OPTIMIZATION OF IP NETWORKS IN VARIOUS HYBRID IGP/MPLS ROUTING SCHEMES

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1 th GI/ITG CONFERENCE ON MEASURING, MODELLING AND EVALUATION OF COMPUTER AND COMMUNICATION SYSTEMS 3rd POLISH-GERMAN TELETRAFFIC SYMPOSIUM OPTIMIZATION OF IP NETWORKS IN VARIOUS HYBRID IGP/MPLS ROUTING SCHEMES Eueung Mulyana, Ulrich Killat Department of Communication Networks, Hamburg University of Technology (TUHH) Address: BA IIA, Denickestrasse 7, 07 Hamburg, Germany Abstract This paper presents an offline traffic engineering (TE) approach in several hybrid IGP/MPLS environments. Though IGP (Interior Gateway Protocol) routing has proven its scalability and reliability, effective traffic engineering has been difficult to achieve in public IP networks because of the limited functional capabilities of conventional IP technologies. MPLS (Multi-Protocol Label Switching) on one hand enhances the possibility to engineer traffic on IP networks by allowing explicit routes, but on the other hand it suffers from the scalability problem. Hybrid IGP/MPLS approaches rely on IP native routing as much as possible and use MPLS only if necessary. In this work we propose a novel hybrid traffic engineering method based on genetic algorithms and investigate it in several IGP/MPLS routing schemes. This can be considered as an offline TE approach to handle long or medium-term traffic variations. In our approach the maximum number of hops as well as the maximum delay an (Label Switched Path) may take and the maximum number of s that can be installed in the network are treated as constraints. We apply our method to the German scientific network (G-WiN) for which a traffic matrix was randomly generated. We will show results for several hybrid routing schemes and compare them to the result of pure IGP routing. Keywords routing, traffic engineering, metaheuristics, evolutionary computation, IP networks, MPLS. INTRODUCTION As the candidate for the future multi-service communication platform, IP technology has to support a wide range of quality of service assertions as required by different types of applications. In this respect, it should also be able to provide the necessary management flexibility for controlling traffic distribution over the network in order to keep network resources within desired operational conditions,

2 Eueung Mulyana, Ulrich Killat which are necessary for guaranteeing quality of service. Hence,traffic engineering (TE), i.e. mapping traffic flows onto the existing physical network topology in the most effective way to accomplish desired operational objectives [3] has become an important issue in IP networks. There are several approaches for deploying TE in current IP networks e.g. by optimizing the parameters used for routing decisions, so that a better network performance will be obtained [4-0,3,,7], or by using explicit routing in an overlay model with ATM or Frame Relay technology. Recent developments in Multiprotocol Label Switching (MPLS) open new possibilities to address some of the limitations of IP systems concerning traffic engineering. In particular MPLS efficiently supports origin connection control through explicit label-switched paths (s). In an MPLS network, it is possible to explicitly specify one or several paths for each traffic demand from a source to a destination. By using a full mesh of s, the traffic matrix of source to destination flows in a network can easily be obtained. Because of scalability issues in a full mesh architecture and for seamless migration from the current IP network running IGP (Interior Gateway Protocol), the ISPs may adopt a tactical approach to MPLS, in which they create -tunnels only when necessary, for example to address specific congestion problems. Although this approach does not fully profit from the benefits of MPLS, it is an attractive alternative compared to the traditional TE method. In the context of pure MPLS networks, this approach can be thought of as a mixed TE approach using both connectionless and connection-oriented routing capability of MPLS routers. To the best of our knowledge, there are only a few publications that consider IGP/MPLS scenarios for offline traffic engineering [5,,4,5,8]. In [5] three different models are presented. In the first model (basic IGP shortcut) a packet will be forwarded to an if its destination is the tail-end of the. In the second model (IGP shortcut) all packets to nodes that are the tail-ends of s and to nodes that are downstream of the tail-end nodes will flow over those s. In the last model s are advertised in the IGP and used in the shortest path calculation as virtual interfaces. In these three models IGP and MPLS are working together in the same layer i.e. IGP routing is modified taking into account s. Recent work such as [,4] presents an overlay model where IGP and MPLS are working separately. In our investigations we consider three hybrid routing schemes, which will be discussed in detail in the next Section. In the following we first formulate the problem and introduce some notations. In Section 3 we present the genetic algorithm for solving the problem. After that in Section 4 we present some results and analysis for the network instance i.e. the German scientific network (G-WiN) from [] for which the traffic matrix was randomly generated.

