Interest-Aware and Bandwidth-Efficient MulticastGroup Planning

Selcuk CevherM. Umit Uyar

The City College andGraduate Center of the City University of New York, NY

{scevher,uyar}@ccny.cuny.edu

Mariusz A. FeckoJohn Sucec

Sunil SamtaniApplied Research Area

Telcordia Technologies Inc., Piscataway, NJ{mfecko, jsucec, ssamtani}@research.telcordia.com

Abstract—In multicast group planning in tactical networks(TNs), one of the most important challenges is to maximizethe efficient use of network and end-user resources. Hence,multicast groups should be planned cleverly such that thetotal shared link capacity in a multicast distribution tree isincreased and the reception of unwanted traffic at receiver nodesis reduced to a maximum extent. In this paper, we proposea technique to generate a group configuration by estimatingthe link capacity shared by any two users in a shortest-pathmulticast tree to improve the branching characteristics such asheight and breadth of the tree while reducing the unwantedtraffic by considering overlapping user interests. A two-tierinformation dissemination system architecture for TNs, wherea central planner controls many dissemination executives, is atypical application for multicast group planning techniques. Weprovide simulation results showing that, compared to existinggroup planning techniques, our approach generates multicasttrees with improved branching characteristics while keeping theunwanted traffic within acceptable limits.

Keywords: multicast group planning, shortest-path multicasttree, total shared link capacity

I. INTRODUCTION

In military tactical networks (TNs), multicast group plan-ning is utilized to maximize the efficient use of network andend-user resources including available bandwidth, processingpower and battery life. TNs typically handle a large numberof information sources requested by a large number of users,each of whom is selectively interested in a subset of thesesources. One of the most important challenges in planning themulticast groups is to increase the total shared link capacitywhile reducing the unwanted traffic at the receiver nodes.

Multicasting enables sending out only one copy of aninformation to only a subset of users. Although multicastingcan reduce bandwidth utilization, it also increases routing stateand management overhead. Therefore, multicast is preferableto unicast only if its bandwidth efficiency overcomes therouting state and management overhead [1]. Since the shapeof a shortest-path multicast tree (i.e., height and breadth)has a critical impact on multicast bandwidth efficiency, new

978-1-4244-2677-5/08/$25.00 c©2008 IEEE This work has been preparedthrough collaborative participation in the Communications Networks Con-sortium sponsored by the U.S. Army Research Lab under the CollaborativeTechnology Alliance Program, Cooperative Agreement DAAD19-01-2-0011.The U.S. Government is authorized to reproduce and distribute reprints forGovernment purposes notwithstanding any copyright notation thereon.

techniques should be developed to improve the branchingcharacteristics of shortest-path multicast trees. In [2], multicastbandwidth efficiency is shown to increase due to the rise inthe number of shared links among receivers as the heightof a shortest-path multicast tree grows. Similarly, branchingthat occurs near the leaves of a multicast tree improves thebandwidth efficiency.

Another potential weakness of multicasting is that certainamount of irrelevant data can be received by multicast userssince each user receives all of the flows mapped to themulticast groups to which it subscribes. Especially in TNs,minimizing the amount of unwanted traffic is crucial sincethe users’ resources are very limited. Content filtering isone of the methods proposed to reduce the unwanted trafficreception [3], [4]. However, using filtering while disseminatingdata to a large receiver set is inefficient since it wastes bothnetwork and user resources since the task of determining theinterest of a received message is placed on the receiver set[5]. Furthermore, the placement of filtering nodes is a diffuculttask in mobile wireless networks due to dynamically changingnetwork topology.

Multicast group planning techniques try to find an ap-proximate solution to NP-complete Channelization Problem(CP) defined in [6]. Creating as many multicast groups asthe number of information sources is not feasible since itwill create an unacceptable multicast state and managementoverhead. For example, a two-tier information disseminationsystem architecture for TNs, where a central planner controlsmany dissemination executives, is a typical application formulticast group planning techniques.

CP is defined as finding an optimal mapping of informationflows to multicast groups, and an optimal subscription ofusers to multicast groups so as to minimize total bandwidthconsumption and unwanted traffic. User-Based Merge (UBM),and Flow-Based Merge (FBM) continously merge two usersinto the same multicast group based on a pairwise cost function[6]. Both UBM and FBM assumes that the optimal numberof multicast groups is pre-determined. On the other hand,localized multicast group update (LMGU) algorithm defined in[7] and [8] generates an adaptive number of multicast groupsdepending on the underlying network conditions (e.g., end-user tolerance to unwanted traffic).

