Efficient Multicast Schemes for Optical Burst-Switched WDM Networks Myoungki Jeong† , Yijun Xiong‡ , Hakki C. Cankaya‡ , Marc Vandenhoute‡ and Chunming Qiao† Depts. of EE and CSE† , SUNY at Buffalo Alcatel Corporate Research Center‡ Buffalo, NY 14260 Richardson, TX 75081 fmjeong,
[email protected] fxionyi,cankhc,
[email protected] Abstract– In this paper, we study several multicast schemes in optical burst-switched WDM networks taking into consideration of the overheads due to control packets and guard bands (GBs) of bursts on separate channels (wavelengths). A straightforward scheme is called Separate Multicasting (S-MCAST) where each source node constructs separate bursts for its multicast (per each multicast session) and unicast traffic. To reduce the overhead due to GBs (and control packets), one may piggyback the multicast traffic in bursts containing unicast traffic using a scheme called Multiple Unicasting (M-UCAST). The third scheme is called Tree-Shared Multicasting (TS-MCAST) whereby multicast traffic belonging to multiple multicast sessions can be mixed together in a burst, which is delivered via a shared multicast tree. The multicast schemes (MUCAST and TS-MCAST) are compared with S-MCAST in terms of bandwidth consumed and processing load.
1 Introduction As traffic demand increases exponentially in the Internet, Wavelength Division Multiplexing (WDM) networks [3, 6, 12] become a natural choice for the backbone. Recently, IP over WDM networks (or so-called Optical Internet) have received a considerable amount of attention (e.g. [1, 10]). Multicasting (i.e. one-to-many communications) is becoming more and more popular and important in the Internet. Multicasting in IP over WDM networks can be done via IP multicast, multiple WDM unicast, or WDM multicast [4]. In this paper, we will concentrate mainly on WDM multicast. There are two WDM multicasting approaches, one based on wavelength-routing as in [5, 11, 7], and the other based on optical burst switching (OBS) [8, 10] as in [4, 15]. In the former, multicast data will be switched to one or more outgoing wavelengths according to the incoming wavelength that carries it (as in wavelength-routing). In other words, a wavelength needs to be dedicated to each branch of a multicast tree. This scheme is suitable for high bandwidth multicast applications having a relatively long duration, such as video distribution. In the latter, This research is supported in part by a grant from NSF under contract number ANIR-9801778.
no wavelengths need to be dedicated to a multicast tree and the multicast data is transported via OBS which is more bandwidth efficient than wavelength routing for bursty traffic. However, there are two major overheads in using OBS, namely, control packets and guard bands (GBs). More specifically, to send each burst, a control packet needs to be sent to set up switches and GBs are used in the burst to accommodate possible timing jitters at each intermediate node. In this paper, we study several multicast schemes in optical burst-switched WDM networks taking into consideration of the overheads due to control packets and guard bands (GBs) of bursts on separate channels (wavelengths). These will result in different bandwidth consumption (which is proportional to different amount of GBs) , different channel utilization (inversely proportional to different burst lengths) and different processing loads (proportional to the number of control packets generated) under the same traffic condition. Specifically, we consider three multicast schemes: Separate Multicasting (S-MCAST), Multiple Unicasting (M-UCAST) and Tree-Shared Multicasting (TS-MCAST). Moreover, we propose three tree sharing strategies in TS-MCAST based on Equal Coverage (EC), Super Coverage (SC) and Overlapping Coverage (OC). The performance of the multicast schemes (M-UCAST and TS-MCAST) are compared with that of S-MCAST in terms of bandwidth consumed and processing load. The rest of this paper is organized as follows. In Section 2, we describe different multicast schemes for multicast traffic. In Section 3, we discuss tree sharing strategies and develop algorithms to construct shared tree. In Section 4, we describe simulation model used and present numerical results of the proposed multicast schemes. We conclude the paper in Section 5.