3 Optimization of IP Networks in Various Hybrid IGP/MPLS Routing Schemes. PROBLEM FORMULATION Now we will formulate the problem in mathematical notation. A directed network G = ( N, A) is given, where N is the set of nodes representing the network s routers and A is the set of arcs representing the network s links. Each u, v link ( i, j) A has a capacity c i, j. Furthermore, we have a demand f for each pair ( u, v) N N, giving the demand to be carried from source u to destination v. A set of s is denoted by Π and indexed by k. An k consists of a loop-free node sequence ( hk, L, tk ) where h k, t k denote the head and tail node, u v respectively. A real variable l, i, j is associated with the load on link ( i, j) resulting u, v k from flow demand f along shortest path routing, and l i, j resulting from the k flow or the flow aggregate in k ( f ). Note that for simplicity, in this paper we do not consider ECMP (Equal Cost Multi-Path) in case that several shortest paths exist. It means that the ECMP feature is either disabled or using optimized metrics that result in a unique shortest path routing pattern [4,,7,7] (a) (b) (c) Figure Shortest path trees for node in three different scenarios : (a) Basic IGP shortcut, (b) IGP shortcut, (c) Overlay Consider the network in Figure with an originating from node and ending at node 7 (via nodes 4--8). In IGP/MPLS basic shortcut (BIS) scenario all packets arriving in node with destination of node 7, will be forwarded to the. Figure (a) shows the shortest path tree for node. It is obvious that in the BIS model only the node 7 is reachable via the, while the other flows will follow the normal path. In the IGP shortcut (IS) scenario all packets arriving in node with destination of node 7 as well as of its downstreams i.e. the nodes 8 and 9 will be forwarded to the. Figure (b) shows the shortest path tree for node in the IS model. Note that the link (5,7) will not be used for routing all traffic originating from node anymore. Figure (c) shows the shortest path tree for node in overlay (OV) model. In this model an is used only to route traffic from its

4 $ 45 $ $*$$$)#@(! DE $ $( 45 4 =.! α( = α*! α ( α = / $$ Π Π45 α $$ α > Π$ Π $!$$$ $ Π$ $! $% = + 45!( DE$% $!! =!* + 45! =! $! 45 =!; + &'5$'5$% H = $ I H $ =!> = +! $$ 45 = $ =!! $ ' =!&! $45 $%. ( ρ + J ΠJ/!? ρ ρ!!) ρ = =! # ( $ $7!? ρ =$ $ J Π J 45 $ $ 9 ρ $ -*!@* $$!B

5 Optimization of IP Networks in Various Hybrid IGP/MPLS Routing Schemes all such links for computing ρ max in (7). Furthermore, it is important to limit the number of hops and the delay for the s: max N k h +, k (8) δ k δ max, k (9) where N k denotes the set of nodes that belong to the k, δ k the delay introduced by k, h max the maximum allowable hop-count and δ max the maximum allowable delay. Having the traffic matrix, the metric values and a set of s, we can compute the load distribution on the network. Every solution has a quality measure according to (). Although a solution is feasible if ρ i, j or correspondingly ρ max, the optimization is performed with no constraints to force this condition, but we simply minimize the objective function. The desired result is a set of s which corresponds to the minimized cost function and to the certain performance parameters (e.g. hop-count, delay). Although here we treated the set W as a given set, the method presented can easily be integrated in a metric based optimization approach to address combined problems, for example: some s are created when the metric based approach fails to further improve network performance or vice versa. Since the equations ()-(5) depend on the set Π, which actually has to be determined, the formulation for IGP/MPLS routing presented here might only be suitable for the heuristic solving method to be addressed in Section 3. G-WiN Level G-WiN Level Figure The G-WiN network taken from [] 3. A GENETIC ALGORITHM FOR HYBRID IGP/MPLS TE Genetic algorithm (GA) is a population-based search method, that is adopted from nature. The population consists of individuals or chromosomes that represent solutions to the problem. Hence, the first design challenge of the GA is how to

