1. IntroductionOffering transport services with flexible service-level agreements (SLAs) and achiev-ing high network utilization are important goals of transport network providers. Thecustomer-pipe (trunk) model and the provider-pipe (hose) model are two ways todefine bandwidth parameters in current SLAs. The trunk model assumes that theSLA contains maximum traffic demands between each customer site; i.e., there is atraffic matrix with point-to-point traffic demands. The hose model provides a simplertraffic description by using the total incoming–outgoing traffic from the sites in theSLA; i.e., the traffic description includes point-to-anywhere-type parameters.
The point-to-point traffic demands between each site in the trunk model allow theoperator to independently reserve bandwidth for these customer pipes. Therefore, thetransport provider is able to utilize the network in the best way, since the known traf-fic matrix determines exactly the required link capacities if routing information is alsoknown. The critical part of this model is that the communication pattern between theend points is difficult to estimate. Customers may be unable to exactly predict anddefine traffic loads between the sites, which makes it difficult to specify the completesite-to-site traffic matrix for the SLA. Even if the estimation of the traffic matrix issupported by tools, it is hard to specify the proper bandwidth requirement due to traf-fic fluctuations. Another drawback of the customer-pipe model is the complexity of themanagement of trunks. Resource reservations needs to be configured in each sourcesite to each sink site, including policing, shaping, and admission control configura-tions. That is, the number of parameters to be configured is proportional to the squareof the number of sites in the network. Therefore, configuration complexity maybecome a major drawback of the trunk model in the case of large-scale networks.
Incoming and outgoing traffic volumes at each site are needed for the hose model,which can be specified either according to the physical capacity of the link to the pro-vider’s network or based on measurements. Whichever approach is used, the estima-tion of the traffic demand is easier and more precise compared to the customer-pipemodel. Here the number of configuration parameters is proportional to the number ofsites. These properties make the hose model definitely attractive for customers. Theapplication of the hose model has a great effect on resource provisioning in the opticalbackbone though. Network dimensioning based on partial information on the trafficdemands yields considerable overdimensioning [1] compared with the trunk model.
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Furthermore, the required overprovisioning increases significantly with the size of thenetwork, regarding both the number of sites and the number of links.
We claim that neither of the presented resource provisioning methods scales well tolarge networks. The trunk model requires the specification and configuration of alarge number of traffic parameters, and the hose model requires excessive overprovi-sioning in the backbone.
We propose a SLA description along with the corresponding dimensioning methodthat scales to large networks regarding both bandwidth efficiency and configurationcomplexity. The key idea is to characterize the traffic by point-to-multipoint demands,instead of the point-to-point demands of the trunk model and the point-to-everywheredemands of the hose model. The new model is called the cluster-based model becausethe notion of clusters of sites is introduced to provide a clear framework for the defi-nition of traffic demands.
It is also the goal of this paper to analyze the proposed cluster-based provisioningmodel in different network scenarios. Bandwidth efficiency improvement by routingoptimization and bandwidth requirement for link and node protection will also bestudied and compared with the trunk and hose models.
The rest of the paper is organized as follows. After a short overview of related worksin Section 2, Section 3 describes the proposed provisioning method. In Section 4 alinear-programming-based algorithm for computing the necessary link capacities isgiven. Section 5 describes the simulation environment; then Section 6 gives a detailedcomparison of the different provisioning methods in various situations. InSubsection 6.B routing methods to further improve the performance of the proposedmethod are also described and tested. Finally, the results are summarized inSection 7.
2. Related WorkTrunk-model-based telecommunication network optimization has long been present inthe literature. In Ref. [2] Minoux gives a detailed survey on the possible networkdesign tasks. His method is able to handle different cost models. Pióro et al. [3,4]developed several methods for solving various telecommunication network designtasks. Other heuristics were proposed in Ref. [5] for designing failure-protected net-works. Other papers deal with the special problems of designing virtual private net-works in an optical context (see, e.g., Ref. [6]).
However, major obstacles about estimating the real point-to-point distribution ofthe traffic in a complex network (see e.g., [7–9] on this topic) led to a flourishing lineof research that aims at dimensioning the network directly using only the existinginformation about the network traffic rather than first estimating the full trafficmatrix and dimensioning the network based on this estimation. A prominent repre-sentative of this direction is hose traffic modeling.