In this paper, we propose a novel multicast group plan-ning technique by estimating the link capacity shared byany two users in a shortest-path multicast tree to improvebranching characteristics and by considering overlapping userinterests to reduce the unwanted traffic for each user. Weprovide simulation results showing that, compared to exist-ing techniques, our approach generates multicast trees withimproved branching characteristics while keeping unwantedtraffic within acceptable limits. For example, total shared linkcapacity was observed to increase by %18 and more than%100 for dense topologies with 50 and 35 nodes, respectively.

In the next section, we provide background informationon CP. We analyze the motivation for our multicast groupplanning technique and describe our new algorithm in Sec-tion III. We provide simulation results for dense and sparsetopologies with different number of nodes generated by BRITEtopology generator [11] in Section IV. We finally present ourconclusions in Section V.

II. BACKGROUND

A. Channelization Problem (CP)

CP in a data dissemination system can be describedthrough three matrices, namely, user-flow interest matrixW , flow-to-multicast group mapping matrix X , and thesubscription matrix Y , which are defined as follows [6] :

W=(wjm)N×M , where rj ∈ R, fm ∈ F , N=|R|, M=|F |,

wjm ={

1 receiver rj is interested in source fm

0 otherwise

X=(xim)M×K , where fi ∈ F , gm ∈ G, M=|F |, K=|G|,

xim ={

1 flow fi is assigned to multicast group gm

0 otherwise

Y =(yjm)N×K , where rj ∈ R, gm ∈ G, N=|R|, K=|G|,

yjm ={

1 receiver rj subscribes to multicast group gm

0 otherwise

In the above-mentioned equations, F = {f1, f2, ..., fM} isthe set of information sources, G = {g1, g2, ..., gK} is the setof multicast groups, and R = {r1, r2, ..., rN} is the set ofreceivers. Given W matrix, CP can be defined as finding Xand Y matrices. Although CP is NP-Complete [6], practicalnetwork conditions may not require the optimal solution ofCP.

B. Overlap Matrix

An interest profile Pi of a receiver ri specifies the set ofinformation sources that ri wants to receive. An NxN profileoverlap matrix (POM ) representing the common informationinterests among receivers is defined in [9]. The value of eachentry POMij is the ratio of the number of information sourcesincluded in both Pi and Pj to the number of informationsources contained only in Pi. On the other hand, an NxNinterest overlap matrix (IOM ) is defined in [7] and [8]using a different pairwise cost function. Each entry IOMij

Fig. 1. Hierarchy in military tactical networks

is |Pi

⋂Pj | − |(Pi − Pj)

⋃(Pj − Pi)| where Pi

⋂Pj and

(Pi − Pj)⋃

(Pj − Pi) represent the common and uncommoninformation interests between ri and rj , respectively.

For example, given W in Table I, the interest profiles P1 andP2 of r1 and r2 are the sets {f2, f3} and {f1, f2}, respectively.POM12 is 1/2 due to |P1

⋂P2| = 1 (i.e., P1

⋂P2 = {f2})

and |P1| = 2. However, IOM12 is 1 − 2 = −1 sinceP1

⋂P2 = {f2} and (P1 − P2)

⋃(P2 − P1) = {f1, f3}.

f1 f2 f3

r1 0 1 1r2 1 1 0r3 0 0 1

TABLE IAN EXAMPLE W MATRIX

III. OUR APPROACH

A. Motivation

In a four-level hierarchical military tactical network (TN),there is typically a direct connection between a layer and thelayers below it (Fig. 1) [10]. A Soldier Radio Waveform (SRW)subnetwork is deployed to transmit data, voice, and videoflows to the soldiers in a Future Battlefield Network (FBN).In this environment, multicast transmissions are preferred todeliver the ISR (intelligence, reconnaissance and surveillance)data, both for air-to-ground (from UAV sensors) and forground-to-ground links, and low-bandwidth SRW data fromsensor fields. For example, the soldiers on one or more SRWsubnets may need to be constantly updated with situation-awareness information gathered from multiple sources, includ-ing for example soldiers on another SRW subnet, sensor infor-mation from unattended sensors connected through an SRWnetwork, and surveillance data from the UAV sensors. Theamount of data from these sources is likely to vary with respectto the volume and update frequency, leading to a dynamicand adaptive multicast dissemination environment. However,multicast deployment in FBNs can be justified only if multicastefficiency overcomes additional routing state and managementoverhead. Since the shape of a multicast distribution tree has acritical impact on bandwidth efficiency, multicast trees shouldbe created cleverly so that their branching characteristics areimproved as much as possible.