6 Eueung Mulyana, Ulrich Killat encode a solution in terms of a chromosome. The next step is to use this encoding method to produce an initial population by randomly generating a suitable number of chromosomes. There are no standardized rules to decide how many chromosomes should be in the population. A size of around 30 up to 00 chromosomes is typically enough, because the number of chromosomes in the population is not directly correlated with the quality of the solutions. After generating this intial population, all chromosomes enter the evolution loop, consisting of selection and some processes to form new chromosomes. At the beginning of each iteration some vectors of high quality are selected as parent chromosomes, which by applying the genetic mechanisms crossover and mutation will hopefully produce some better solutions for the next generation. The least successful chromosomes of the previous iteration will be removed and then be substituted by the new ones. Applying the described processes in many iterations we continuously improve the average quality of the solution vectors until the termination condition is satisfied. The GA used for our hybrid IGP/MPLS problem, is basically a slight modification from that in [3]. Due to space reason, we will simply refer to [3] for the selection, crossover and mutation algorithm and in the following only discuss the encoding method, since it is the main difference to the GA in [3]. The traffic matrix f(u,v) u= u= u=3 u=4 index candidates v= - f(,) f(3,) f(4,) 4 D O v= f(,) - f(3,) f(4,) 3 C G N R v=3 f(,3) f(,3) - f(4,3) B F I K M Q v=4 f(,4) f(,4) f(3,4) - A E H J L P chr. val. val. val. val. val. val f(3,) f(,) f(4,) f(,3) f(,3) f(4,3) (a) (b) Figure 3 Chromosome representation (for the case of asymmetric s) Encoding. In order to apply a genetic algorithm to the problems defined in Section, in general a suitable encoding of possible solutions in a vector (i.e. chromosome) representation is needed. In our case a chromosome is represented by a set of l numbers y, y, K, y l, K, yl where y l is an integer and yl [ 0, cmax ]. Each position l is related to a certain node pair and the corresponding flow respectively. Figure 3-(a) shows a simple case of the association of the flows to each position in the chromosome. The set of flows or node pairs where an may be installed could simply be given or chosen on a random basis. If y l = 0, no for the node pair will be installed and the flow will be forwarded to the next hop node according to the shortest path computation. If y l 0, an for the node pair will be installed and the flow will be routed to a that. The set of possible s for

7 _ Optimization of IP Networks in Various Hybrid IGP/MPLS Routing Schemes u, v flow f associated with the position l is obtained by applying k-shortest paths algorithm with respect to the number of hops. This set will then be processed by removing the candidate, which is exactly the same as the original shortest path and those which do not fulfill the constraints (8) and (9). The remaining s are put in a list and the value of y l points directly to the position of the in the list. It is not necessary to sort the candidates according to some criteria (e.g. number of hops) because changing the value of y l is performed on a stochastic basis by the genetic operators (crossover and mutation). Figure 3-(b) shows an example of the representation of a chromosome together with its interpretation. The l cardinality of the candidates for each position (the constant c max ) could be different depending on the result of k-shortest paths algorithm or a given upper bound : where cgiven c l max Pl = cgiven if P c else l given (0) is the given upper-bound and P l a set of all possible s for the flow associated with the position l. Using this representation we limit the number of s to be installed in the network by L. 4. RESULTS AND DISCUSSION Table Some typical computation results (L=48) As mentioned in Section, for the following results we used the German research and scientific network G-WiN network shown in Figure. It consists of 7 nodes (0 level- nodes and 7 level- nodes) and 7 links. Each level- link has a transmission capacity of either.5 Gbps or 0 Gbps while each level- link has either Mbps or. 5 Gbps []. The traffic matrix is composed of

8 Eueung Mulyana, Ulrich Killat 70 flows and each of them was generated randomly in the interval [4,355] Mbps. The mean demand value is 34. Mbps and around 75% of the demands are below the value of 30 Mbps. From the topology point of view, the optimization problem for this network could be implemented in a hierarchical way i.e. we concentrate in optimizing the level- network using the aggregated flows from all level- nodes. In this way the complexity will be drastically reduced. For instance if we consider the total number of s for a pure MPLS network, with the hierarchical approach we could reduce the total number of s by 87%, with the assumption that each flow can not be split. However in order to keep the generality of the method in our experiments we used a plain approach and considered both, level- and level- networks, in the same layer. The genetic algorithm was set to terminate if it reached a value of 50 iterations or after 70 iterations with no more improvements. Delays are modelled statically and consist of three components: the propagation delay δ prop, the processing and serialization delay δ sp (link-rate dependent) and delay margin δ. The parameter setting was : constant c 000 (), maximum c max = hop-count h 4 (8), δ max = ms (9), δ sp = ms for Mbps lines and δ c = ms per link. In all experiments, the set of possible node pairs to be connected by an is given: an may be installed for every two level- nodes, which are not directly connected. Hence, the number of can be installed in the network is upper-bounded by L=48. The ECMP feature was disabled and the metric value was originally set inversely proportional to the link s capacity. Some typical results for each hybrid scheme are displayed in Table. Network Utilization. Traffic on level- links is not reroutable, thus for the optimization they were marked as unconsidered and ρ max in () was substituted cons with ρ max i.e. the maximum utilization on the level- network. Using the original cons IGP routing ρ max is about 77.5% for the link (5,). With optimization we can reduce the value to 4.4% depending on the routing schemes and trade-off factors (i.e. the number of s to be installed). In general, a better utilization value will be obtained by using more s. The best result is obtained for IS model. This could be the effect of the aggregation capability in the IS model. This aggregation capability is indicated by the parameter ω which denotes the number of different flows carried by an. The average value of ω tends to increase for the IS model. Network Delay. Improving network utilization in all cases will increase the average delay in the network, because to avoid hot-spot the flows often must follow longer routes. By adding delay and hop constraints as in (8) and (9) we try to trade between these performance parameters. Figure 4 shows the sourcedestination delay before and after optimization, for the case of L=48 (Table ). =

