Optical Switching and Networking 5 (2008) 170–176www.elsevier.com/locate/osn

A novel approach to provision differentiated services in survivableIP-over-WDM networks

Smita Raia, Lei Songb, Cicek Cavdarc, Dragos Andreic, Biswanath Mukherjeec,∗

a Cisco Systems Inc., San Jose, CA 95134, United Statesb Yahoo! Inc., Sunnyvale, CA 94089, United States

c University of California, One Shield Ave, Davis, CA 95616, United states

Received 4 December 2007; received in revised form 21 December 2007; accepted 24 January 2008Available online 16 February 2008

Abstract

IP-over-WDM networks are starting to replace legacy telecommunications infrastructure and they form a promising solutionfor next-generation networks (NGNs). Survivability of an IP-over-WDM network is gaining increasing interest from both theInternet research community and service providers (SPs). We consider a novel static bandwidth-provisioning algorithm to supportdifferentiated services in a survivable IP-over-WDM network. We propose and investigate the characteristics of both integer linearprogram (ILP) and heuristic approaches to solve this problem. In the heuristic method, we propose backup reprovisioning to ensurenetwork resilience against single-node or multiple-link failures. Illustrative examples compare and evaluate the performance of thetwo methods in terms of capacity-usage efficiency and computation time.c© 2008 Elsevier B.V. All rights reserved.

Keywords: Differentiated service provisioning; IP-over-WDM networks; Survivability; ILP

1. Introduction

Internet Protocol (IP) is becoming the convergencelayer for packet-based integrated services for voice,Internet and video data. And given the increasing pene-tration of optical fiber infrastructure using wavelength-division multiplexing (WDM), IP-over-WDM networksare starting to replace legacy telecommunications in-frastructure; and they form a promising solution fornext-generation networks (NGNs). Noting that a faultin such a network, such as a fiber cut, can lead to huge

∗ Corresponding author.E-mail addresses: rai@cs.ucdavis.edu (S. Rai),

leis@yahoo-inc.com (L. Song), ccavdar@ucdavis.edu (C. Cavdar),dandrei@ucdavis.edu (D. Andrei), bmukherjee@ucdavis.edu(B. Mukherjee).

1573-4277/$ - see front matter c© 2008 Elsevier B.V. All rights reserved.doi:10.1016/j.osn.2008.01.007

data and revenue loss, survivability of an IP-over-WDMnetwork is gaining increasing interests from both theInternet research community and service providers(SPs), in order to support important services and to en-sure resilience against network failures.

1.1. Related work

The IP-over-WDM architecture is an attractivesolution for the future Internet. Several recent worksinvestigate aspects related to this architecture: thesurvivable provisioning in IP-over-WDM networks istackled in [1–4]. The work in [1] proposes a multi-layer protection scheme for IP-over-WDM networks,with the aim of achieving a tradeoff between blockingperformance and signaling overhead. The authorspropose strategies based on traffic requests and network

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policy. Also, it proposes an adaptive approach, whichprovides limited protection, while considering networkperformance and signaling.

The work in [2] tackles the problem of survivablerouting in IP-over-WDM networks. In order to providefailure restoration at the IP layer, the IP topologymust be mapped on the WDM topology such that, ifa failure occurs in the optical layer, the IP topologyis still connected: this is called a survivable mapping.The authors first introduce a method for proving if asurvivable mapping exists or not, then show how to traceand strengthen the vulnerable areas in the topology, andfinally give a scalable algorithm to find a survivablemapping.

In [3], the authors propose a scheme based onrecovery at the WDM layer, where backup resourcesharing between the IP and WDM layers is used toimprove network utilization.

In [4], two classes of services – Fully Protected (FP)and Best-Effort Protected (BEP) – are provided to end-users. The BEP traffic runs over the “excess” bandwidthin the core network, such that it does not affect theservice offered to FP-users.

1.2. Our contribution

Our study considers three typical categories ofdifferentiated services in an IP-over-WDM network[5], namely wavelength traffic, Guaranteed-Bandwidth(GB) IP traffic, and Best-Effort (BE) IP traffic.The percentage of IP traffic is 75%, which is veryrepresentative in modern networks. We assume 40 Gbpsfiber trunks in the WDM layer.

In addition, we make the following assumptions:

(i) Static demands.(ii) Traffic demands are aggregated at their ingress

nodes and are provisioned with wavelengthlightpaths.

