A Hierarchical and a Non-Hierarchical European Multi-Domain Reference network: Routing and Protection 1

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1 A Hierarchical and a Non-Hierarchical European Multi-Domain Reference network: Routing and Protection 1 Diána Meskó, Gábor Viola and Tibor Cinkler, Member, IEEE Budapest University of Technology and Economics, H-1117 Budapest, Hungary {mesko viola cinkler}@tmit.bme.hu Abstract Routing, TE & Resilience are gaining on their importance in heterogeneous networks. For testing network design and configuration tools, as well as for simulating routing algorithms and protocols in a realistic multidomain reference environment a common reference network is necessary to estimate and compare the known and novel methods. Until now there have been three to five well known single domain reference networks. The most widely used one is the NSFnet [1] the North-American academic network. The other two often used reference networks are the European COST 239 [2] and the LION/COST 266 [2] fictive Pan-European Reference Networks. Two national networks are also often used the Italian [3] and the German [4] one. However, all these networks consist of a single domain only. To our knowledge there is no reference network so far capable of taking into account the multi-domain and multi-provider character of modern networks. The aim of this paper is to fill this gap. We propose a non-hierarchical reference network (e1-net-nh) and a hierarchical one (e1-net-h) and compare them from the point of view of blocking ratio and path length distribution for two cases, namely, with no protection at all and with dedicated protection. 1 Introduction In modern networks, particularly in transport networks it is not enough to test network design and configuration tools, or routing algorithms and protocols in a single domain environment because there are in practice typically multiple interconnected domains operated by different operators. These domains typically cover different areas, but they may overlap as well. In all cases they are interconnected directly or through intermediate domains. Therefore, a multidomain reference network is needed. In a multidomain environment there are two or more domains, typically administrative domains, where the information spread between the domains is reduced compared to that provided within the domains. This information characterizes the network state to be used for optimal routing. There are two reasons for reduced information flooding. First, as the network grows the amount of information to be spread grows by square (quadratically) as the number of nodes grows. Second, the operators in a multi-provider environment do not want to provide all their strategic information to their competitors. Our goal was to define a Multi-Domain Reference Network, as realistic as possible, for evaluation and benchmarking purposes. 2 Physical Topology of the e1- net 2.1 Tool for setting up the topology (DomainMaker) To collect input data for the e1-net we have created a Java GUI (Graphical User Interface). We will refer to it as DomainMaker. It is a further developed version of the GraphMaker [5]. It saves the graphically assembled national networks into an XML file to be merged with other national networks into the European Multi-Domain Reference Network, e1-net. It is important that multiple, even partially overlapping networks are supported to better model the realistic network structures [6]. The link and node types, the capacities as well as the traffic statistics (both, static (traffic matrix) and dy- 1 The work reported in this paper has been carried out within the European FP6 project NoE e-photon/one and its second phase NoE e-photon/one+ ( and

2 namic (traffic pattern)) are generally not provided by the network providers, therefore, we have generated them based on estimations. For this purpose the methods and results obtained in projects COST 266, IST LION and IP NOBEL have been used [2, 7, 8]. 2.2 Composing the topology With the DomainMaker a network topology consisting of several domains has been created. The topology has been collected in different ways. On the one hand it has been looked up on the Internet, e.g. the national academic network (NREN) topologies. On the other hand we contacted partners involved in e-photon/one and other European projects, who were supposed to have access to this information. If there was not just an academic network topology but other network topologies as well, then for that country more than one topology has been collected. In some cases the topology was just indirectly defined. In our European Reference Network we have used one topology for each country only. All the topologies are available at: both, separately domain-by-domain and jointly as a huge Multi-Domain network. 2.3 Estimating the traffic The capacity of the links was given for a few domains only, therefore, we have decided to estimate all of them. For estimating the capacities a traffic matrix was needed. We have chosen the estimating mechanism, which was presented in the Extended Final Report of COST Action 266 [2]. Using the information collected in COST 266 we have first estimated the traffic between certain countries. The total traffic between two countries has been determined by taking into account the used bandwidth per user, the average time of usage per day and the total number of users. This way estimates for the amount of voice, transaction data and Internet traffic in 22 (reference year of the traffic studies) were obtained [9]. Using the above mentioned method the traffic matrix has been estimated. The traffic data of some countries was missing, therefore, we have estimated them from traffics of other countries, which have similar level of technology and economy scaled by the population of these countries. The international traffic of each country was given from it to all other countries, which was assumed to be symmetrical, i.e., equal in both directions. The ratio of the amount of the international to the national traffics was 1:1. The traffic matrix of the European Reference Network e1-net is available at as well. Country After the aggregation man/ # of node nodes [1 ] Population [1 ] Before the aggregation man/ # of node nodes [1 ] 2.4 The capacity estimation Austria Belgium Croatia Czech France Germany Hungary Italy Netherlands Norway & Sweden Poland Portugal Slovakia Slovenia Spain Switzerland UK Table. 2 Data of the Aggregation First the link capacities were set to infinite. In practice it was large enough to accommodate all the traffic along their shortest paths. All the static traffic was routed one-by-one. When all were routed the capacity usage was summed for each link, and then set to be the link capacity. 3 The Non-Hierarchical Reference Network (e1-net-nh) There were two problems with the e1-net. On the one hand the number of the nodes for a country was very diverse from country to country. On the other hand some domains did not have enough links to the other domains. The formerly diverse values of the number of inhabitants per node for different nodes were modified to get a more homogeneous figure. Table 1 shows the classification for this homogenisation. Population in a country (million) The scale of the aggregation 1 1 node per 1 million person node per 1,5 million person node per 2 million person 5 1 node per 3 million person Table. 1 The Scale of the Aggregation Table 2 shows the countries, the population of these countries in 23, the number of the nodes in the countries and the number of the citizens per node before and after the aggregation.