Its main concept has long been present in the literature under the theory of non-blocking networks [10]. In Ref. [11], the author also examines the hose-model-basednetwork-planning questions using tree routing.
Duffield et al. in Ref. [12] were the first to propose this concept for provisioning IPvirtual private networks. In their paper an analysis of the bandwidth efficiency of thehose model is presented. This paper inspired research on developing algorithms fordesigning minimum-cost networks based on hose specifications. Kumar et al. inRef. [13] argued that the optimal cost solution for hose realizations shall be based ontree topology, and they proved that the general design problem with asymmetric hoses(different amounts of traffic sent and received by the hose) and constrained linkcapacities is NP-hard. The latest important contribution in this field improves tree-based hose realization by proposing restoration algorithms [14].
A more detailed discussion of the overdimensioning required by the hose modelcompared with trunk reservation can be found in Ref. [1] by Jüttner et al. The authorsinvestigated the effect of the different bandwidth allocation methods, the networksize, and the link density on this overprovisioning factor. They also presented a linear-programming-based method to compute the exact value of the link capacities requiredby the hose model.
In Ref. [15] the authors examine a similar question to that of Ref. [12], but theyenable multiplexing of the traffic by using an online demand estimation mechanismand a dynamic admission control based on this estimation.
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Erlebach and Ruegg [16] developed an algorithm to find the capacity optimal rout-ing if load sharing is enabled.
3. Cluster-Based Provisioning MethodThe new provisioning method is proposed for a quality-of-service (QoS) enabled opticalbackbone network, with a static SLA between the provider and the customer. The pro-vider may have a policing function that controls ingress–egress traffic in accordancewith the SLA. The customer may have a shaper at the network edges for the samepurpose. Furthermore, the customer may operate an admission control function forreal-time services to avoid QoS degradation due to exceeding the traffic limitationsspecified in the SLA.
The proposed service model allows one to define SLAs—based on the concept ofclusters—in a more flexible way than the hose and trunk models. By defining a clus-ter as a set of sites, intracluster provisioning and intercluster provisioning can be dif-ferentiated in SLAs. Intracluster provisioning is concerned with resources betweensites of a single cluster, whereas intercluster provisioning refers to resource provision-ing between sites that belong to different clusters.
The concept of trunk and hose models can still be used in the context of both intra-cluster and intercluster provisioning. In intracluster provisioning, the trunk modelmeans that SLAs include point-to-point limitations between each site pair in the clus-ter. Hose provisioning for intracluster traffic means that SLA parameters are trafficdemands from sites toward any other sites in the same cluster, i.e., point-to-anywheretraffic demands in the scope of the cluster.
In intercluster provisioning, using the notion of trunk and hose models is not sostraightforward. It can be applied by considering each external cluster as a single sitein the trunk model. That is, trunk provisioning for intercluster traffic in that contextmeans that the SLA includes bandwidth parameters for traffic aggregates from eachsite to each cluster, i.e., site-to-cluster traffic demands are specified. Hose provisioningin the intercluster context assumes that the total intercluster traffic of the sites is tobe specified.
Network provisioning includes both intracluster and intercluster parts, so four pro-visioning methods can be defined from the combination of the above methods:
• trunk for intracluster and trunk for intercluster,• trunk for intracluster and hose for intercluster,• hose for intracluster and trunk for intercluster,• hose for intracluster and hose for intercluster.The management of this architecture includes tasks for the customer and for the
provider too. It is up to the customer to measure traffic in the network and renegoti-ate SLAs when traffic exceeds a given limit. Configuring the admission control andshaping in accordance with the SLA is also the task of the customer. On the otherhand, the provider has to ensure that the bandwidth specified in the SLA is alwaysavailable in the backbone. In the case of renegotiation, the provider has to checkwhether he or she can cope with the increased traffic or whether some of the linksneed to be upgraded. It is also up to the provider to configure policing according to theSLAs.
A simple SLA is attractive to customers because it makes the corresponding net-work management tasks simpler too. On the other hand, underspecified trafficdescriptions yield overprovisioning, which makes the offerings more expensive. There-fore, a reasonable balance between management complexity and overprovisioning inthe backbone is to be found.