2 Multicasting in Optical Switched WDM Networks
Burst-
In this section, we describe three schemes for multicasting in optical burst-switched WDM networks, namely, S-MCAST, M-UCAST and TS-MCAST. We assume that each source maintains one burst assembly queue for each multicast session and each destination of unicast traffic. In S-MCAST, each multicast group (session) constructs its
own source-specific multicast tree along which the assembled bursts carrying multicast traffic for the group are delivered. In other words, multicast traffic is transmitted independently of unicast traffic. More specifically, in each multicast session, IP packets arriving within a given assembly time Tbm are assembled into a burst. After the assembly time is over, the burst is sent out along the multicast tree. In M-UCAST, multicast traffic is treated as unicast traffic. More specifically, during the burstification process, a copy of multicast data can be assembled together with unicast data for the same destination. In other words, multicast is accomplished with multiple unicast and hence multicast traffic essentially gets a “free” ride for GBs. By doing so, the control overhead can be reduced since the GB can be shared by both traffic types in a longer burst, and in addition, the number of control packets also decreases. Hence, although M-UCAST wastes bandwidth for multicast traffic (see e.g. [11]), it is possible that under certain network conditions (and in particular, depending on the GB size), M-UCAST for multicast traffic is better than S-MCAST (see Section 4). In TS-MCAST, if there is a certain degree of membership overlap or some special relation between multicast sessions in Hi where Hi is a set of all multicast sessions originating from an edge router i, the set Hi is split into a number of subsets based on certain strategies (to be described in Section 3). Each subset, called Multicast Sharing Class (MSC)1 , then either constructs a new shared tree (ST) or uses one of the existing multicast trees (one of the multicast sessions in the subset) depending on the algorithm used to construct or select the ST (also to be described in Section 3). During the burstification process, IP packets belonging to the multicast sessions in a MSC are assembled together to form bursts. Therefore, the average burst length using TS-MCAST will be longer than that without tree sharing. This helps reduce the bandwidth waste due to GB as well as the number of control packets generated. Note that multicast traffic is transmitted independently of unicast traffic.
3 Tree Sharing Multicasting In this section, we describe how to decompose a set of multicast sessions (Hi ) into a number of MSCs where each MSC uses a ST. Let Nc be the set of all core routers, Ne be the set of all edge routers and Nl be the set of all links in the network. In the following discussion, we model a multicast session (or tree) in the network using a triple T = (C; E ; L) where C Nc is the set of core routers, E Ne is the set of edge routers, and L Nl is the set of links on the multicast tree, respectively.
3.1 Tree Sharing Strategies We consider three strategies, namely Equal Coverage, Super Coverage and Overlapping Coverage, for deciding which sub1 subset and MSC will be used interchangeably
may contain only one multicast session.
and as a special case, a MSC
