Routing Optimization in IP/MPLS Networks under Per-ClassOver-Provisioning Constraints

Eueung Mulyana, Ulrich Killat

Department of Communication Networks, Hamburg University of Technology (TUHH)

Address : BA IIA, Denickestrasse 17, 21071 Hamburg

Phone: +49-40-42878-2925, fax : +49-40-42878-2941

Email:�mulyana�killat�@tuhh.de

Abstract

MPLS (Multi-Protocol Label Switching) and DiffServ (Differentiated Services) provide means to implement various QoS typesin IP networks. While MPLS enhances the classical IP networks by supporting efficient connection control through explicitlabel-switched paths (LSPs), DiffServ allows differentiated QoS parameters commitments to be supported on the same IP back-bone for different classes of service. In this paper, we address an offline multi-class traffic engineering problem in IP/MPLSnetworks, where each class of traffic for a certain source destination pair is routed through a unique LSP, subject to per-classover-provisioning constraints. We introduce a heuristic, which indirectly solves the problem by iteratively optimizing IGP (Inte-rior Gateway Protocol) link metrics for both the aggregate and the particular class of demands. Several computational results forthe case of two and three traffic classes are provided.

keywords : routing, traffic engineering, IP/MPLS networks, differentiated services (DiffServ)

1 IntroductionThe increase of competition between Internet Service Providers (ISPs) together with the heightened importance of IP to businessoperations has led to an increased demand and consequent supply of IP services with tighter Service Level Agreements (SLAs) forIP performance [8]. To support those SLAs commitments, IP networks have to be engineered and enhanced, since they originallysupported and mainly were designed for best-efforttraffic. To cope with limitations of the classical IP networks, several advancedtechnologies have been developed. MPLS (Multi-Protocol Label Switching)[18] among other things, provides a basic means toefficiently engineer IP traffic, while DiffServ (Differentiated Services) [6] gives the possibility to differentiate treatments for IPpackets with respect to their class of service. Traffic engineering (TE) is generally concerned with the performance optimizationof operational networks [2, 3, 22]. In classical IP networks running an IGP (Interior Gateway Protocol), TE can be deployed byoptimizing the parameters used for routing decision [4, 5, 7, 9, 11, 15, 19]. These parameters (also known as weights or metrics)are administratively assigned to each link in the network and used by routers to compute shortest paths to each destination forrouting of the demands. In this regard, the main benefit of MPLS is the ability to use paths other than shortest paths selected bythe IGP to achieve a more balanced network utilization. Such a path is called an explicit-route label-switched path (ER-LSP).Recent work such as [4, 12, 16, 17] presents hybrid TE approaches where several MPLS capable nodes are installed in a classicalIP domain to bring this MPLS benefit in that network. In the context of multi-class DiffServ/MPLS networks, TE could beimplemented both on per-aggregate as well as per-class basis [13, 14]. Here we use the term aggregateto point to the totaldemands between two nodes for all traffic classes. Using demands’ aggregate, on the one hand TE may result in globally optimalperformance, but on the other hand it may lead to several class specific constraints (e.g. over-provisioningconstraints) not beingsatisfied. Over-Provisioning (OP) is a usual and easy way to provide a certain QoS level in backbone networks. For some casesas addressed in [8], OP based on traffic aggregate can be more expensive than that based on each traffic class, for instance if theportion of important traffic in a backbone is much less than that of normal best-effort traffic. In this paper we propose as our maincontribution an offline TE approach for the problem of per-class unsplittable routing in IP/MPLS networks to specifically addresssuch per-class over-provisioning traffic constraints. As management complexity may be increased by installing a large numberof ER-LSPs, we adapt a hybrid routing strategy, where packets can be routed using both hop-by-hop (vanilla) LSPs based onIGP link metrics and ER-LSPs, which are established only to achieve better performance or to obtain a feasible routing solutionif one or more constraints are violated. The remainder of this paper is organized as follows. The following section introducessome notations and mathematically describes the problem of unsplittable per-class TE problem using both vanilla and ER-LSPs.In Section 3 we discuss the heuristic which will be used for solving the problem. In Section 4 we present some results for thecase of two and three traffic classes.