Regarding traffic measurement and SLA renegotiation, large aggregates make iteasier for the customer to identify when the SLA is to be renegotiated. In cluster-based provisioning, the required traffic information in the SLA can be adjusted to thetraffic information available for the customer.
The form of the SLA grossly affects the complexity of the configuration of admissioncontrol for real-time traffic and shaping for best-effort traffic, i.e., the managementcomplexity. Bandwidth limitations for cluster-based provisioning should be configuredfor a group of sites, which may be a single site, a cluster, or multiple clusters. As shap-ing and admission control at the customer identify clusters based on the IP prefixes,the configuration entry for a bandwidth limitation in any of these functions consists of
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a list of IP address prefixes and a bandwidth value. Thus the configuration complex-ity not only depends on the number of bandwidth values but on the number of IPaddress prefixes too. When a cluster is mapped to a single IP prefix then the configu-ration is clearly less cumbersome than in the case when it consists of a number of dis-joint prefixes. Therefore, if clusters are already defined at the beginning, then theaddressing plan should take them into account.
When considering the responsibilities of the provider, the complexity of the policingconfiguration is similar to that of shaping and admission control at the customer. Inaddition, the provider has to check whether backbone links can cope with the trafficlimited by ingress policers. In this respect, the cluster-based method is similar to hoseprovisioning because complex calculations are needed to fulfill this task. Checkingnetwork resources against trunk-based resource requests is much easier as all-resource reservation protocols, such as aggregate RSVP or future next step in signal-ling (NSIS) protocols for routed IP networks and RSVP-TE for multiprotocol labelswitching (MPLS) support trunk reservations.
It is also up to the provider to optimize routing so that the actually used networkresources are minimized. Thus he or she should solve the optimization task withrequirements on minimum bandwidth efficiency, maximum number of bandwidthlimitations, and maximum number of IP prefixes.
4. Capacity Calculation MethodIt is shown in this section how the link capacities can be computed when the cluster-based bandwidth reservation method is used. The proposed algorithm is an extensionof the method proposed in Ref. [1].
In the model, the network is represented by a directed graph G= �V ,E� given by theset V of vertices and the set E of edges representing the sites and the links of the net-work, respectively.
The actual routing between any pair of sites is also assumed to be given. Routing isusually given by a path puv for each pair of sites u and v. However, it is possible tospecify the routing in a more general way. For each pair of sites u and v a flow func-tion ruv :E→ �0,1� is introduced, where ruv�e� denotes the portion of the traffic betweenu and v that goes on the link e. In this way, both the single-path routing [by settingruv�e� to 1 on the edges of the path between u and v and to 0 on the other edges] andthe shared routing can be handled.
Once the routing is given, a given traffic matrix determines the load on the links. Iftuv denotes the amount of the traffic from u to v, then the traffic of a certain link e is
tr�e� ª �u,v�V
ruv�e�tuv. �1�
The network dimensioning method presented in this paper does not assume theknowledge of the actual traffic matrix but only some side constraints on the trafficmatrix and aims at designing a network that is able to carry any traffic that meetsthe given side constraints.
4.A. PreconditionsThe side constraints to be given express what is known about the traffic in advance orwhat can be measured. The side constraints can be classified as follows.
Trunk Parameter. When specifying this type of side constraint, the maximumamount of the traffic from a certain given site u to another one v is assumed to beknown. The maximal value is denoted with Tu→v. This constraint can be formalized asfollows:
tuv � Tu→v. �2�
Hose Parameter. In the case of the traditional hose traffic description the limitsfor the traffic originated from and directed to a certain site u are specified by Tu→Vand TV→u, respectively. In mathematical formulas it means
�v�V
tuv � Tu→V, �v�V
tvu � TV→u. �3�
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Cluster-Based Parameter. This constraint specifies limits for the traffic betweena site u and an arbitrary set S of sites (e.g., a cluster). Similar to the previous case,two values are used for describing the traffic, one for the outgoing and another for theincoming traffic, Tu→S and TS→u, respectively. In mathematical formulas we have
�v�S
tuv � Tu→S, �v�S
tvu � TS→u. �4�
Note that all the above constraints are linear, making it possible to solve efficientlythe optimization problems that use these constraints.
