An Offline Hybrid IGP/MPLS Traffic Engineering Approach under LSP Constraints

Transcription

1 An Offline Hybrid IGP/MPLS Traffic Engineering Approach under LSP Constraints Eueung Mulyana, Ulrich Killat Department of Communication Networks, Technical University Hamburg-Harburg Address : BA IIA, Denickestrasse 17, 1071 Hamburg Phone: , fax : fmulyana,killatg@tu-harburg.de Abstract MPLS (Multi-Protocol Label Switching) enhances the possibility to engineer the traffic on IP networks by allowing explicit routes. Though IGP (Interior Gateway Protocol) routing has proven its scalability and reliability, effective traffic engineering (TE) has been difficult to achieve in public IP networks, because of the limited functional capabilities of conventional IP technologies. Without MPLS there are in general two possibilities to perform TE in IP networks: either by improving the existing routing protocols, or by optimizing the parameters used for routing decisions in order to obtain better load distributions. In this work we propose a novel hybrid IGP/MPLS traffic engineering method based on genetic algorithms, which can be considered as an offline TE approach to handle long or medium-term traffic variations in the range of days, weeks or months. In our approach the maximum number of hops an LSP may take and the number of LSPs which are applied solely to improve the routing performance, are treated as constraints due to delay considerations and the complexity of management. We apply our method to the German scientific network (B-WiN) for which a traffic matrix is available and also to some other networks with a simple demand model. We will show results comparing this hybrid IGP/MPLS routing scenario with the result of pure IGP routing and that of a full mesh MPLS with and without flow splitting. keywords : routing, traffic engineering, metaheuristics, evolutionary computation, IP networks, MPLS 1 Introduction Due to rapid growth of the Internet and due to increasing requirements for service quality, some efforts have been invested by internet service providers(isps), to build a more scalable network architecture or expanding network infrastructure and capacity. Another important issue is traffic engineering (TE), that could give ISPs some degree of control of the traffic distributed over the network. In practice, TE means mapping traffic flows onto the existing physical network topology in the most effective way to accomplish desired operational objectives. 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 [, 7, 8, 9, 15, 19], 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 (LSPs). In 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 LSPs, 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 LSP-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. To the best of our knowledge, there are only a few works that consider IGP/MPLS scenarios for offline traffic engineering [1, 16, 19]. In [19] three different models are presented. In the first model (basic IGP shortcut) a packet will be forwarded to an LSP if its destination is the tail-end of the LSP. In the second model (IGP shortcut) all packets to nodes that are the tail-ends of LSPs and to nodes that are downstream of the tail-end nodes will flow over those LSPs. In the last model LSPs 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 LSPs. Recent work such as [1, 16] presents an overlay model where IGP and MPLS are working separately. Although from algorithmic point of view the overlay model is less complicated and more predictable, in the sense that an LSP is used only to route traffic from its source to its destination, in this work we consider the basic IGP shortcut scenario and leave the other scenarios for future investigations. In the following we first formulate the problem and introduce some notations. In Section 3 we discuss the genetic algorithm for solving the problem. After that in Section 4 we present some results for the core network of the German scientific

