scalable routing in delay tolerant networks mohammad reza faghani 1
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Scalable Routing In Delay Tolerant Networks
Mohammad Reza Faghani
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An Outer Space Network
Mars
Jupiter
station
Earth
MarsMars
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What is Delay Tolerant Networks ?
Intermittent link (dis)connection No guarantees on End-to-End path Frequent long duration partitioning
A B
E
D
F
C
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What is Delay Tolerant Networks ? High latency, low data rate
Order of hours latency
Long queuing times Because of disconnections (Store and
Forward) Extremely large (hours, even days)
Constraints on end node Limited power, limited buffer
How packets route !?4
Routing in DTN DTN Routing Challenges.
Instantaneous end to end path may not exist.
Large queuing delays. Buffer limitations at intermediate
nodes. Large messages.
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Routing in DTN Routing Goals:
Eventual Delivery (delivery ratio) Minimizing delivery delay Scalability Cost-effective
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The Routing Problem in DTN Nodes with finite storage capacity Links with dynamic behavior
Time varying capacity (c(t)) C(t) = 0, if link is down
Message Src, Dst, start-time, size
Output: Compute path(s) for every message Objective: Minimize delay Other objectives: message delivery ratio, minimize $
$ cost
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The Routing Problem in DTN Edge parameterized by
Source Destination capacity function delay function.
Define link costs and find minimum cost path. Cost varies with time. Compute minimum cost paths over this
dynamic cost assignment Modified Dijkstra by taking into account time of arrival
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Input variable used in Routing Contacts
Complete link time variant datas
Contacts summary : Time independent information Average waiting time until the next contact
Queuing : Link queues, available storage
Traffic Demand
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Routing input vs. Performance
Input variable used
“Per
form
ance
”
ContactsSummary
Contacts
Contacts+
LocalQueuing
MED
ED
EDLQEDAQ
LPdistributed
Contacts+
GlobalQueuing
Contacts+
GlobalQueuing
+Traffic
Demand
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Routing in large networks As the network size grows, number
of contacts increases. These algorithms are not scalable
for large networks. Using the idea used in static
scalable routing. The hierarchical routing
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Scalable Routing in DTNs Cong et. al. proposed a simple DTN
model. This makes hierarchical routing
possible For scalability, defined two
contact information compression methods.
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Simplified DTN model Static nodes (white) Mobile nodes with
repetitive motion Motion cycle:
T1=2 mins, T3=T4=3 mins Contact: a time period for communication.
Persistent contacts: (2,1), (3,4), & (5,6) Persistent contact: (ni nj - - -)
Predicted contacts: (1,3), (3,6), & (4,5) Predicted contact: (ni nj Tij tstart tduration)
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Hierarchical Routing in Static Networks
Hierarchical network Uses multilevel clustering. Offers scalable management of
routing tables. Hierarchical routing
Uses the hierarchical network as a topology abstraction
A top-down process: the decision made in a higher level is more important
Clustering & Clusterhead
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Hierarchical Clustering in Static Networks
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Level 2
Level 1
Level 0
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Hierarchical Clustering in Static Networks
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Level 2
Level 1
Level 0
Hierarchical Clustering in Static Networks
Before any routing each node in the network needs to obtain the topology information of its clusters in all levels.
Source should know the hierarchy address of destination.
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Hierarchical Clustering in Static Networks
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Level 2
Level 1
Level 0
Node 61 represents the cluster of nodes 60,80,240
All nodes have their own hierarchy address e.g. node 6 HA equals (13, 22, 61, 80).
Hierarchical Routing in Static Networks
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SourceDestination
Level 2
Level 1
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Hierarchical Routing in DTNs Hierarchical routing
Similar to that in static networks Multilevel clustering
Clusterhead selection Links: contact information
aggregation Contact information compression
methods
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Cluster head Selection Objective
Clusterhead: the center (in terms of delay) of a cluster Cluster members are close to their clusterheads
Absolute priority
D(i,j) is the weighed average delay between nodes i and j Higher if n is closer to the shortest paths among its
neighbors Clusterheads that have the highest APs are self-selected.
Relative priority
Node i selects a nearby clusterhead n who has a high AP
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Contact information aggregation Hierarchical links have
time-variant delays They contain aggregate
contact information Contact information in
a level k+1 link are aggregated from therelated level k links.
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Contact information compression
Aggregation level (La ) Above La, each link contain only a
constant delay
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Contact information Compression
Contact information aggregated to link (6,7) is shown in (c).
There are two possible shortest paths across the time as shown in (d) & (e)
The contact information stored by link (6,7) after contact information removal
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Hierarchical Routing in DTNs Similar to Hierarchical Routing in static
networks Hop by hop routing A top-down decision making within each hop
Step 1: top-down routing When the routing process is above La
Step 2: Routing with contact information Routes on the combined contact information
in all clusters below La
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Simulation An example
network Static nodes Mobile nodes
Mobile node Whose trajectory
travels severalrandom waypointswithin a randomsquare bound
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Simulation Results Route length Distribution
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Simulation Results Hop-count
ratio 70/30
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Simulation Results Delay ratio
70/30
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Simulation Results DHR+CIR
Delay ratio 70/50
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Simulation Results Storage communication overhead
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Summary Summary
Routing performance is close to the optimal routing result in terms of hop-count and delay
Routing performance improves as aggregation level (La ) increases
Routing performance improves as the source and destination distance increases
Storage and communication overhead is reduced by the compression methods while desirable routing performance and scalability is achieved.
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References [1] Liu C,Wu Jie. Scalable Routing in Delay Tolerant
Networks.In proc. of the 8th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2007
[2] S. Jain, K.Fall, and R.Patra. Routing in Delay Tolerant networks. In Proc. of ACM SIGCOMM, 2004
[3] Leonard Kleinrock, Farok Kamoun, "Hierarchical Routing for Large Networks, Performance Evaluation and Optimization", Computer Networks, Vol. 1, No. 3, pp. 155–174, January 1977
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Questions !?
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