The capacity reservation method presented in this paper is based on a clustering ofthe sites. Thus the set of the sites is partitioned into k disjoint subsets called clusters,i.e., V=C1�C2� ¯ �Ck, and Ci�Cj=� whenever i� j.
The idea is to differentiate between the intracluster and the intercluster traffic. Inboth cases there are two natural possibilities.
4.A.1. Intracluster TrafficConsider the cluster Ck and the site u sitting in Ck. Then the intracluster traffic of thissite can be given in the following two ways.
Intratrunk. In this case, the exact amount of traffic of u to each sites in Ck isgiven, by using the parameter Tu→v for each v�Ck, u�v.
Intrahose. In this case, only the limits for the sum of the outgoing (and incoming)traffic of u going to (or coming from) another site in the same cluster is given, by usingthe parameters Tu→Ck
and TCk→u.
4.A.2. Intercluster TrafficConsider again the site u sitting in Ck. Similar to the above cases, there are two waysof describing the traffic of u outside its cluster Ck, as follows.
Intertrunk. In this case the exact amount of traffic of u from and to each cluster isgiven, by using the parameters Tu→Cj
and TCj→u for each j�k. Note that this is aweaker description than the traditional point-to-point trunks.
Interhose. In this case only the limits for the sum of the outgoing (and incoming)traffic going to (or coming from) another site in another cluster are specified for eachsite, by using the parameters Tu→V∖Ck
and TV∖Ck→u.
4.B. Capacity CalculationAs was mentioned, the aim is to dimension the network so that it is able to carry anypossible traffic demand that meets the preconditions. Thus each individual link mustbe dimensioned considering the worst-case scenario. To compute this maximum trafficof a link e, the traffic matrix tuv that meets the preconditions and maximizes the traf-fic value (1) must be found.
Because the objective function (1) and the constrains (2)–(4) are linear functions,the value of Eq. (1) can be maximized efficiently by any linear programming method.(In the numerical evaluations the simple lp_solve software package [17] was used.) Ofcourse, to get the total necessary bandwidth, this process has to be repeated for eachedge e�E.
Note that ruv�e� is zero for most of the u ,v pairs, so the size of the real linear pro-gram to solve can be largely reduced by omitting each variable tuv whose correspond-ing route does not use the edge e. The constraints that do not affect any remainingvariables can also be omitted.
As an example, let us observe the linear program where the intratrunk–interhosetraffic description is used:
max �u,v�V
ruv�e�tuv �5a�
subject to
tuv � R ∀ u, v � V, u � v, �5b�
tuv � 0 ∀ u, v � V, u � v, �5c�
t � T ∀ k, ∀ u, v � C , u � v, �5d�
uv u→v k
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�v�V\Ck
tuv � Tu→V\Ck∀ k, ∀ u � Ck, �5e�
�v�V\Ck
tvu � TV\Ck→u ∀ k, ∀ u � Ck. �5f�
5. Simulation EnvironmentTo demonstrate the advantages of the proposed architecture, simulations were carriedout to test the new provisioning method on a realistic topology with realistic trafficdistributions. Topologies of large IP backbone networks are publicly available, whichmade it easy to define the example network. The network that was used in the perfor-mance analysis of this paper is based on the AT&T backbone network [18] shown inFig. 1.
For the purpose of the present study, the site-size statistics presented in Ref. [19]were used to estimate the generated load in sites of the investigated network. Duringthe dimensioning process, only voice sources were assumed to be present in the sys-tem, which generated calls according to the Poisson arrival process. Thus each site inthe network was assigned an offered load value, assuming a fairly big network. Theoffered load of the largest site was 4500E, the smallest one 100E, and the total offeredload in the network was 35400E. After having the generated calls in the sites, theoffered load matrix was calculated by distributing the originated calls proportionallyto the size of the destination sites in terms of generated calls (i.e., the more calls aregenerated in a site, the more it receives from the others).
The input bandwidth parameters for the dimensioning process were determined byapplying the Erlang B dimensioning formula for the traffic aggregate that shares acommon bandwidth limit in the SLA and in the admission control. When calculatingthe bandwidth parameters, a target blocking probability of 0.1% was assumed.