2 network (B-WiN) for which the traffic matrix was available and also for some other networks with a simple demand model. Problem Formulation IGP/MPLS Routing. 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 link (i; j) A has a capacity c. Furthermore, we have a demand f u;v for each pair (u; v) N N, giving the demand to be carried from source u to destination v. A set of LSPs is denoted by Π and indexed by k. An LSP k consists of a loop-free node sequence (h k ; :::; t k ) where h k, t k denote the head and tail node, respectively. A real variable l u;v is associated with the load on link (i; j) resulting from flow demand f u;v along shortest path routing, and l LSP k ij resulting from the flow aggregate in LSP k (f LSPk ). 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 LSP 1 f1,6 f,5 f,6 Figure 1: Basic shortcut IGP/MPLS scenario Figure : The B-WiN network (node 11 is a pseudonode representing IP-gate for international traffic) Consider the network in Figure 1 with a tunnel originating from node and ending at node 6 (via node 4). In IGP/MPLS basic shortcut scenario all packets arrive in node with destination of node 6, e.g. the flows f ;6 and f 1;6, will be forwarded to this tunnel. To route the flow f 1;6 router at node 1 computes the shortest path which is the node sequence (1 6), so that the flow will be forwarded to node. Node evaluates the destination of the flow, and notices that it is the same as the tail-end of the tunnel, so that it will be routed to the tunnel. In contrast for the flow f ;5, the router at node computes the shortest path, which is the node sequence ( 6 5). It identifies that there are no tunnels ending at node 5, so the flow will be forwarded to node 6. Let A u;v = f(ff1;ff ); :::; (ff r 1;ff r);:::;(ffs 1;ff s)g, 8r f; :::; sg be defined as a set of links that belong to the shortest path for the flow f u;v, N T = ft1;:::;t k ;:::;t jπj g, 8k f1; :::; jπjg as a set of all tail nodes in Π and Nu;v int = fff ;:::;ff s 1g as the set of all intermediate nodes in the shortest path sequence for flow f u;v. So the total load on the link (i; j) can be computed as follows: where l u;v = 8 >< >: f u;v l = uv l u;v + k l LSP k (1) if (i; j) A u;v and v= N T ;or if (i; j) A u;v and v = t k N T and h k = Nu;v int ;or if (i; j) A u;v and v = t k N T and h k = ff p N int u;v for i = ff r 1, j = ff r, 8r f; :::; pg l LSP k = 8 < : f LSPk if (i; j) belongs to the LSP k () (3)

3 Note that f LSPk is defined as the flow aggregate in LSP k i.e. f LSPk f u;v for u = h k and v = t k. For a given traffic matrix F = (f u;v); 8(u; v) N N and a set of metrics W = (w ); 8(i; j) A, the problem is then to find a set of LSPs to increase the network performance and can be formulated as : min f c 1 ρ max + jπj g (4) ρ» ρmax, 8(i; j) A (5) l where ρ = c is the utilization of the link (i; j). A constant c 1 is used to trade between these two components. With the Eq. 4 we prefer solutions with a low ρ max, which implies that the network is better utilized and a low jπj, because the number of LSPs is directly correlated with the management complexity. Furthermore, in some cases it is important to limit the number of hops for the LSP to avoid long delay that might be introduced by a long LSP: jn LSPk j»h max +1, 8k (6) where N LSPk denotes the set of nodes that belong to the LSP k and h max the maximum allowable hop-count. Having the traffic matrix, the metrics and a set of LSPs, we can compute the load distribution on the network. Every solution has a quality measure according to Eq. 4. Although a solution is feasible if ρ» 1 or correspondingly ρmax» 1, 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 LSPs which corresponds to the minimized cost function and to the certain performance parameters. Although here we treated the set W as a given set, the method presented can be easily integrated to a metric based optimization approach to address combined problems, for example : some LSPs are created when the metric based approach fails to further improve network performance or vice versa. Note that the formulation for IGP/MPLS routing presented here is intended for the heuristic solving method will be presented in Section 3. General Routing Problem (MPLS with flow splitting). For comparison we will now discuss the optimal routing from a socalled general routing problem (GRP) []. In this case, there are no limitations on how flows can be distributed along the paths from source to destination, so that it can be formulated and solved in polynomial time. ffi u;n + min fc 1 ρ max + 1 8(m;n)A uv jaj ij uv f u;v c g, 8(i; j) A (7) f u;v c» ρ max, 8(i; j) A (8) m;n = ffi n;v + 0» 8(n;m)A Equation 7 is the objective function to minimize ρ max and the average utilization ρ = 1 jej n;m, 8f u;v; 8n N (9)» 1, 8fu;v, 8(i; j) A (10) P P ij ρ, where ρ = f u;v uv c, 8(i; j) A. Since the objective functions in Eq. 4 and Eq. 7 are different, the result from GRP is theoretically not the lower bound from IGP/MPLS routing. But by choosing a quite high value of