(iii) IP utilization levels were assumed: For Guaranteed-Bandwidth (GB) services, the data rates “remain(s)close to the contracted amount [5]”. Consequently,we assume that 100% utilization is possible. ForBest-Effort (BE) traffic, we start from the target uti-lization of 85% (which is a typical value in a practi-cal network) for core IP network engineering. Ourapproach contains a coordinated inter-layer IP/OL(OL = optical layer) restoration strategy that relieson rapid backup reprovisioning to deal with fail-ures and traffic surges.

In this study, the designs of the Layer-1 (WDM) andLayer-3 (IP) networks were done separately. The Layer-3 design was done first; the resulting 40 Gbps trunks

between routers were then handed off to the Layer-1 design, where it was combined with the wavelengthdemands in the traffic matrix.

As a simplifying assumption for this study, wesegregated the Best-Effort (BE) IP traffic and theGuaranteed-Bandwidth (GB) IP traffic on separate40 Gbps connections. This was done with little effecton utilization. This obviated the need to do any Layer-3design for the GB IP traffic.

1.3. Organization

The rest of the paper is organized as follows. Sec-tion 2 describes IP layer design and aggregated traf-fic distribution. WDM layer design is studied andexplained in Section 3, where a static bandwidth-provisioning algorithm is proposed to support differen-tiated services, and both ILP and heuristic implementa-tions are presented. We compare the two approaches viasimulation in Section 4. Section 5 concludes the paper.

2. IP layer design and traffic distribution

2.1. IP layer design

Both wavelength and GB IP traffic demands aresupported by wavelength provisioning. Shortest-pathrouting is employed. The IP layer design for the Best-Effort (BE) traffic component of the network loadcombines shortest-path routing with optimized cut-through lightpath determination.

We use a typical US nationwide network (24 nodesand 43 bi-directional links) in the study. The networktopology is shown in Fig. 1 with link length (inkilometers) marked. For a given BE traffic matrix,all node-pair demands are routed on the shortest paththrough the network and their paths and path loadsare recorded. A hop-by-hop network design is thengenerated. This assumes that each demand passesthrough each router at all intermediate sites on its path(this would result in a network where all lightpathswere single hop). From the hop-by-hop design, theaggregate traffic load on every fiber span in the networkis determined.

2.2. Traffic distribution

We assume a 40-40-20 traffic distribution pattern,which is representative in modern networks [5].A 40-40-20 distribution corresponds to 40% trafficbetween “large” nodes (e.g., big cities), 40% trafficbetween “large” and “small” nodes, and 20% trafficbetween “small” nodes. Adjustments were made to

172 S. Rai et al. / Optical Switching and Networking 5 (2008) 170–176

Fig. 1. An example of nationwide network.

accommodate the nodal degree distribution. For asource–destination pair demand, it may need multiplewavelengths (e.g., 1, 2, 4, or 8 wavelengths), which hasa maximum value of 8 in this study.

An aggregate point-to-point demand set is thencreated by assuming that the demand between nodesA and B is proportional to the product of thebandwidth entering/leaving the network at the twonodes based on their “size” (“large” or “small”) andthen adjusting for the “large”/“small” distribution. Indetermining the actual traffic distribution, the numberof wavelength services and amount of GB IP servicesis first determined. Traffic demands are then assignedto node pairs, starting with the largest services first.Services in a given restoration class (1:1 shared-pathprotection in our study) [5] are assigned only if bothnodes were of appropriate nodal degree. The remainingnode pairs are assigned BE IP traffic according to theaggregate distribution.

3. WDM layer design

3.1. Problem statement

We are given a graph G = (V, E) and the number ofwavelengths available on each link λ : E → Z+. Wemay have other functions such as cost/distance definedon links, C : E → Q+. Our aim is to route connectionrequests between node pairs (s, d) to guarantee failurerecovery and to maximize sharing of backup bandwidth.

3.2. Link-vector technique to maximize backup sharing

The idea of link vector has been widely applied invarious studies (e.g., the conflict vector in [6], etc.) toidentify the sharing potential between backup paths.