As was described before, the dimensioning methods work on clustered networks. Itcan be easily seen that the structure of the clusters affects the required capacity, evenif the number of clusters is kept the same and only the arrangement of the sites to theclusters is varied. To partition the network sites into clusters, a heuristic algorithmwas used. The clusters formed by the algorithm are guaranteed to be connected sub-networks in themselves. The other objective of the algorithm was to balance the sizeof the clusters in terms of sites. Finding the optimal arrangement at a given numberof clusters is beyond the scope of this paper.
6. Performance StudyIn this section the performance evaluation of the proposed cluster-based provisioningmethod is presented. First, the four proposed variants are compared without consider-ing routing optimization and protection methods. Then it is studied how the routingoptimization can be used to improve the bandwidth efficiency. Finally, the effects ofprotection methods on the results are investigated.
Fig. 1. Topology of the studied network.
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6.A. Comparison of Cluster-Based Provisioning VariantsThe comparison of the provisioning variants is based on the shortest-path routing andassumes that no protection methods are applied. The required capacity of the linksand the management complexity are the key measures characterizing the perfor-mance of the methods; therefore they were calculated for the evaluation. The studiednetwork scenario is based on the 25-node AT&T network (Fig. 1) and the precalcu-lated traffic matrix. Dimensioning was performed for each possible number of clusters(from 1 to 25). The cluster-based provisioning variants were introduced in Section 3;from now on the following abbreviations are used to refer them:
• tt—trunk provisioning for intradomain and interdomain traffic,• th—trunk provisioning for intradomain traffic and hose for interdomain traffic,• ht—hose provisioning for intracluster traffic and trunk for intercluster traffic,• hh—hose provisioning for intracluster and intercluster traffic.Figure 2 shows the overprovisioning factor, which is defined as the relative differ-
ence between the capacity need of the evaluated method and the trunk model. Notethat, although the pure hose and trunk provisioning is not displayed explicitly, theirresults can be seen in the figures because they are equivalent to specific cases of thecluster-based methods: If there is one cluster, then method ht and hh are equivalentto the hose model and methods tt and th correspond to the trunk model. The resultsfor the cluster-based methods are between the hose and trunk model results except forsome points below the x axis. These points indicate that the cluster-based methodscan overperform the trunk provisioning by exploiting the Erlang gain of multiplexingvoice trunks.
Figure 3 presents the average number of bandwidth limitations per site, which isclosely related to management complexity. It can be seen that the values for the fourcluster-based methods are between those of the hose and trunk models. The figuresalso highlight the trade-off between bandwidth efficiency and management complex-ity.
To compare the variants, the real question is the necessary management complexityusing a clustering that provides a certain targeted overprovisioning. The managementcomplexity–overprovisioning scatterplot in Fig. 4 compares the variants in this point
Fig. 2. Overprovisioning factor with different dimensioning methods.
Fig. 3. Management complexity with different dimensioning methods.
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of view. The dots represent the management complexity and overprovisioning valuesfor the above cluster configurations. It can be seen that the ht method is the best inthis sense on the AT&T network: It requires the least management complexity for anyfixed overprovisioning. For example, by allowing 20% extra bandwidth in the back-bone over the requirement of the trunk model, the needed configuration parameters ina site decreases from 25 to 5. Note that the hose model would require 130% extrabandwidth with a single parameter in each site.
The reason why hose provisioning is better than the trunk method for intraclusterprovisioning is that link capacities inside the cluster are not very sensitive to the pro-visioning method, but the trunk model needs many more configuration parametersthan the hose model. The small difference in efficiency is because the topology insidea cluster is typically close to a tree, which is the optimal scenario for the hose model[1] due to the sparse topology of the example AT&T backbone network.
In the rest of the paper, the focus is on the performance of this method from differ-ent aspects.
6.B. Routing MethodsPrevious studies have shown (see, e.g., Refs. [2,5,20] for traditional trunk-based net-work design and Ref. [1] for the hose model) that the choice of routing may have a sig-nificant effect on the bandwidth efficiency, both in trunk and hose dimensioning. Forpure trunk dimensioning, obviously the best choice is when the traffic is routed viathe least-hop path, whereas tree routing (i.e., when the traffic is routed via a spanningtree) gives the best performance for hose dimensioning.