c 1 in both equations (e.g. 1000), we could hope that GRP will give an approximate lower bound. Eq. 9 describes flow conservation constraints that ensure the desired traffic flow to be routed from source to destination. The Kronecker delta ffi is defined as having the value one when i = j and zero when i 6= j. is associated with the fraction of the fu;v that flows on the link (i; j). Furthermore, it might be necessary to discard some nodes in Eq. 9, which introduce long delays or represent external destination networks, in case of n 6= u and n 6= v to avoid transit of the traffic on those node (e.g. the node 11 in Figure ). The variable MPLS (full mesh - without flow splitting). For the second comparison, we will change the contraint in Eq. 10 to binary condition, which implies that each flow f u;v can not be split anymore. ρ 1 if the flow fu;v is routed on the link (i; = j) (11) Our experiments with CPLE 7.5 show that, although the objective function 7 implicitly contains no-loop condition for the optimal solution, explicit no-loop constraints are needed to speed-up the computation. 8(n;m)A n;m» 1, 8n N, 8f u;v (1)

4 8(m;n)A m;n» 1, 8n N, 8f u;v (13) Further computation cost can be saved by using symmetrical LSPs, so that half of integer variables can be deleted. = xv;u j;i, 8(i; j) A, 8f u;v (14) A Demand Model. Obtaining a realistic traffic matrix is quite hard, because network operators have many reasons to keep it for themselves. To test our implementation we use the B-WiN network from ERNANI project [13] for which a traffic matrix and the set of weights are available. The B-WiN network was the German research and scientific network, which is now replaced by the more advance network G-WiN, for which unfortunately no related informations are publicly available. Because we use also the G-WiN [10] and the SURFnet network (the scientific network in the Netherlands) [18], we will now introduce a simple demand model, which is a formal and a generalized version of the model proposed in [17]. The model consists of two parts. The first one (Eq. 15) is for local traffic i.e. traffic between nodes in the network. And the second one (Eq. 16 and 17) is for traffic to(from) outside networks. (f t u;v) down = (f t u;v) up = ( ( f local u;v = random [c local min c t Φt down if u = e t jnj i Enwc t and v Ei t c t Φt up if v = e t jnj i Enwc t and u Ei t, c local max ] (15) c t = random [1 ff t, 1+ff t ] (18) where T is a set of outside traffic-types, Φ t down is the approximate downstream traffic volume of type t i.e. traffic from outside network, Φ t up is the approximate upstream traffic volume of type t i.e. traffic to outside network, Enwc t =(e t i) is a set of nodes with connection to outside network for traffic type t, Ei t is a set of nodes whose traffic is routed through the node e t i. Thus the traffic matrix can be formulated as follows : f u;v = f local u;v + 8tT 3 A Genetic Algorithm for Hybrid IGP/MPLS TE (16) (17) f(f t u;v) down +(f t u;v) upg, 8(u; v) N N (19) Genetic algorithm (GA) is a population-based search method, that is adopted from the nature. The population consists of individuals or chromosomes that represent solutions to the problem. So the first design challenge of the GA is how to 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. The size of around 50 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 continously improve the average quality of the solution vectors until the exit condition is satisfied. This exit condition is ideal if the best fitness found matches the global optimum of the objective function. As for most cases we do not know this global optimum, the program will terminate based on a predefined number of iterations or when for a certain number of iterations there are no more improvements. In the following we will dicuss the method in more detail. 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 numbers hy 1;y ; ;y l ; ;y Li where yl is an integer and y l [0;c max]. Each position l is correlated with a certain flow from the traffic matrix, so that when y l =0the flow will be routed according to shortest path computation. If y l 6=0, the flow will be routed with a certain LSP. The constant c max is defined as follows: c max = 8 < : jp l j c given if jp l j»c given where c given is the given upper-bound and P l a set of all possible LSPs for the flow associated with the position l. To select the flows, we first route all flows according to shortest path computation and select the flows for each position from the links with else (0)

