Presentation on theme: "Scalable Routing In Delay Tolerant Networks"— Presentation transcript:
1Scalable Routing In Delay Tolerant Networks Mohammad Reza Faghani
2An Outer Space Network station Jupiter Mars Mars Mars Earth Can not have direct access
3What is Delay Tolerant Networks ? Intermittent link (dis)connectionNo guarantees on End-to-End pathFrequent long duration partitioningCBADEF
4What is Delay Tolerant Networks ? High latency, low data rateOrder of hours latencyLong queuing timesBecause of disconnections (Store and Forward)Extremely large (hours, even days)Constraints on end nodeLimited power, limited bufferHow packets route !?
5Routing in DTN DTN Routing Challenges. Instantaneous end to end path may not exist.Large queuing delays.Buffer limitations at intermediate nodes.Large messages.
7The Routing Problem in DTN Nodes with finite storage capacityLinks with dynamic behaviorTime varying capacity (c(t))C(t) = 0, if link is downMessageSrc, Dst, start-time, sizeOutput: Compute path(s) for every messageObjective: Minimize delayOther objectives: message delivery ratio, minimize $$ cost
8The Routing Problem in DTN Edge parameterized bySourceDestinationcapacity functiondelay function.Define link costs and find minimum cost path.Cost varies with time.Compute minimum cost paths over this dynamic cost assignmentModified Dijkstra by taking into account time of arrival
9Input variable used in Routing ContactsComplete link time variant datasContacts summary :Time independent informationAverage waiting time until the next contactQueuing : Link queues, available storageTraffic Demand
10Routing input vs. Performance Input variable used“Performance”ContactsSummary+LocalQueuingMEDEDEDLQEDAQLPdistributedGlobalTrafficDemand
11Routing 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
12Scalable Routing in DTNs Cong et. al. proposed a simple DTN model.This makes hierarchical routing possibleFor scalability, defined two contact information compression methods.
13Simplified DTN model Static nodes (white) Mobile nodes with repetitive motionMotion cycle:T1=2 mins, T3=T4=3 minsContact: 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)651324As a sample sattelite networks, define contacts by their category, define 0.1 , 0.01
14Hierarchical Routing in Static Networks 514181920211110122223Hierarchical networkUses multilevel clustering.Offers scalable management of routing tables.Hierarchical routingUses the hierarchical network as a topology abstractionA top-down process: the decision made in a higher level is more importantClustering & Clusterhead82613241415
15Hierarchical Clustering in Static Networks Level 2231273645Level 1Level 01025111211115191624222777221791318202182333366614444555
16Hierarchical Clustering in Static Networks 1Level 2231273645Level 1Level 0101125121115191624227722179131820218233366144455
17Hierarchical 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.
18Hierarchical Clustering in Static Networks 1Level 2231273645Node 61 represents the cluster of nodes 60,80,240All nodes have their own hierarchy addresse.g. node 6 HA equals (13, 22, 61, 80).Level 1Level 010112512111519162422772217913182021823336614445518
19Hierarchical Routing in Static Networks 1Level 2231273645Level 1Level 0101116121115191624227722179131820218233366144455DestinationSource19
20Hierarchical Routing in DTNs Similar to that in static networksMultilevel clusteringClusterhead selectionLinks: contact information aggregationContact information compression methods
21Cluster head Selection ObjectiveClusterhead: the center (in terms of delay) of a clusterCluster members are close to their clusterheadsAbsolute priorityD(i,j) is the weighed average delay between nodes i and jHigher if n is closer to the shortest paths among its neighborsClusterheads that have the highest APs are self-selected.Relative priorityNode i selects a nearby clusterhead n who has a high AP
22Contact information aggregationHierarchical links have time-variant delaysThey contain aggregate contact informationContact information in a level k+1 link are aggregated from the related level k links.651324
23Contact information compressionAggregation level (La )Above La, each link contain only a constant delay
24Contact information CompressionContact 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
25Hierarchical Routing in DTNs Similar to Hierarchical Routing in static networksHop by hop routingA top-down decision making within each hopStep 1: top-down routingWhen the routing process is above LaStep 2: Routing with contact informationRoutes on the combined contact information in all clusters below La
26Simulation An example network Mobile node Static nodes Mobile nodes Whose trajectory travels several random waypoints within a random square bound
31Simulation ResultsStorage communication overhead
32SummarySummaryRouting performance is close to the optimal routing result in terms of hop-count and delayRouting performance improves as aggregation level (La ) increasesRouting performance improves as the source and destination distance increasesStorage and communication overhead is reduced by the compression methods while desirable routing performance and scalability is achieved.
33References 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 S. Jain, K.Fall, and R.Patra. Routing in Delay Tolerant networks. In Proc. of ACM SIGCOMM, 2004 Leonard Kleinrock, Farok Kamoun, "Hierarchical Routing for Large Networks, Performance Evaluation and Optimization", Computer Networks, Vol. 1, No. 3, pp. 155–174, January 1977