In the cluster-based provisioning method, clustering divides the network into twolevels, which motivates the investigation of the effect of multilevel routing solutions.Applying shortest and tree routing on both level 4 additional routing scenarios can bedefined as follows:
• ss—shortest-path intracluster and shortest-path intercluster,• st—shortest-path intracluster and tree intercluster,• ts—tree intracluster and shortest-path intercluster,• tt—tree intracluster and tree intercluster.In the case of multilevel routing, the cluster-level mechanism has precedence over
the site-level algorithm, which means the following: If shortest-path routing is sedboth on the intracluster and intercluster level, then routing paths are chosen in sucha way that they cross as few intercluster links as possible, and among these paths theleast-hop path is selected. This principle is applied to the other three routing sce-narios as well.
Figure 5 shows the overprovisioning factor of the ht method at the five investigatedrouting strategies (simple shortest plus the four routing strategies described above).The basic conclusion that can be drawn based on Fig. 5 is that using tree routing onthe intercluster level results in worse performance than shortest-path routing. Thereason for the bad performance of the intercluster tree is that it disables direct con-nection between many clusters, resulting in large detours. One can also observe thatusing tree or shortest-path routing on the intracluster level does not make a signifi-cant difference. This is because the analyzed network is relatively sparse; thus the
Fig. 4. Management complexity as a function of overprovisioning at different dimen-sioning methods.
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routing paths in the tree and shortest routing case are similar. In the case of moredense networks the tree routing on the intracluster level should perform better thanthe shortest-path routing.
Similar tests were made for the other cluster-based provisioning methods as well,and the results confirmed the expectation that using tree routing where hose dimen-sioning is applied and shortest-path routing in network segments dimensioned basedon the trunk model is the best choice regarding the routing. Thus the best routingtype for each of the cluster-based methods can be selected as follows:
• tt routing for the hh method,• ts routing for the ht method,• st routing for the th method,• ss routing for the tt method.The performances of the four methods with their best routing were also investi-
gated. For comparison, Fig. 6 shows again the overprovisioning factor–managementcomplexity scatterplot for each method.
The situation is similar to that of using shortest-path routing for each, but two dif-ferences can be observed. One is that one cannot state that the ht method is the over-all best because if the overprovisioning factor is higher than 40%, some th and hh con-figurations require less management complexity to achieve the same overprovisioning,though the difference is almost negligible. The other difference is that many pointsbelonging to the ht and tt curves are located at the right-hand side of the y axis. Thismeans that there are many options to achieve better performance than the trunkmodel with less management complexity.
6.C. Protection MethodsIn backbone networks one of the most important requirements is fault tolerance. Thisfact motivated the following tests to examine how much overprovisioning is needed toprovide certain fault tolerance using traditional dimensioning methods and the pro-posed cluster-based schemes.
Fig. 5. Overprovisioning as a function of the number of clusters for different routingstrategies for the ht provisioning method.
Fig. 6. Management complexity as a function of overprovisioning for different dimen-sioning methods with optimal routing.
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Tunneling techniques used in optical backbones can be different. The major differ-ence regarding protection methods is if the backbone is a routed IP network or aMPLS-based network.
When the backbone is a routed IP network, the route of packets is determinedbased on the actual content of the routing tables. As a result of a link failure, the rout-ing tables of the affected routers will be updated by routing protocols. When all rout-ing tables are updated based on the changed link state information, the packets arerouted via the shortest path considering only the remaining links. This process maytake a few minutes. It also means that the protection path of a given flow depends onthe failed link.
If the backbone is a MPLS network, then the advanced failure-handling features ofMPLS can be used. One of the techniques for protection in MPLS is using backuplabel-switched paths (LSPs). That is, two LSPs are set up between each pair of sites,a primary and a secondary. When all the links are up, the primary LSP is used forcommunication. Whenever a link along the path of the primary LSP fails, traffic isrerouted to the secondary LSP. To ensure that the secondary LSP can be used in caseof any failure along the path of the primary LSP, the two LSPs must be disjoint.Because LSPs are set up before the actual link failure, the protection path of a givenflow is independent of which link failed. An advantage of this technique is a muchfaster fail-over time than IP routing.
6.C.1. Capacity CalculationThe effects of protection methods were examined both in routed IP networks and inMPLS with backup LSPs. During the investigation, shared protection was assumedfor single-link and single-node failures. That is, the network dimensioning was per-formed in such a way that links will support the rerouted traffic if any link or anynode fails, but only one at a time. When a node fails, all of its links are removed fromthe topology and its traffic is also removed from the traffic matrix.