5 high utilization. The set of possible LSPs for flow f u;v is obtained by applying Dijkstra s algorithm to the modified network topology : we will cut certain links on the currently available paths to find a new path. If the new path does not exist, then it will be added to the list. This method is repeated several times to obtain more LSP candidates. Selection. All chromosomes will be selected according to their fitness. In our case we want a fitness value as small as possible. There are two selection mechanisms i.e. to select parent chromosomes for a new generation and to remove some of bad chromosomes from the current population. For the first task we implement a so-called rank selection to make the probability to be selected a little bit more balanced for all chromosomes in the population. We first rank the population and then every chromosome receives a probability value from this ranking: the probability value is measured relative to the probability value of the last (worst) chromosome i.e. the last but one will have twice that probability etc. Of course the total of these probabilities must equal one. Hence these probability values can be mapped on corresponding non overlapping intervals in the range [0; 1] and a randomly chosen number in this interval is used to select a chromosome. For the second task we simply sort the chromosomes according to their fitness from good to bad and then remove some of the least performing chromosomes. Crossover and Mutation. In genetic algorithms there are two standard operators to produce new individuals. The first one is called crossover. In general the production of new chromosomes by crossover consists of the combination of two parent chromosomes of the old population. This means that all offspring s genes will be inherited either from the first parent or from the second one. The main goal of this mechanism is to get better solutions. The second method is called mutation and changes the genes randomly. Its main goal is to lower the danger of getting stuck in local optima. Of course the implementation of these operators may vary depending on the problem and encoding method of the solutions. In our implementation, crossover is performed by generating a random real number, that is randomly distributed in the interval [0; 1]. If the number is lower than 50% the gene yl O1 of offspring 1 will be inherited from the gene yl P 1 from parent 1. If the number is more than 50% the gene yl O1 will be inherited from the gene yl P. The complementary rule exists for the gene yl O of offspring. For mutation we generate another real number; if this number is lower than a probability of mutation p mut the offspring s genes will be arbitrarily mutated. 4 Results For the following results we set the constant c 1 = 6:10 4 (Eq. 4) for the B-WiN and G-WiN networks, and c 1 = 10 5 for the SURFnet5 network. the maximum hop-count h max =4(Eq. 6), the length of the chromosomes ( = the maximum LSPs number) L = 60 for the B-WiN and G-WiN networks and L = 100 for the SURFnet5 network. The search process is terminated if there are no more improvements after 300 iterations. All results for GRP and MPLS scenarios are computed with CPLE 7.5 MIP optimizer. The B-WiN network topology is shown in Figure. Unfortunately (for space reasons) the traffic matrix, link capacities and metrics could not be presented here. The G-WiN and SURFnet5 networks topologies are taken from [10] and [18] respectively. The demands for these G-WiN and SURFnet5 networks are generated according to Eq. 19 and for shortest path computation hop-count metric (all weights equal 1) is used. Fitness Convergence Convergence of Max. Utilization Conv. of Total Number of LSPs Fitness Average Fitness Best Fitness Maximum Utilization Average Max. Util. Best Max. Util. Total LSPs Average Total LSPs Best Total LSPs Iterations Iterations Iterations Figure 3: The convergence characteristics of the GA for the B-WiN network Convergence. Figure 3 shows the convergence characteristic of the fitness, ρ max, and jπj for the B-WiN network. There are almost no differences between the result for the fitness and that for the ρ max. In contrast to that, the result for jπj shows more dynamics. It can also happen that jπj best < jπj average. This is the influence of the constant c 1, that was set to 6:10 4. It means that the importance ratio for ρ max and jπj is 1000:1. With this setting, the algorithm will first prefer to search a good ρ max.