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2 Problem DescriptionPer-class Unsplittable MPLS Routing (Using Vanilla and ER-LSPs). In MPLS networks, demands can be routed usingeither hop-by-hop (vanilla) or explicit route (ER-) LSPs. A vanilla LSP is actually part of a tree, which is built by a certainprotocol (e.g. vanilla Label Distribution Protocol LDP) using existing IP forwarding tables [20]. On one hand it provides a veryfast and simple forwarding mechanism but on the other hand it only generates the same routes as normal IP data paths. In contrastby using ER-LSPs, routes other than shortest paths can be selected, since the control message to establish such an LSP is sourcerouted. This introduces routing flexibility, which is very important from operator point of view, e.g. to implement policy-based orQoS-based routing for certain classes of traffic in the network. In this paper we consider per-class unsplittable MPLS routing i.e.:(1) demand for a certain source, destination and class of traffic can not be split and has to be carried by one LSP (either vanillaor ER-LSP); and (2) demands with different traffic classes for the same source and destination are routed by LSPs, which do notnecessarily have to follow the same route. This is illustrated in Figure 1. Figure (a) shows the metric value used by IGP for eachlink on the network, (b) the shortest path tree constructed by IGP seen from node �, and (c) two (vanilla and ER-) LSPs used forrouting from node � to node �. The vanilla LSP follows the same route as the IP data path ���������, while the ER-LSP couldbe set differently e.g. through the node sequence �� � � � � � ��. In the context of per-class unsplittable MPLS routing, eachLSP for the same source and destination node will carry a different class of traffic e.g. in Figure 1(c) vanilla LSP could be usedfor best-efforttraffic, while ER-LSP for premiumtraffic or vice versa. Since vanilla LSPs are built using IP forwarding tables,which are determined by shortest path computation with respect to IGP link metrics, routing optimization can be performed inthe same way as for pure IGP networks i.e. by directly optimizing link metrics. More flexibility for traffic engineering is offeredby ER-LSPs: a path can completely be specified by the source. In spite of this, it is commonly argued that networks relyingintensively on ER-LSPs lack scalability and correspondingly that the number of ER-LSPs installed is proportionally related tomanagement complexity. Therefore, it is reasonable to deploy ER-LSPs in sparse manner, concentrating on cases where vanillaLSPs can not satisfy requirements or performance objectives. For the following discussions we use the term ”LSP” and ”ER-LSP” interchangeably, while the term ”vanilla LSP” will always be stated explicitly. For optimizing paths for vanilla LSPs wewill search for IGP metrics that result in uniqueshortest path routing pattern [5, 7, 21]. Now we will formulate the problem inmathematical notation. A directed network � � ����� is given, where � is the set of nodes representing the network’s routersand � is the set of arcs representing the network’s links. Each link ��� �� � � has a capacity ���� . The set of all supported trafficclasses is denoted by and is indexed by �. A demand ���� for traffic class �, gives the demand to be carried from source to destination �, �� � � � . A set of LSPs for traffic class � is denoted by � and indexed by �. An LSP��� �� consistsof a loop-free node sequence � ��� ���� �

��� where �� , ��� denote the head and tail nodes, respectively. A real variable ������� ��� is

associated with the load on link ��� �� resulting from flow demand ���� along shortest path routing (vanilla LSP), and �LSP��������

resulting from the flow in LSP��� ��. Let ���� be defined as a set of links that belong to the shortest path for the flow ����

(always the same for all �). For a given set of traffic classes and the corresponding traffic matrix �� � ����� �� �� � � � ,knowing the set of metrics � � ������� ���� �� � � and the set of LSPs �

�� � , the total load on the link ��� �� can be

computed as follows:

���� ���

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The capacity on a link has to be sufficiently available for all demands that are routed through that link i.e. the following constraintshave to be satisfied.

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��min � ��

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��� , �� � (5)

(4) is the utilization upper-bound constraint for aggregate traffic while (5) is the minimum OP constraint for traffic class �. Notethat ���� denotes the the given minimum OP factor for traffic class � and ������ � ���� �

������ �

��� the residual link capacity

availablefor traffic class �, assuming that the set is ordered from high to low priority traffic (i.e. � � � more important than� � � ). Thus, the problem of per-class unsplittable MPLS routing using vanilla and ER-LSPs is to find the set of metric values� and the set of LSP, such that network resources are used efficiently while satisfying the constraints. Due to the managementcomplexity reasons by applying ER-LSPs, this problem can therefore be seen as a multi-objective optimization problem: (1) tooptimize network resources e.g. by minimizing ���; and (2) to minimize management complexity i.e. in terms of . As itwill be addressed in the next section, we solve the problem indirectly using a metric-based TE approach, where (4) and (5) areimplemented as softconstraints i.e. during the search process they can be violated. At the end of the optimization, it will then bechecked whether the solution is valid and satisfies both constraints.