6.C.2. Native IP BackboneAll the variants of the new provisioning method and their dependency on the proposedrouting methods were studied in the same way as the previous cases when no protec-tion was considered. The tests indicated that applying protection has no significantinfluence on the relative performance of the four variants of the cluster-based provi-sioning method. Therefore, only the protected counterpart of Fig. 6 is demonstrated.
Figure 7 shows the corresponding management complexity–overprovisioning factorpairs for the optimized routing strategies. In this case the overprovisioning factor isdefined as the relative difference between the given method and the trunk modelusing shortest-path routing and applying protection. As was mentioned, the resultsshow characteristics similar to those of the protectionless case, except for the fact thatthere are more cluster-based configurations that require significantly less networkcapacity than the trunk model does here. It is because applying this kind of protectionmethod, shortest-path routing is not the optimal choice for the trunk model. Note thatthe optimal routing strategy for the trunk model can only be obtained using explicitroute definitions, and the calculation of these routes is a complex optimization prob-lem. It can be observed that the curve of the ht method is almost vertical at the left
Fig. 7. Management complexity as a function of overprovisioning for different dimen-sioning methods with optimal routing and protection.
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side where the points represent the cases where the network was split into many clus-ters. Therefore, in the examined AT&T network scenario, splitting the network intomore than five clusters—left to point �−6% ,5� on the curve—makes no sense becausethe achievable additional overprovisioning gain over that point is negligible.
6.C.3. MPLS-Based BackboneThe effect of a path-protecting redundancy mechanism on the performance of cluster-based provisioning methods was also investigated. First two disjoint paths were deter-mined using Edmond’s minimal cost flow algorithm [21]. Among them the one withthe lower cost was chosen to be the primary and the other to be the secondary path.
The same tests were performed as for the protection mechanisms of the native IPbackbone. The overprovisioning factor–management complexity graph is plotted forthis protection case in Fig. 8. The displayed results are similar to those of the otherprotection method, but in this case there is no such cluster-based configuration thatcould overperform the trunk provisioning. Another difference is that the curve of theht method is not as steep as in the other case. The reason for this is that the appliedrouting strategies are developed to force the primary route to the aimed path in arouted IP network. In contrast, path protection is based on Edmonds’ algorithm,which chooses the primary and secondary paths such that the summed costs of thetwo paths are minimal, so the shortest path and the primary path could be different.For example, when there is no disjoint alternative path to the shortest one, but thereare two other disjoint paths in the network, Edmonds’ algorithm will choose thosepaths. Thus splitting the network into five clusters seems to be the best choice herefor the ht method.
7. ConclusionThe main contribution of this paper is the cluster-based provisioning method, whichby dividing the network into clusters makes it possible to define point-to-multipoint(cluster) SLAs between providers and customers. The method is a generalization ofthe well-known hose and trunk models combining the advantages of both in the caseof large networks, where neither methods scale due to low bandwidth efficiency andlarge management complexity, respectively. The behavior of the proposed methoddepends on the number of clusters. The two extrema represent the traditional hoseand trunk methods.
A linear-programming-based algorithm was also proposed for congestion-free net-work dimensioning using the above traffic model. The congestion-free network designallows the customer to use nonadaptive real-time services in optical networks, whichwould be degraded in the case of congestion.
Performance evaluation was carried out to compare the variants of the method andto study the effect of routing optimization and protection methods.
Route optimization was shown to decrease the overdimensioning of the cluster-based method in the above example of five clusters from 20% to 5%. A two-level rout-ing strategy was the best for the selected intracluster–hose intercluster–trunk model,which applied tree routing inside the cluster and shortest-path routing between clus-ters.
Fig. 8. Management complexity as a function of overprovisioning for different dimen-sioning methods with optimal routing and path protection.
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Tolerance for single failures in a routed IP network required 50% extra capacity forthe trunk model. By using the cluster-based method with five clusters, the total linkcapacity increased with another 20% (with 70% compared to trunk without protec-tion). The gain of routing optimization, however, almost disappeared when protectionmethods were also applied. Simple shortest-path routing, which is independent ofcluster definitions was the best routing strategy.
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