set of j Hi j ( 2) multicast sessions rooted at an edge router i should become a MSC. For simplicity, we use MSC j rather than MSCi j to denote the jth MSC at an edge router i. In EC, multicast sessions with the same membership are grouped into the same MSC. Hence, s multicast sessions in MSC j (as its elements) have the same set of member edge routers, i.e., E j1 = E j2 = = E js although each multicast session in MSC j may have a different multicast tree (or path to each member). Fig. 1 (left) shows an example of EC (where s = 2) in which multicast trees T1 (solid line) and T2 (dashed line) have the same set of edge routers (i.e. E4, E6 and E8) as their members. In such a case, one of the existing multicast trees, T1 or T2, is selected to be the new ST. A less restricting tree sharing strategy is SC where all multicast sessions in MSC j do not necessarily have the same set of edge routers. Specifically, if two multicast sessions, T1 and T2 have a special relation or in other words, T1 is a super tree for MSC j , such that ET2 ET1 , then T1 and T2 are grouped into the same MSC (i.e. MSC j ) (see Fig. 1 (middle)). Note that, IP packets (belonging to T2) will also be delivered to E3 and E5 via T1 but subsequently discarded by E3 and E5. Finally, OC is the most general strategy in that it allows a number of multicast sessions having a sufficient degree of overlap in edge routers E, core routers C, links L or tree sharing gain α (from (4)) to be grouped into the same MSC. More specifically, we define the degree of overlap as follows. Consider the jth MSC (MSC j ) at an edge router i and assume that it has s multicast sessions. That is, MSC j = (T j1 ,T j2 ,...,T js ) where T jk = (C jk ; E jk ; L jk ) for k=1,2,...,s. Then, the degree of overlap can be defined in terms of edge routers, core routers or links in the MSC as follows: γ
E
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L
S
=
∑sk=1 j E jk j ? j k E jk j j Sk E jk j (j MSC j j ?1) ;
(1)
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=
∑sk=1 j C jk j ? j k C jk j j Sk C jk j (j MSC j j ?1) ;
(2)
S
=
∑sk=1 j L jk j ? j k L jk j j Sk L jk j (j MSC j j ?1) :
(3)
∑k2MSCi j (rik + G=Tbm )sik : (r0i j + G=Tbm )s0i j
(4)
Note that according to such a definition, γE = 1 in EC and γE 1 in SC and OC. For a MSC j at an edge router i, denoted by MSCi j , we define the bandwidth gain due to tree sharing as the ratio between the average amount of multicast traffic carried per link without/with tree sharing. Let ri j be the data rate of a session j at an edge router i and si j be the number of links on the multicast tree for the session j. In addition, let r0i j be the sum of the data rate of all the sessions in MSCi j and s0i j be the number of links on the shared multicast tree used for MSCi j . Assuming that G is the guard band size, then the bandwidth gain α j for MSCi j due to tree sharing is equal to αj =
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Figure 1: Tree Sharing Strategies: Equal Coverage (left), Super Coverage (middle) and Overlapping Coverage (right). One may apply the OC strategy as follows. First, one of the four criteria, namely, the edge router overlap, core router overlap, link overlap, and tree sharing gain is selected. Then from a set of given multicast sessions, choose a pair of two-session with either the highest value of γE (γC or γL ) or the highest value of α depending on the criterion applied. Afterwards, a third session (one of other multicast sessions not belonging to the pair selected) is combined with the pair if this increases the value of the criterion selected, and so on. Regardless of the initial criterion selected, traffic sharing gain α for the candidate MSC is evaluated. If α is more than a threshold value η (say 1.0), the MSC is formed. The same process is repeated for remaining multicast sessions (if 2) to form another MSC, and so on. Note that if α is used as the initial criterion, α is applied two times (first for selection and second for examination). Fig. 1 (right) shows an example for OC where if the combination of T1 and T2 has the highest value of the criterion applied, and its traffic gain is over a given threshold, the two trees are grouped into the same MSC.