r1

source

r2

r3

r1

source

r2 r3

(a) (b)

Fig. 2. (a) A Path graph with 4 nodes (b) A star graph with 4 nodes

In FBNs with highly dynamic mobile nodes intercon-nected by heterogeneous wireless links, if well-known mul-ticast groups can be pre-planned and pre-loaded into networknodes/hosts, the multicast discovery problem becomes trivial.However, this solution may also typically result in the receiptof unwanted multicast traffic as members of these well-knowngroups will receive packets from all group sources even thoughthe data from certain sources are not needed. While thismay not be a problem in capacity-rich commercial networks,unnecessary multicast traffic could saturate the small-capacityFBN wireless links.

Our proposed solution to CP addresses the improvement inbranching characteristics of multicast trees to allow efficientmulticast transmissions and the reduction of the receipt ofunwanted traffic while ensuring that each user receives allinformation it requests.

B. Shared Link Matrix

The main benefit of multicasting technique is dependentupon the shape of the multicast distribution tree. If the majorityof the paths from the source are not shared by the receivers,multicast is not much more efficient than unicast. In fact, insuch a case, multicast may be even less efficient than unicastdue to the routing state overhead to implement it. Multicasttree efficiency is determined by two important properties [2],namely, height and breadth of the tree. As the height ofthe multicast tree grows due to newly added receivers nearthe bottom, the number of shared links among the receiversincreases. Early branchings in the tree (i.e., near the root)decreases the multicast efficiency since this will increase thepacket duplication and reduce the number of shared links.

A path and a star graph, each with four nodes, are depictedin Figs 2.a and 2.b, respectively, where the black nodes arethe receivers and the gray ones are the information sources. Apath graph, each receiver node with one incoming edge, has thelargest height among all possible trees with the same numberof nodes while a star graph, with the information source isdirectly connected to all receivers, has the smallest.

The number of transmission links to be traversed for asingle data packet from the information source to reach eachreceiver in Fig. 2 is shown in Table II. If multicasting isdeployed in the topology in Fig. 2.a, three transmission links

r1 r2 r3

4-node path graph 1 2 34-node star graph 1 1 1

TABLE IISHORTEST PATH LENGTHS FOR EACH RECEIVER IN FIGS 2.A AND 2.B

will be used to deliver a data packet to all receivers sincemulticasting prevents packet duplication. In the case of unicast,however, the same packet will be repeatedly sent to eachreceiver, utilizing a total of six transmission links. On the otherhand, multicast and unicast schemes use the same numberof transmission links in the topology in Fig. 2.b due to thebranching at the information source. This difference in therelative multicasting efficiency is an indication of how muchthe height of the distribution tree impacts the performance ofmulticasting.

Let us define a matrix to store the number of shared linksamong all user pairs in a multicast distribution tree.

Definition 1: Let ri and rj (i, j ∈ N; i < j) be two receiversin a multicast distribution tree. The number of links shared byri and rj is defined as the size of the largest subpath rootedat the information source which is traversed by both of theshortest paths from the information source to ri and rj .

Definition 2: Shared link matrix S is defined as the sym-metric square matrix (sij)N×N where N is the number ofreceivers in the multicast distribution tree, and each entry sij

contains the number of links shared by the receivers ri and rjfor i 6= j.

(a) S matrix for Fig. 2.ar1 r2 r3

r1 x 1 1r2 x x 2r3 x x x

(b) S matrix for Fig. 2.br1 r2 r3

r1 x 0 0r2 x x 0r3 x x x

TABLE IIISHARED LINK MATRICES FOR FIG.2

Table III shows the shared link matrices for Figs 2.a and2.b. Any element sij , where i = j or i > j, is not included inthe computation since (1) the number of links that a receiver rishares with itself is already the length of the shortest path fromthe information source to ri, and (2) matrix S is symmetric.Such sij (i = j, or i > j) elements are denoted by xin Table III. As can be seen in Table III, a larger numberof transmission links are shared among the receivers in themulticast distribution tree if the height of the tree increases.