3 If there were more nodes than extrapolated the graphs were aggregated. The considerations for aggregation were as follows: If only one or two links are adjacent to a given node then it can be merged with the neighbouring node (or nodes) according to the meta-mesh concept [1]. If there is a node of degree of three that we want to remove to reduce the topology then we have to connect its neighbouring nodes by a link if they were not yet connected. This will yield a ring and the middle node can be erased with all the links adjacent to it. These two operations were repeated until the number of the nodes was satisfactorily reduced. If there were fewer nodes in a country / domain than estimated, then the cities with large population were added to the network. When linking these new nodes the previous aggregation considerations were taken into account. For the capacity estimation of the completed network the already described mechanism has been used. However, these capacity values rarely meet the typical values. Therefore, the estimated capacities have been rounded up. The SDH/SONET transmission technology gives the discrete values for the estimated capacity values of fine granularity. The aggregated network topology named e1-net-a is also available at: Although this network topology is the most realistic representation of the data given by the service providers there were some missing international / inter-domain links. Therefore, the next step was to introduce new international links, a new capacity estimation and rounding up. Figure 1 shows this European Non- Hierarchical Reference Network topology, e1-net-nh. Figures showing all the network topologies (e1-net, e1-net-a, e1-net-nh and e1-net-h) and the xml files are available at: Fig. 2 The e1-net-nh 4 The Hierarchical Reference Network (e1-net-h) In testing and simulating routing algorithms and protocols having a separate higher level network dedicated to interconnecting the certain domains is in practice more realistic. Therefore, a hierarchical network topology that is based on the non-hierarchical one has been also defined. The concept was to make a higher level domain which consists only of interdomain links. In this case to set up an international / inter-domain connection (i.e., between the nodes of two different domains) has affected always three domains: That of the source node, that of the destination node and the interconnecting higher level domain. For this network topology called e1-net-h (Figure 2) a new capacity estimation has been made as well as a capacity rounding up. Fig. 2 The e1-net-h