A Heuristic for Installing LSPs�� �; fail� false;optimize network(� )� �� ;if (��min � ���

����) then break;

for each � � � dooptimize network(��)� ��� ;compare(������);update �;if (��min � ���

����) then break;

if (��min ����

) then fail� true; break;end for

Simulated Annealing� � �; � �; � � ��;while (not stopCriterion) do

while (not equilibriumAt(T)) do� � move();evaluate(�);�� computeProbability(�� �� );if (accept(�� �)) � �;if (� better than �) � � �;

end while� � update(� );

end while

Figure 3: A heuristic for installing LSPs and a general simulated annealing framework

3 A Solving StrategyFigure 3 (left) shows the heuristic used for solving the problem. It makes use of a traffic engineering procedure based on simulatedannealing (Figure 3 right), that returns a set of metric values (a weight-system) � and the corresponding routing patterns for thegiven demands, each time it is called by the heuristic. The TE procedure as shortly explained in [15] will search for a weight-system that results in uniqueshortest path routing. In the first step, the network will be optimized using the demand aggregate� �

�� �� . The resulting weight-system � � ��

����� ���� �� � � is used as IGP metrics for establishing vanilla LSPs.Afterwards, it will be checked whether the constraints (4) and (5) are already satisfied. If it is the case, no further steps arenecessary. Otherwise several ER-LSPs have to be installed and the heuristic calls the metric-based TE procedure in each iterationusing the traffic matrix for the corresponding traffic class. The resulting routing pattern (���) is compared with that resultingfrom � (�� ). Routing entries in ���, which do not match those in �� will be installed as ER-LSPs. As will be discussed laterin this section, the metric-based TE procedure tries to indirectly minimize the number of ER-LSPs to be installed by searchingfor weight-systems that do not differ very much with the reference set �. This heuristic is for most cases sufficient, thoughthere is surely no guarantee that a small number of weight changes can always yield a small number of different routing paths.

A Simulated Annealing (SA) Approach for Metric-Based TE. Simulated Annealing (SA) belongs to the oldest metaheuris-tics and is one of the first algorithms which had an explicit strategy to avoid local optima by allowing movestowards lessperforming solutions with a certain probability, which is a function of a parameter called temperature. The probability of doingsuch a move is decreasing during the search. Here we focus on a general SA framework as displayed in Figure 3 (right) and referto [1] for a comprehensive review of local-search based methods (including SA). In each iteration step a moveoperator is calledto pick a neighbour�� around the current (search) agent �. After evaluating �� and computing the acceptanceprobability �, itwill be checked whether �� is accepted and the best agent �� is necessary to be updated. The temperature is decreased each timethe equilibrium for the current temperature is reached. The search process is terminated if the system is frozen i.e. all stoppingcriteria are satisfied. In our implementation, the SA is joint with a plain local-search (PLS) approach by forcing the probabilityto have the value of zero, if at least one of the following conditions is satisfied: (1) the neighbourhood around �� is not yet

completelyexplored; or (2) �� is improved. This hybridization aims among other things, at speeding-up convergence at smallnumber of iterations, by concentrating the search around �� first, before exploring other neighbourhood regions. Let ��� bedefined as a boolean variable which expresses whether a condition for performing PLS is satisfied (��� � �) or not (��� � �),the acceptance probability for the case of minimization can thus be expressed by:

� �

���

� if ����� � ����

���������������

� if ����� � ���� and ��� � �� otherwise

(6)

where � denotes the objective function, and � the parameter temperature, respectively. As mentioned earlier, the metric-basedTE procedure tries indirectly to minimize the number of ER-LSPs to be installed by searching for weight-systems that do notdiffer very much with the reference set �. An easy way to achieve these partial changes is by integrating a function measuringweight changes in the optimization objective:

min � �� �max ����

� � (7)

� �

�� if � �� �

� else(8)

The last term in (7) measures similarities between the reference weight-system ���� �

�� � �

� � �

���� and the current

weight-system ���� ��� � �� � �����. (7) is the optimization objective to prefer solutions with a low maximum utilization,which implies that the network is better utilized and a small number of weight changes. A constant �� is used to trade betweenthese two components. In this approach, in spite of guidance performed by the objective function, search agent � can still moveeverywhere in the solution space. Thus, sometimes it is necessary to additionally guide moves to prefer the original weight valueswhenever possible.