C. Our Algorithm (IBMP)

IBMP aims to estimate the number of links which willbe shared by each pair of receivers in a multicast tree. Itmerges the user pairs who will share a relatively larger numberof transmission links with a high probability into the samemulticast group. For this purpose, it sequentially finds theshortest-path trees (SPTs) for a given network topology wherethe root of the tree is the information source. The possible

Fig. 3. Algorithm for finding the estimated number of shared links

Fig. 4. Algorithm for combining S and IOM matrices

number of transmission links in a multicast tree which willbe shared by a receiver pair is determined by calculating themean of the number of shared links in each individual SPT ofeach information source.

IBMP consists of two tasks, namely, finding the estimatednumber of shared links for each receiver pair in a multicasttree (i.e., S matrix) and combining S with IOM matrix. Thealgorithms for these tasks are shown in Fig. 3 and Fig. 4,respectively. The current topology G, the set of informationsources F and the set of receivers R are the inputs for the al-gorithm in Fig. 3 where |F | denotes the total number of SPTs.Function called findSPT (G, fi) employs the Dijkstra’s algo-rithm to find the SPT starting from the the information sourcefi. Function called findS(SPT ) computes the S matrix forSPT found by findSPT . Using the two inner loops in Fig. 3,the estimated number of shared links for each receiver pair inthe multicast tree is computed as the mean number of sharedlinks in |F | shortest-path trees.

IBMP presents a tradeoff between the unwanted traffic andthe number of shared links for each pair of receivers ri and rj(i, j ∈ N; i < j) using the simple function f to combine eachentry sij in S with the corresponding element mij in IOM :

f(sij ,mij) = λ ∗ sij + (1− λ) ∗mij (1)

where λ (0 ≤ λ ≤ 1) determines the contribution of eachentry in S to the corresponding entry in the combined matrixSc

ij . The algorithm for combining S with IOM is presentedin Fig. 4.

1

2

3

4

5

6

7

10

53

8

129

152

1

7

Fig. 5. An example topology for 7 nodes and 10 edges

2

3

10

3

2

1

4

5

6

7

5

15

1

1

2

3

4

5

6

710

3

8

15

2

1

2

3 5

3

9

2

1

4

5

7

5

15

1

root root

6

root

(a)

(b)

(c)

Fig. 6. SPTs for different information sources

To evaluate the performance of our IBMP algorithm, weutilized a modified version of localized multicast group update(LMGU) algorithm described in [7]. LMGU algorithm usesIOM to merge a user pair with maximum merging benefit intothe same multicast group. We added a new input parameter toLMGU algorithm enabling us to execute LMGU on Sc ratherthan on IOM .

Let us describe the computation of S matrix using theexample topology with seven nodes shown in Fig. 5 wheredotted and solid circles represent information sources andreceivers, respectively. For simplicity, each node in Fig. 5is assumed to have a single functionality, namely, either aninformation source or a receiver. The numbers over the edgesdenote the link capacities. IBMP sequentially finds the SPTsof the entire topology of Fig. 5 for the informaton sourcesnumbered as 2, 5 and 7. These SPTs are shown in Figs 6.a, 6.band 6.c, respectively. Table IV shows the S matrices computedfor each SPT in Fig. 6. For example, nodes 1 and 3 in Fig.6.a share no transmission links in Table IV.a since the shortestpaths starting at node 2 to each of these nodes do not intersect.On the other hand, the shortest path to node 6 contains theshortest path to node 1 resulting a shared transmission linkwith a capacity of 10.

Table V presents the S matrix computed based on the Smatrices in Table IV. Each entry sij in S is calculated as themean value of sij elements in S matrices shown in Table IV.For example, the estimated shared link capacity between nodes1 and 6 in Table V is (10+2+15)/3 = 9 where 3 representsthe total number of information sources.