4 5 Comparing The Non- Hierarchical and the Hierarchical Network From Routing and Protection Point of View In this section as an illustration we compare the nonhierarchical network to the hierarchical one from the point of view of routing without and with protection. The evaluation criteria are average blocking experienced and the histogram of path-lengths. The parameters of the networks are shown in Table 3. The hierarchical network consists of slightly more nodes and links since nodes and links used for the backbone were counted here as well. The density (average degree) of the hierarchical network is also slightly higher, however the number of edges (links) that leave the domains is slightly lower (Number of interfaces). e1-net-nh e1-net-h Number of the nodes Number of the edges Number of the interfaces Average degree 3,746 3,787 Table. 3 Parameter of the Two Reference Networks In each simulation a traffic pattern of at least 75 demands was routed to obtain results of satisfactory confidence level. We have generated demands as a Poisson process with average arrival intensity of,264 per time unit, and with exponential holding time of 4 time units on average. The average blocking of demands was 6% and 9% for the case with no protection while 5% and 49% for the case with protection for the non-hierarchical and hierarchical networks respectively e1-net-nh with and without dedicated protection e1-net-nh w ithout protection e1-net-nh w ith dedicated protection Fig. 3 Path length histogram for the case with and without protection for the e1-net-nh (the nonhierarchical network). Blocking was 6% for the case without protection and it was 5% in the case with protection. Figures 3 and 4 show the distribution of the path lengths for the case without and with protection for the two reference networks. It can be well seen that for both networks the protection resulted in significantly longer paths, i.e., more links were used for working plus protection paths, than for protection paths only. This was what we expected. However, the interesting thing is that the peek of the distribution of path lengths is about the same for the two networks with smaller deviation (narrower histogram) in case of the hierarchical network. The reason is that due to the backbone domain the distance between the nodes of different domains becomes more predictable. This can be considered as improved distance-fairness that leads to improved blocking fairness between different node-pairs. The drawback is the slightly increased blocking, since there will be less alternative paths, i.e. the backbone will be the bottleneck that will be traversed by each inter-domain demand. It can be well seen from the shape of the histograms that the total length of the working plus protection paths is roughly the double of that for the case with no protection e1-net-h with and without dedicated protection e1-net-h w ithout protection e1-net-h with dedicated protection Fig. 4 Path length histogram for the case with and network). Blocking was 9% for the case without protection and it was 49% in the case with protection. Unfortunately, the very high blocking deteriorates our statistics. Therefore, we have carried out our simulations assuming capacities large enough to avoid not only blocking, but also congestions that force demands taking shorter paths. These results can be seen in Figures 5 and 6. Figures 5 and 6 show the histogram of path lengths for the case of no protection, and for the case with protection, this time, however not the sum for working and protection paths, but separately. Figure 5 shows results for the non-hierarchical, while Figure 6 for the hierarchical network. For both networks it can be seen that the working paths have length distribution with negligible differences that is caused by the aggregation of domain information while performing multi-domain routing. Compared to working paths for both networks the histogram of the protection paths is slightly shifted to left, i.e., the protection paths are slightly longer. If we compare Figures 5 and 6 it can be seen, that the length of working versus the protection paths differs slightly more for the hierarchical network (Figure 6), and deviation from the mean value is smaller than for the non-hierarchical one. This means that in the case of hierarchical networks we have more paths around the average length value, that can be explained by the fact, that using the backbone network the nodes are virtually closer to each other, i.e., using one intermediate domain only (the backbone one) they nodes can reach each-other. On the other hand, their protection paths will become longer, since there are fewer links between domains, and therefore fewer disjoint paths

5 between the end-nodes that causes choosing longer second paths (protection paths) e1-net-nh with and without protection e1-net-nh w ithout protection e1-net-nh w orking path w ith protection e1-net-nh protection path w ith protection Fig. 5 Path length histogram for the case with and network) e1-net-h with and without protection e1-net-h w ithout protection e1-net-h w orking path w ith protection e1-net-h protection path w ith protection Fig. 6 Path length histogram for the case with and network) 6 Conclusion Although there are four networks described, the hierarchical (e1-net-h) and the non-hierarchical (e1- net-nh) network topologies are recommended for use as reference for evaluations. Choice between these two networks should depend on the problem studied. As an illustration we have compared these two networks for routing with no protection and with dedicated protection. The hierarchical one is simpler to manage, it has routes of more predictable length that leads to better fairness, however, it has slightly higher blocking and lower availability due to the lower number of alternative paths, and also it requires more nodes and links. All additional information and new results will be available at András Pataki for carrying out a part of the simulations and to András Kern for building the graphical interface and scripts for collecting input and editing the reference networks. The authors are also grateful to all the partners that provided data on their national networks. 8 References [1] NSFNET The National Science Foundation Network. Available: [2] COST European Cooperation in the field of Scientific and Technical Research. Available: z/dpt24/cost266/docs/cost266_extended_fin al_report.pdf [3] The Italian Academic & Research Network. Available: [4] Germany s National Research and Education Network. Available: [5] Blue Marsh Softworks. Available: [6] EU FP6 NoE e-photon / One DomainMaker homepage. Available: [7] Next Generation Optical Network for Broadband European Leadership. Available: [8] OPTMIST, IST LION Available: [9] R. Inkret, A. Kuchar, B. Mikac: Extended Final Report of COST Action 266; Advanced Infrastructure for Photonic Networks, 23, pp [1] W. D. Grover: Mesh-Based Survivable Networks, 24, pp , ISBN X 7 Acknowledgment The authors are grateful to János Szigeti for writing the simulator for routing in multi-domain networks, to