4 Results and AnalysisFor the following discussion we use the networks as shown in Figure 1 (net6) and Figure 2 (net14). Network net6consists of 6nodes and 14 directed links (each of 100 units capacity), while network net14consists of 14 nodes and 44 directed links (each of2.5 Gbps capacity), respectively. For net6two traffic matrices ( � �premium�� � ��� best-effort�� � ���) and for net14threetraffic matrices ( � �premium�� � ��� assured�� � ��� best-effort�� � ���) are randomly generated. Demands for best-efforttraffic were produced using the hot-spot model as introduced in [9], while those for premium and assured traffic were just takenrandomly from several predefined demand values. The resulting mean demand values for each traffic class are: ������� �����units for net6; and ������� ������ ������ Mbps for net14, respectively. The constant �� in (7) was set to ���� in order to prioritizesolutions, which result in better values of ���.

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Figure 5: OP factor for both aggregate and premium traffic forthe case without ER-LSPs (a) and with several ER-LSPs (b)

Convergence. Figure 4 shows the convergence characteristic of the hybrid SA applied to net14using traffic aggregate � .The parameters were set as follows. The search terminates if the maximum number iterations of 500 or the maximum numberiterations without improvements of 300 is exceeded. The equilibrium for temperature T is reached if the number of iterationswithout improvements at that temperature exceeds the value of 30. ��� is set to have the value of 1 if the current number ofiterations without improvements is less than the value of 25. The Figure shows that from the start and at a small number ofiterations, the algorithm performs plain local-search since improvements always exist at least every 25 iterations. Once the valueis exceeded, the algorithm performs the normal SA till the next event for PLS is triggered i.e. a new value for best agent is found.Looking at the SA parts in the Figure, the impact of temperature values on the moves is very obvious. High temperatures implyhigh acceptance probabilities for moving towards less performing solutions, while at low temperatures only moderate moves areallowed.

OP Factor and Link Utilization. The common rule-of-thumb for capacity provisioning is to have a minimum OP factor largerthan ��� or correspondingly a maximum utilization below �����. For dealing with network failure situations or unexpectedtraffic demands, several network providers might want to have larger OP factors. For the following experiments, we always setthe value of ���� for � other than best-effort traffic larger than ���. Figure 5 shows OP factor for net6after optimization by using:(a) the traffic aggregate � , where all demands regardless their classes are routed using vanilla LSPs; and (b) each traffic matrix�� , where several demands are routed using ER-LSPs. The minimum OP factor for premium traffic was set to ���. This can notbe achieved without ER-LSPs, since optimization using traffic aggregate � results in ����min � ����. Thus, further steps as shownin Figure 3 (left) are necessary. Applying optimization using ���� results in ����min � ���� which now satifies the requirement.This is achieved: (1) by installing a symmetrical LSP for the premium traffic; and (2) at the cost of a small decrease in theminimum OP factor for aggregate traffic from ���� (without the LSP) to ���� (with the LSP). The corresponding graphs for net14are shown in Figure 6. After the first step we have the maximum utilization for aggregate traffic of ������ and the minimumOP factor values of ������ ����� ����� for the premium, assured and best-effort traffic, respectively (Figure 6 a). By setting theminimum OP constraints to ����� ���� ���� we need again to re-route some of the traffic using ER-LSPs. After optimization using���� and ����, ��� decreases to ������ and the minimum OP factor values of ������ ����� ����� can be achieved by installing13 symmetrical LSPs for premium and 4 symmetrical LSPs for assured traffic. Figure 6 (b-ii) and (b-iii) show the resulting OPfactor for each link for � � ��� ��. Although the minimum OP constraints are already satisfied, the optimization can be appliedfor ��� to investigate whether a better solution is available. In our case, after optimization ��� min is improved from ���� to ����while ��� decreases to ������. This happens at the cost of additional 9 symmetrical LSPs that have to be installed for � � �.The resulting link utilizations are shown in Figure 6(b-i).

5 Summary and OutlookIn this paper we have considered the problem of offline routing control in multi-class IP/MPLS networks using both vanilla andER-LSPs, while simultaneously taking OP constraints for each traffic class into account. We proposed a simple heuristic whichiteratively calls a metric-based TE procedure, that indirectly minimizes the number of ER-LSPs to be installed by searching forweight-systems that do not differ very much with the reference set of weights, which is used for establishing vanilla LSPs. Ourexperiments show that starting from an optimized weight-system for traffic aggregate, we can improve OP factors for each trafficclasses by establishing several ER-LSPs for the corresponding traffic, although this happens at the cost of increasing managementcomplexity. Finally, since the work presented in this paper is entirely based on heuristic frameworks, several issues remain opene.g. the question how good are the results from our approach compared to (possible) optimal solutions. With respect to networkresources a comparison can be made with the results from a so-called general routing problem [9]. But it seems non-trivial tomake a comparison with respect to the number of ER-LSPs to be installed. We are currently working on these issues.