In conclusion, merging the nodes 1 and 6 in the exampletopology of Fig. 5 into the same multicast group will result in

(a) S matrix for Fig. 6.anode 1 node 3 node 4 node 6

node 1 x 0 0 10node 3 x x 5 0node 4 x x x 0node 6 x x x x

(b) S matrix for Fig. 6.bnode 1 node 3 node 4 node 6

node 1 x 3 3 2node 3 x x 11 2node 4 x x x 2node 6 x x x x

(c) S matrix for Fig. 6.cnode 1 node 3 node 4 node 6

node 1 x 0 0 15node 3 x x 9 0node 4 x x x 0node 6 x x x x

TABLE IVS MATRICES COMPUTED FOR THE EXAMPLE TOPOLOGY IN FIG. 5

a larger amount of shared link capacity with a high probabilitysince 9 is the maximum entry in S presented in Table V.

node 1 node 3 node 4 node 6node 1 x 1 1 9node 3 x x 8.333 0.666node 4 x x x 0.666node 6 x x x x

TABLE VS MATRIX COMPUTED BASED ON THE S MATRICES IN TABLE IV

IV. SIMULATION RESULTS

We used BRITE topology generator [11] in Bottom-upmode for our simulations. To create realistic topologies, we re-lied on the experiments performed in [12] and [13] to select theBRITE parameters listed in Table VI. If bw distribution is setto constant, all link capacities have identical values, namely,minimum bw= 100. Otherwise, a uniform distribution rangingfrom 100 to 1024 is adopted. Sparse and dense topologies with20, 35 and 50 nodes were generated by adjusting p (add) andbeta parameters which affect the probability of interconnectionbetween any two nodes. To create sparse topologies, we usedthe small value of 0.01 for both p (add) and beta. The valuesof 0.44 and 0.63 were utilized to generate dense topologies.For each generated topology, we set |F |=|V |/4 where |F |and |V | denote the total number of information sources andthe total number of nodes in the topology, respectively (i.e.,|N |=|V |−|F | where |N | is the total number of receivers). Thetransmission rate of each information source fi was randomlyselected as either 10 or 100.

We evaluated the performance of our IBMP algorithm interms of two different metrics, namely, unwanted traffic andtotal shared link capacity. For this purpose, we sequentiallyexecuted LMGU algorithm on Sc and IOM matrices fordifferent values of 0 ≤ λ ≤ 1, and compared unwanted traffic

Configuration 1 Configuration 2grouping model Random pick Random pick

model GLP GLPnode placement Random Randombw distribution Uniform, Constant Uniform, Constantminimum bw 100 100maximum bw 1024 1024

m 2 2number of nodes 20,35,50 20,35,50

p (add) 0.01 0.44beta 0.01 0.63

TABLE VIBRITE PARAMETERS

and total shared link capacity in various source-based multicasttrees with 4, 6 and 8 subscribers.

The results obtained from our simulations are shown inFigs. 7 through 16. The information presented in each of thesefigures was obtained as the mean of 10 different topologies.The title of each figure specifies the size and density (i.e., p(add) and beta) of the topology, and the link capacity distri-bution (i.e., uniform or constant) used in the correspondingsimulation. The x-axis of each figure (i.e., lambda) refers to λin Eq. 1 while y-axis represents the total shared link capacityor unwanted traffic per user. Each figure legend demonstratesthe number of multicast groups (i.e., 4, 6, or 8) and the typeof the LMGU input matrix (i.e., active for Sc, and inactivefor IOM ) used in the respective simulation.

Figs. 7 through 10 show the simulation results obtained forsparse/dense topologies with 20 nodes where link capacitieshave a uniform distribution. For four multicast groups, the totalshared link capacity was observed to increase by more than%100 for λ = 1 (Fig. 9) when LMGU algorithm was executedon Sc matrix. The total shared link capacity remained thesame regardless of λ when LMGU algorithm utilized IOMmatrix since IOM does not reflect any consideration for theshared link capacity. As expected, we obtained similar curvesfor unwanted traffic per user (Fig. 10) since the contributionof the user interests (i.e., 1−λ) to each entry in the combinedmatrix Sc in Eq. 1 decreases as λ grows. In general, thedecision on the value of λ depends on the specific networkconditions (e.g., network bandwidth and user tolerance tounwanted traffic). In sparse and dense topologies with 35nodes, for 8 multicast groups, the total shared link capacitiesincrease by %100 for λ = 0.3 for sparse topologies (Fig. 11),and by more than %100 for λ = 0.75 for dense topologies(Fig. 12). Interesting results were observed for sparse anddense topologies with 50 nodes shown in Figs. 13 through16. For example, as opposed to the expected behaviour, thetotal shared link capacity increased by %18 for 4 multicastgroups and λ = 0.75 (Fig. 15) while no additional unwantedtraffic per user was generated (Fig. 16).