3.2 Construction of Shared Trees After one has decomposed a set of multicast sessions (Hi ) at an edge router i into a number of MSCs according to one of the tree sharing strategies, each MSC has to construct a ST by treating all the members in the subset of the multicast sessions as a new multicast group for the purpose of burstification (forming bursts) and burst delivery. Note that the cases for EC or SC are trivial because one can use any existing tree in EC, and a super tree in SC, respectively. For OC, we propose three ST construction algorithms to construct a ST TS = (V S , LS ) where V S is the set of all nodes (edge and core routers), and LS is the set of all links on ST, respectively. In addition, we consider an algorithm for the purpose of comparison, called ST-NEW, in which a new multicast tree for a MSC is constructed with all members in the MSC by applying the multicast tree construction algorithm of the multicast session. 3.2.1 Greedy Algorithm In this algorithm, all nodes (core and edge routers) and links belonging to the existing multicast trees of a MSC are used to construct ST for MSC. More specifically, the following algorithm called ST-GREEDY is used. ST-GREEDY Algorithm
/* i is the source (root) edge router of MSC j */ φ; V S V S i ; LS φ; VS for each tree T jk MSC j do VS VS C jk E jk ; LS LS L jk ; end for
[fg 2 [f g[f g [f g
Note that it is possible that the ST contains redundant links (e.g. the links (C1; C2) and (C2; C3) in Fig. 1 (left)). 3.2.2 Breadth First Search (BFS) Algorithm In this algorithm, we apply BFS to construct a ST based on the existing trees of a MSC. Starting at the source edge router (root), every link from u to a node v ( one of neighbors) is examined to see if v was on any existing tree T jk (i.e. if v 2 E jk ) but not on the ST yet. If so, both the link and node v are added to the ST, and the node v is added to a queue Q for future consideration. Then the process repeats for each node in the queue Q until Q is empty. Note that the algorithm eliminates some redundant links but does not eliminate all (e.g. the link (C1; C2) in Fig. 1 will still be there but the link (C2; C3) not.). The algorithm called ST-BFS is as follows. ST-BFS Algorithm /* i is the source edge router of MSC j */ V S = i ; LS = φ; Q = φ; enqueue(Q; i); while (Q = φ) do u dequeue(Q); for each link e from u to a neighbor v do s E s C if ((v k=1 jk k=1 jk ) & (v v ; VS VS LS LS e ; enqueue(Q; v); end for end while
fg
6
2 f[
[[ [f g [f g
g
2 V )) =
S
3.2.3 Member-Initiated Algorithm In this algorithm, we start with an existing multicast tree with the largest number of members as the base of the new ST. Then all other members perform an operation similar to a ”join” in CBT [13] (or ”graft” in DVMRP [9]) by doing so, arguments the base tree with additional nodes and links. The algorithm called ST-MEMBER is as follows. ST-MEMBER Algorithm Max(T jk ) where i=1,2,...,s; Tmax
VS φ; LS φ; for each node v Tmax and its link l Lmax do VS VS v ; LS LS l ; end for s E do for each edge router v k=1 jk while (v = V S ) do find upstream node vup on an existing tree T jk (= Tmax ) and link (v; vup ) towards the source; LS LS (v; vup ); VS VS v ; vup ; v end while end for
2 [f g
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2[
[f g
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[ [f g
Cloud of core routers IP subnet Edge router
Core router
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Note that, if the existing trees T jk have been constructed using an efficient algorithm, then by putting the links on the existing tree whenever possible will result in a more efficient ST than that constructed by ST-BFS. In addition, in ST-MEMBER, there are no redundant links as in ST-GREEDY and ST-BFS. As an example, in Fig. 1 (right), after choosing T2 as the Tmax , E4 and E7 will join a new ST (which is the same as T2 initially) according to the algorithm. Then the constructed new ST will include links (C4,C1), (C1,C2), (C2,C3) and (C3,C6) (just showing the links between core routers only). However, ST-GREEDY and ST-BFS will have redundant links (C4,C5) and (C5,C6) (ST-GREEDY only).