References[1] Aarts E., Lenstra J.K., ”Local Search in Combinatorial Optimization”, John Wiley & Sons Ltd., 1997.

[2] Awduche D., ”MPLS and Traffic Engineering in IP Networks”, IEEE Communications Magazine, Vol. 37, No. 12, pp. 42-47, December 1999.

[3] Awduche D., Chiu A., Elwalid A., Widjaja I., Xiao X., ”Overview and Principles of Internet Traffic Engineering”, RFC 3272, May 2002.

[4] Ben-Ameur W., Michel N., Liau B., Geffard J., Gourdin E., ”Routing Strategies for IP-Networks”, Telektronikk Magazine, 2/3, pp. 145-158, 2001.

[5] Ben-Ameur W., Gourdin E., Liau B., Michel N., ”Determining Administrative Weights for Efficient Operational Single-Path Routing Management”, Proceedingsof 1st Polish-German Teletraffic Symposium PGTS, pp.169-176, September 2000.

[6] Blake S. et al., ”An Architecture for Differentiated Services”, RFC 2475, December 1998.

[7] Bley A., Koch T., ”Integer Programming Approaches to Access and Backbone IP Network Planning”, preprint ZIB ZR-02-41, 2002.

[8] Filsfils C., Evans J., ”Engineering a Multiservice IP Backbone to Support Tight SLAs”, International Journal of Computer and Telecommunications Networking,40/1, pp. 131-148, 2002.

[9] Fortz B., Thorup M., ”Internet Traffic Engineering by Optimizing OSPF Weights”, Proceedings of IEEE Infocom, pp. 519-528, March 2000.

[10] Gouveia L., Patricio P., de Sousa A.F., Valadas R., ”MPLS over WDM Network Design with Packet Level QoS Constraints based on ILP Models”, Proceedingsof IEEE Infocom, April 2003.

[11] Karas P., Pioro M., ”Optimisation Problems Related to the Assignment of Administrative Weights in the IP Networks’ Routing Protocols”, Proceedings of 1stPolish-German Teletraffic Symposium PGTS, pp. 185-192, September 2000.

[12] Koehler S., Binzenhoefer A., ”MPLS Traffic Engineering in OSPF Networks - A Combined Approach”Proceedings of 18th International Teletraffic CongressITC, pp. 21-30, September 2003.

[13] Le Faucher F., Lai W., ”Requirements for Support of Differentiated Services-aware MPLS Traffic Engineering”, RFC 3564, July 2003.

[14] Le Faucher F.(ed) et al., ”Multi-Protocol Label Switching (MPLS) Support of Differentiated Services”, RFC 3270, May 2002.

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[15] Mulyana E., Killat U., ”Impact of Partial Demand Increase on the Performance of IP Networks and Re-optimization Approaches”, Proceedings of the 3rdPolish-German Teletraffic Symposium PGTS, pp. 275-284, September 2004.

[16] Mulyana E., Killat U., ”Optimization of IP Networks in Various Hybrid IGP/MPLS Routing Schemes”, Proceedings of the 3rd Polish-German TeletrafficSymposium PGTS, pp. 295-304, September 2004.

[17] Riedl A., ”Optimized Routing Adaptation in IP Networks Utilizing OSPF and MPLS”, Proceedings of IEEE ICC, Vol. 3, pp. 1754-1758, May 2003.

[18] Rosen E., Viswanathan A., Callon R., ”Multiprotocol Label Switching Architecture”, RFC 3031, January 2001.

[19] Staehle D., Koehler S., Kohlhaas U., ”Optimization of IP Routing by Link Cost Specification”, Technical Report, University of Wuerzburg, 2000.

[20] Smith P.A., Jamoussi B., ”MPLS Tutorial and Operational Experiences”, NANOG 17 Meeting, October 1999.

[21] Thorup M., Roughan M., ”Avoiding Ties in Shortest Path First Routing”, [online].

[22] Xiao X., Hannan A., Bailey B., Ni LM., ”Traffic Engineering with MPLS in the Internet”, IEEE Network Magazine, pp. 28-33, March 2000.