept@usc.edu

Repository Name USC Viterbi School of Engineering Department of Computer Science

Repository Location Department of Computer Science. USC Viterbi School of Engineering. Los Angeles\, CA\, 90089

Publisher Department of Computer Science,USC Viterbi School of Engineering, University of Southern California, 3650 McClintock Avenue, Los Angeles, California, 90089, USA (publisher)