Essentially, the idea is to associate a vector with eachlink in the network, identifying the number of backupwavelengths to be reserved on this link to protect againstfailures of other links. The link vector ve for link e canbe represented as an integer set, {ve′

e | ∀e′∈ E, 0 ≤

ve′

e ≤ λ(e′)}, where E is the set of links; λ(e′) specifiesthe number of wavelengths on link e′; and ve′

e specifiesthe number of working lightpaths that traverse link e′

and are protected by link e (i.e., their correspondingbackup lightpaths traverse link e).

The link vector captures the necessary informationon the sharing potential offered by each link througha simple data structure. The number of wavelengthswhich need to be reserved for backup lightpaths on linke is thus v∗

e = max∀e′{ve′

e }. Therefore, using the linkvector, we can simply reserve v∗

e wavelengths on link eas backup wavelengths.

We apply the link-vector technique in the proposedbandwidth-provisioning algorithm.

3.3. ILP approach

We develop a mathematical formulation of the WDMlayer design problem using the link-vector technique,and the formulation turns out to be an integer linearprogram (ILP). Our ILP model is much simpler andmore efficient than previously-developed models forshared-path protection [7]. Our model is scalable forlarger networks (24 nodes) compared to the previousmodel (10 nodes). In our formulation, the number ofvariables grows with the product of the number of linksand the number of node pairs requesting connections.In comparison, in the ILP formulation in [7], variablesare possible routes connecting each node pair so thatthe number of variables tends to grow exponentiallywith network connectivity and dimension. Therefore,

S. Rai et al. / Optical Switching and Networking 5 (2008) 170–176 173

Table 1ILP notations

psdi j Equals 1 if working path for traffic between (s, d) is routed through link (i, j).

Λsd Traffic demand between nodes (s, d).W Number of wavelengths.Bi j Maximum backup capacity to be reserved for sharing in the backup pool on link (i, j). (Equivalent to v∗

e in link-vectortechnique.)

N xyi j Amount of backup capacity to be reserved on link (i, j) to protect working paths crossing through link (x, y). (Equivalent

to ve′

e in link-vector technique.)δsdi j〈xy〉

Equals 1 if backup path for traffic between (s, d) is routed through (i, j) when working path crossing through (x, y) fails.

the number of variables is much smaller in our modelthan in previous work.

We define pHops and bHops as the number ofworking and backup hops that need to be assigned for agiven static traffic demand set. The set includes traffic ofthree categories (described above) and follows 40-40-20distribution pattern. Note that we assign equal weightsto primary capacity and backup capacity in the objectivefunction. In practice, weights can be assigned flexibly toshow the impacts of any of three bandwidth allocations— working bandwidth, backup bandwidth, or both.

To formally state the problem, we use the notationsin Table 1.

Objective: Minimize 0.5 pHops + 0.5 bHopsSubject to:

1. Primary path flow conservation over the physicaltopology:∑

j

psds j = Λsd∑

i

psdid = Λsd∑

i

psdis =

∑j

psdd j = 0∑

i

psdik =

∑j

psdk j , if k 6= s, d.

2. Capacity constraint: Primary and backup loadscannot exceed the capacity of a link (i, j):∑s,d

psdi j + Bi j ≤ W.

3. Backup path flow conservation over physicaltopology if link (x, y) fails:∑

j

δsds j〈xy〉

= psdxy∑

i

δsdid〈xy〉

= psdxy∑

i

δsdis〈xy〉

=

∑j

δsdd j〈xy〉

= 0∑i

δsdik〈xy〉

=

∑j

δsdk j〈xy〉

, if k 6= i, j.

Backup and primary paths need to be link disjoint:

∀(s, d) :

∑i, j

δsdi j〈i j〉 = 0.

4. Calculate required backup capacity on link (i, j)when link (x, y) fails:

N xyi j =

∑s,d

δsdi j〈xy〉

.

5. Calculate maximum backup capacity needed (maxi-mum backup load) on link (i, j):

∀(x, y) : B≥

i j N xyi j .

3.4. Heuristic approach

Considering the scalability problem of an ILPapproach for large networks, we propose a heuristicapproach with enhanced handling against both linkfailures and single-node failures. In our heuristicalgorithm, on the basis of the link vector, we tweak thecost of links to maximize backup sharing. After we finda primary path lw, we assign a low cost to those links(say, e) for which for all e′εlw, νe′

e < ν∗e . This link is

an ideal candidate for serving on the backup path, sinceit allows the connection provisioned on lw to share thepool of backup wavelengths on e, without necessitatingan increase in the number of backup wavelengths. Whilefinding paths, we also take into account the current loadon a link along with its distance, as a cost metric, so thatwe can route around heavily-loaded links.