4 Simulation Study 4.1 Network Model and Assumptions The network model used consists of edge routers and core routers as shown in Fig. 2. Each edge router is an access point to a backbone network consisting of core routers. For simulation, a random backbone network is generated. We assume that there is a WDM link between nodes (the terms node and router are used interchangeably) with an unlimited bandwidth (i.e. a sufficient number of wavelengths available so that all traffic at a source edge router can be routed to desired destination edge routers without blocking). Such an assumption makes sense since we are only interested in the average amount of bandwidth consumed per link by multicast traffic using different multicast schemes. More specifically, we inject a fixed amount of unicast traffic, and a fractional amount of multicast traffic into the network and measure, for instance, the average amount of multicast traffic carried on each link. By making the amount of multicast traffic injected to the network the same for different multicast schemes, the measured amount of multicast traffic per link represents the amount of bandwidth consumed by the multicast traffic per link, and thus, the larger the amount, the less efficient a multicast scheme is. For unicast traffic, Dijkstra’s algorithm [2] is employed to construct a routing table at each node. We assume that the aggregate data rate of unicast traffic from one edge router to all other edge routers is 1 unit per second (e.g. 100 Gbps when 1 unit = 100Gb). All unicast traffic has an equal distribution of the destination edge routers and thus the average data rate of the
Figure 2: An optical burst-switched WDM network. unicast traffic from one edge router to another is 1 =(j Ne j ?1) units per second. For multicast traffic, it is assumed that all core routers are multicast-capable and that every multicast session has one source, a pre-determined membership and the same data rate which is r < 1 units per second (even though in the analysis in Section 3 we used ri j for the data rate of the multicast session, here we will use a percentage of unicast traffic). In addition, for each multicast session, a source-specific multicast tree is constructed based on the shortest-path heuristic (SPH) [14]. One of the simulation parameters is the total number of multicast sessions j Ns j, whose source will be evenly distributed among all the edge routers. In other words, the average number of multicast sessions per edge router is j Ns j = j Ne j. In addition, for a given multicast session, every edge router (other than source itself) has a probability p of being a member, so the average number of members (edge routers) per multicast session is (j Ne j ?1) p (excluding the source). In addition to some of the parameters mentioned so far, there are a number of other parameters that affect the performance of the multicast schemes. The following are parameters examined and their default values. First, the number of core routers in the network is 40 and the average nodal degree of each core router is 4. The GB size is perhaps the most important parameter affecting the performance of the several multicast schemes and its default size in the simulation is 2 percent of the average payload of unicast traffic. For example, if the average data rate of the unicast traffic from one edge router to another is 1 Gbps and the burst assembly time is 500 µs, then the average payload length is 500 Kbits and the G is equal to 10 Kbits. The burst assembly time for unicast/multicast traffic is 500µs and 1000µs, respectively. In addition, the amount of multicast traffic as a percentage of that of unicast traffic is 5 percent by default in the simulation and the number of multicast sessions per source edge router is 15 on average. Finally, the average number of members per multicast session is 70 percent of the total number of edge routers (i.e. p is 0.7) and the number of edge routers per core router is 1. Note that in simulation of TS-MCAST using the OC strategy, the default criterion is tree sharing gain, and the default algorithm for ST construction is ST-MEMBER.
In this subsection, we present the simulation results. Whenever appropriate, we use the performance of S-MCAST as the base, that is, we determine the ratio of the average amount of multicast traffic per link using the two multicast schemes (MUCAST and TS-MCAST) over that using S-MCAST. A. Effect of the number of core routers Fig. 3 shows the effect of the network size (number of core routers) on the ratio of the average amount of multicast traffic per link obtained by M-UCAST and TS-MCAST to that obtained by the S-MCAST scheme. Note that in Fig. 3, the GB size is the same for all network sizes. As can be seen from Fig. 3, the relative performance of M-UCAST degrades quickly. This is because as the network size increases (from 10 to 70), which results in the increase of the average path length (from about 3.5 to 5.3 hops), the increase in the penalty due to multiple transmissions of the same burst far exceeds the benefit of sharing GB with unicast traffic. It is also clear that TSMCAST with the OC strategy shows the best performance, although all TS-MCAST schemes show a gradual decrease in the performance as the average path length (or number of hops) increases. If the number of hops is more than 5.5, there is no gain from tree sharing. This, as well as the results showing that the performance of TS-MCAST (EC) and TS-MCAST (SC) quickly approach that of S-MCAST, indicates that the degree of overlap among different multicast trees (sessions) reduces as the network size increases. 1.5 S-MCAST M-UCAST TS-MCAST(EC) TS-MCAST(SC) TS-MCAST(OC-link) TS-MCAST(OC-core) TS-MCAST(OC-member) TS-MCAST(OC-gain)
Ratio of multicast traffic per link to S-MCAST
1.4 1.3 1.2 1.1
GB size. In addition, Fig. 4 also shows the relative performance of TS-MCAST (OC) when applying different criteria. We observe that with a small GB size the performance difference is not much, but as the GB size increases, the performance difference becomes larger with the tree sharing gain being the best criterion. It is not difficult to envision that in a mesh network M-UCAST may outperform even TS-MCAST in terms of multicast traffic in some network conditions since no overhead due to GBs (and control packets) exists for multicast traffic. Note that the performance of EC/SC is not shown because it is very close to that of S-MCAST, and whenever appropriate hereafter.