6 B-WiN G-WiN SURFnet5 IGP IGP/MPLS MPLS GRP IGP IGP/MPLS MPLS GRP IGP IGP/MPLS MPLS GRP # nodes # links # demands # Variables # Constraints ρ max ρ # LSP Table 1: Some network parameters and the results (all networks) Network Utilization. For the B-WiN network the maximum utilization ρ max and correspondingly the maximum scale factor = 1 ρ max found in IGP/MPLS scenario are quite close to the approximate lower bounds from general routing problem (GRP) and MPLS (the exact parameters are shown in Table 1). But they are also not so far from the ρ max and for IGP case (compared to the results from two other networks). One possible reason for this is, that the metrics used for the B-WiN network are already optimized, while those for two other networks are not (hop-count metric). Total Number of LSPs. Table 1 shows that the total number of LSPs varies from about 1% for the SURFnet5 network (compared to the MPLS case) to 7%(G-WiN). Our early investigations with several runs show that a factor of about 5% can be achieved on average. It can be improved by increasing h max. 5 Conclusion In this paper we have considered the problem designing LSPs for hybrid IGP/MPLS traffic engineering scenario and proposed a novel approach based on genetic algorithms. Although in IGP/MPLS schemes an ISP does not have all features that MPLS may offer, for example a source destination flow measurement, the approach seems to be an attractive alternative and complement for the traditional offline traffic engineering by optimizing IGP metrics. Our early results show that the performance obtained by constructing a few LSPs is quite close to the performance obtained by a full-mesh LSPs configuration. Surely it should be further investigated in particular by using larger networks. This issue, the influence of several parameters and statistical characteristics of the method are subject of our future research. References [1] A. Riedl. Optimized Routing Adaptation in IP Networks Utilizing OSPF and MPLS. In IEEE ICC, May 003. [] B. Fortz, M. Thorup. Internet Traffic Engineering by Optimizing OSPF Weights. In IEEE Infocom, March 000. [3] Cisco Systems. Advanced Topics in MPLS-TE Deployment. White Paper, [4] D. Awduche. MPLS and Traffic Engineering in IP Networks. IEEE Communications Magazine, December [5] D. Awduche, A. Chiu, A. Elwalid, I. Widjaja,. iao. Overview and Principles of Internet Traffic Engineering. RFC 37, May 00. [6] D. Beckmann. Algorithmen zur Planung und Optimierung moderner Kommunikationsnetze, Dissertation, Technical University Hamburg-Harburg, 001. [7] D. Staehle, S. Koehler, U. Kohlhaas. Optimization of IP Routing by Link Cost Specification. Technical Report, University of Wuerzburg, 000. [8] E. Gourdin. Optimizing Internet Networks. OR/MS Today, Vol. 8, Nr., April 001. [9] E. Mulyana, U. Killat. A Hybrid Genetic Algorithm Approach for OSPF Weight Setting Problem. In Second Polish-German Teletraffic Symposium PGTS, 00. [10] H. M. Adler. Neues im G-WiN. In 37. DFN-Betriebstagung, November 00. [11] J. Boyle, V. Gill, A. Hannan, D. Cooper, D. Awduche, B. Christian, W.S. Lai. Applicability Statement for Traffic Engineering with MPLS. RFC 3346, August 00. [1] Juniper Networks. Internet Software Configuration Guide, MPLS Applications. Release 5.6, [13] K. Below, C. Schwill. Erhoehung des Nutzungsgrades eines ATM Netzes for den Wissenschaftsbereich (ERNANI). Abschlussbericht, March 000. [14] N. Shen, H. Smit. Calculating IGP routes over Traffic Engineering Tunnels. Internet Draft, December [15] P. Karas, M. Pioro. Optimisation Problems Related to the Assignment of Administrative Weights in the IP Networks Routing Protocols. In First Polish-German Teletraffic Symposium PGTS, 000. [16] S. Koehler, A. Binzenhoefer. MPLS Traffic Engineering in OSPF Networks - A Combined Approach. In ITC 18, August- September 003. [17] S. Schnitter, G. Hasslinger. Heuristic Solutions to the LSP-Design for MPLS Traffic Engineering. In NETWORKS 00, 00. [18] SURFnet Network. [19] W. Ben-Ameur, N. Michel, B. Liau, J. Geffard, E. Gourdin. Routing Strategies for IP-Networks. Telektronikk Magazine, /3 001.