V. CONCLUSIONS

In this paper, we propose a multicast group planningtechnique to generate a group configuration by estimatingthe transmission link capacity shared by any two users in a

Fig. 7. Sparse topology with 20 nodes, uniformly distributed link capacities

Fig. 8. Sparse topology with 20 nodes, uniformly distributed link capacities

Fig. 9. Dense topology with 20 nodes, uniformly distributed link capacities

Fig. 10. Dense topology with 20 nodes, uniformly distributed link capacities

Fig. 11. Sparse topology with 35 nodes, uniformly distributed link capacities

Fig. 12. Dense topology with 35 nodes, identical link capacities

Fig. 13. Sparse topology with 50 nodes, identical link capacities

Fig. 14. Sparse topology with 50 nodes, identical link capacities

Fig. 15. Dense topology with 50 nodes, identical link capacities

Fig. 16. Dense topology with 50 nodes, identical link capacities

shortest-path multicast tree to improve the branching charac-teristics (i.e., height and breadth) of the tree while reducing theunwanted traffic per user by considering overlapping user in-terests. We provide simulation results showing that, comparedto existing group planning techniques, our approach generatesmulticast trees with improved branching characteristics whilekeeping the unwanted traffic within acceptable limits. Forexample, total shared link capacity was observed to increaseby %18 and more than %100 for dense topologies with 50and 35 nodes, respectively. 1

REFERENCES

[1] C. Diot, B. N. Levine, B. Lyles, H. Kassem, and D. Balensiefen,Deployment Issues for the IP Multicast Service and Architecture, IEEENetwork Magazine special issue on Multicasting, vol. 14, no. 1, Jan 2000.

[2] R. C. Chalmers, and K. C. Almeroth, Modeling the Branching Charac-teristics and Efficiency Gains in Global Multicast Trees, Infocom, 2001

[3] R. Shah, R. Jain, and F. Anjum, Efficient Dissemination of PersonalizedInformation Using Content-Based Multicast, IEEE Infocom, 2002.

[4] T. W. Yan, and H. G. Molina, SIFT - A Tool for Wide-Area InformationDissemination, USENIX Technical Conference, pages 177-186, 1995.

[5] B. N. Levine, J. Crowcroft, C. Diot, J. J. Garcia-Luna-Aceves, and J.F. Kurose, Consideration of Receiver Interest for IP Multicast Delivery,Infocom, 2000.

[6] M. Adler, Z. Ge, J.F. Kurose, D. Towsley, and S. Zabele, ChannelizationProblem In Large Scale Data Dissemination, ICNP, 2001.

[7] X. Ma, S. Cevher, M. U. Uyar, M. Fecko, J. Sucec, and S. Samtani,Network Planning for Multicast Using Partitioned Virtual User Do-mains, LNCS 4787 Real-Time Mobile Multimedia Services, pp. 113-124,MMNS 2007, San Jose, CA, October 2007

[8] S. Cevher, M. U. Uyar, M. Fecko, J. Sucec, and S. Samtani, MulticastPlanning for Mission-Critical Networks, IEEE Sarnoff Symphosium,Princeton, NJ, USA, April 2008

[9] O. Papaemmanouil and U. Cetintemel, SemCast, Semantic Multicast forContent-Based Data Dissemination, ICDE, 2005.

[10] J. Lee, and M. Cheng, Hierarchical Level-based IP Multicasting forTactical Networks, MILCOM, 1999.

[11] BRITE Topology Generator, http://www.cs.bu.edu/brite/[12] M. Gjoka, V. Ram, and Y. Xiaowei, Evaluation of IP Fast Reroute

Proposals, COMSWARE, 2007[13] O. Heckmann, How to use topology generators to create realistic

topologies, Technical Report, 2002

1The views and conclusions contained in this document are those of the au-thors and should not be interpreted as representing the official policies, eitherexpressed or implied, of the Army Research Lab or the U.S. Government.