Ratio of multicast traffic per link to S-MCAST
4.2 Numerical Results
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Figure 4: Effect of the GB size: comparison of several multicast schemes with different criteria. Fig. 5 shows the relative performance of TS-MCAST (OC) when applying different ST construction algorithms. STGREEDY performs the worst and the performance difference for other three algorithms is not much. However, the ST-BFS and ST-NEW schemes requires more processing load than STMEMBER to construct the shared tree. Note that the performance of ST-MEMBER is very close to that of ST-NEW with much less processing load.
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Figure 3: Effect of the number of core routers: comparison of several multicast schemes. B. Effect of the GB size Note that the GB size is perhaps the most important parameter affecting the performance of various multicast schemes. Fig. 4 shows the effect of the GB size. From Fig. 4, we observe that M-UCAST scheme performs the worst with a small GB size, and becomes better as the GB size increases. This is because as the GB size increases, the benefit of amortizing GB with both multicast and unicast traffic increases. This is also why the performance of TS-MCAST (OC) is improved with the
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Figure 5: Comparison of the shared tree construction algorithms. C. Effect of the membership size The efficiency of tree sharing depends on the average membership size of each multicast session. Fig. 6 shows the effect of the membership size on the performance of the multicast schemes. As can be seen from Fig. 6, the relative performance of all other multicast schemes to S-MCAST increases as the membership size increases since more multicast trees will be overlapped. The performance of the two tree sharing strategies,
EC and SC, improves drastically when the membership size is above 80 percent. TS-MCAST (OC) results in the best performance which consumes only a half of the bandwidth when compared to S-MCAST. However, M-UCAST scheme does not provide any improvement in performance over S-MCAST. 1.4
Ratio of multicast traffic per link to S-MCAST
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UCAST and TS-MCAST. The first one is natural and the other two are proposed to reduce the amount of GBs (and the number of control packets) per unit of multicast data. We have developed three tree sharing strategies, criteria and shared-tree (ST) construction algorithms for allowing multiple multicast sessions to mix data when assembling bursts, which are then delivered via a ST. We have evaluated the performance of various multicast schemes and our results suggest that the proposed TS-MCAST scheme performs better than S-MCAST in all cases as expected and that M-UCAST may perform better than S-MCAST under certain conditions.
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Figure 6: Effect of the membership size: comparison of several multicast schemes.
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In order to have tree sharing at an edge router i, the number of multicast sessions has to be larger than 2, i.e., jHi j 2. If there are more multicast sessions originating from an edge router i 2 Ne , it is likely that the efficiency of tree sharing will increase. Fig. 7 shows the relative performance of the multicast schemes as the total number of multicast sessions increases from 100 to 1000 (accordingly the average number of multicast sessions at each edge router increases from 2.5 to 25) as expected. Note that the M-UCAST scheme becomes more efficient with an increased number of multicast sessions since more multicast data share the GBs with unicast traffic. The TSMCAST scheme achieves the best performance and shows an improvement even with a small number of multicast sessions (e.g. 5 per edge router).
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1.7 1.6 1.5
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1 0.9 0.8 0.7 0
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Figure 7: Effect of the number of multicast sessions in the network: comparison of several multicast schemes.
5 Conclusions In this paper, we have studied three multicast schemes in optical burst-switched WDM networks, namely S-MCAST, M-
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