9 Optimization of IP Networks in Various Hybrid IGP/MPLS Routing Schemes Compared to the original IGP routing, the average delay at the end of the optimization increases by the value of about 0.05 ms (OV), 0. ms (BIS) and 0.54 ms (IS) respectively. These differences could again be seen as a direct consequence of each hybrid scheme. The number of demands, which are rerouted on longer paths for each scheme, increases from.4% (OV) to.08% (IS). An almost similar case happens for the routing hop-count: the average hop-count at the end of the optimization increases, at most by the value of about 0.8 (IS). However, in contrast to Figure 4 which indicates that some of demands are rerouted through paths with less delay, there are no demands rerouted with less hop-count i.e. they are rerouted either through paths with equal hop-count or through those with higher hop-count. Average =.874 ms Average =.7377 ms #flows (with delay increase)=.4 % #flows (decrease) = 0.8 % #flows (similar) = 98.3% Average = 7.37 ms #flows (increase) =.08 % Average =.9038 ms #flows (increase) = 5.7 % #flows (decrease) = % #flows (similar) = 93.3 % #flows (decrease) = 5.7 % #flows (similar) = 73. % Figure 4 Source destination delays for all schemes 5. SUMMARY AND CONCLUSION In this paper we have considered the problem designing s for hybrid IGP/MPLS traffic engineering schemes and proposed a novel approach based on genetic algorithms. Our results show that by constructing a few s we could improve network efficiency by reducing the maximum utilization in G-WiN

10 Eueung Mulyana, Ulrich Killat network scenario from 77% to 43% while simultaneously keeping the overall network performance in terms of delays and hop-count within an acceptable range. Although in IGP/MPLS schemes an ISP does not have all available features that MPLS may offer, for example a source destination flow measurement, the approach seems to be an attractive alternative and complementing the traditional offline traffic engineering by optimizing IGP link metric. BIBLIOGRAPHY [] Adler H. M., Neues im G-WiN, 37. DFN-Betriebstagung, November 00. [] Awduche D., MPLS and Traffic Engineering in IP Networks, IEEE Communication Magazine, December 999. [3] Awduche D., Chiu A., Elwalid A., Widjaja I., Xiao X., Overview and Principles of Internet Traffic Engineering, RFC 37, May 00. [4] Ben-Ameur W., Gourdin E., Liau B., Michel N., Determining Administrative Weigths for Efficient Operational Single-Path Routing Management, Proceedings of st PGTS, 000. [5] Ben-Ameur W., Michel N., Liau B., Geffard J., Gourdin E., Routing Strategies for IP Networks, Telektronikk Magazine, /3, 00. [] Bley A., Koch T., Integer Programming Approaches to Access and Backbone IP Network Planning, Preprint ZIB ZR-0-4, 00. [7] Bourquia N., Ben-Ameur W., Gourdin E., Tolla P., Optimal Shortest Path Routing for Internet Networks, Proceedings of st INOC, 003. [8] Fortz B., Thorup M., Internet Traffic Engineering by Optimizing OSPF Weights, Proceedings of IEEE Infocom, March 000. [9] Gajowniczek P., Pioro M., Szentesi A., Harmatos J., Solving an OSPF Routing Problem with Simulated Allocation, Proceedings of st PGTS, 000. [0] Karas P., Pioro M., Optimisation Problems Related to the Assignment of Administrative Weights in the IP Networks Routing Protocols, Proceedings of st PGTS, 000. [] Koehler S., Binzenhoefer A., MPLS Traffic Engineering in OSPF Networks A Combined Approach, ITC 8, September 003. [] Moy J., OSPF Version, RFC 38, April 998. [3] Mulyana E., Killat U., A Hybrid Genetic Algorithm Approach for OSPF Weight Setting Problem, Proceedings of nd PGTS, 00. [4] Riedl A., Optimized Routing Adaptation in IP Networks Utilizing OSPF and MPLS, Proceedings of IEEE ICC, May 003. [5] Shen N., Smit H., Calculating IGP Routes over TE Tunnels, Internet Draft, December 999. [] Staehle D., Koehler S., Kohlhaas U., Optimization of IP Routing by Link Cost Specification, Tech. Report, University of Wuerzburg, 000. [7] Thorup M., Roughan M., Avoiding Ties in Shortest Path First Routing,. [8] Wang Y., Zhang L., A Scalable Hybrid IP Network TE Approach, Internet Draft, June 00.

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