The computational complexity of the algorithm isO(|E |

2). More details of the algorithm can be found in[8]. This step-by-step approach to calculate primary andbackup paths may potentially fall into trap situations.To handle this case, we use a k-disjoint-path algorithm[9] to find a set of diverse paths (k = 2 in our study).We modify cost on links to denote the current load onthe link as well as its distance. Note that, when we findk diverse paths simultaneously, we cannot use differentcost metrics simultaneously on different links, hence wecannot find backup paths sharing as much capacity as

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possible with other primary paths, but this approach isneeded to resolve “traps” [8].

Since the connection requests are assumed to bestatic for this network study, we choose the order ofprovisioning as follows:

A. Choose wavelength services to provision first inthe order of the number of wavelengths required.Since all wavelength services, whether they request2, 4, or 8 wavelengths, must be routed together, wechoose to route them first. Among the wavelengthservices, the requests for 8 wavelengths are routedfirst, followed by 4 and 2 wavelengths.

B. Choose guaranteed bandwidth IP requests to findprimary and backup paths.

C. Route the Best-Effort IP traffic and provide primaryand backup paths.

Within this ordering, a connection requiring morebackups will be given higher priority than that requiringfewer numbers of backups.

The above technique guarantees link-disjointedness.There may be problems when a node fails in thefollowing two scenarios:

A. The primary and backups share a node but no links.This shared node will have degree ≥4.

B. Two or more primaries with a node in common (butlink disjoint, node degree ≥4) share a wavelengthon a link for their backups. In this case, the failureof this node will lead to contention for the backupwavelength. Problem occurs when the shared nodeserves as transit on all the primaries; if it is asource or destination, then we need not reserve morewavelengths.

For connections with single-failure guarantees, we findnode-disjoint primary and backup paths. For otherconnections, with say x-failure guarantee, we find x +1link-disjoint paths. We simulate all single-node failuresand see if there is enough backup capacity to resolvecontention for wavelengths, which had been previouslyreserved, assuming only link failures. Note that, forconnections with higher protection requirements than asingle failure, since there is no node with degree ≥6 inthe network, at least one path will survive, in the caseof a node failure, and we can reprovision the backups tofortify the connection against other failures. In this case,the resource overbuild (spare capacity ratio) [5] willbe lower, since the only nodes common to link-disjointpaths will be nodes with degree ≥4. And by simulatingtheir failures, we would also take out the primaries (aswell as backups) originating/terminating through them,giving us room for backup re-assignment.

We perform wavelength assignment on the workingpaths after the routing phase. For the backup paths,the wavelength to be assigned depends on the exactfailure location, since connections share a pool ofbackup wavelengths on any link. We propose to studywavelength-allocation algorithms for backup paths aspart of future work.

We note the following key features of our design,which can offer very good quality of service:

A. The fill factor of Best-Effort IP traffic on awavelength is 85%.

B. We provide single-failure guarantee for Best-EffortIP traffic at the wavelength layer.

C. By simulating node failures and backup reprovision-ing, we use capacity much more efficiently than astatic approach.

D. Simultaneous link failures are supported. Fornode failures, backup-bandwidth reprovisioning isnecessary.

4. Illustrative numerical examples

In this section, we evaluate through simulation theperformance of both ILP and heuristic implementa-tions of the proposed static bandwidth-provisioning al-gorithm. The network topology shown in Fig. 1 is usedin this study. The performance results are similar for dif-ferent network topologies (not shown here) and load in-puts.

The traffic from the three categories is randomlygenerated with different bandwidth granularities fol-lowing the 40-40-20 distribution pattern. Traffic de-mands are uniformly distributed among all validsource–destination pairs in each distribution pattern. Weassume that the percentage of wavelength traffic is 25%and the rest are IP traffic [5]. Among IP traffic, GB andBE demands follow 50-50 distribution. All traffic de-mands are provided 1:1 shared-path protection service.

We aim to statically provision all traffic with nopacket loss in the network. Therefore, each fiber isallowed to add wavelengths when necessary to ensurezero blocking probability. We observe that the maximalnumber of wavelengths on a link ranges from 21 to 128based on different demand set sizes.

Table 2 shows the execution times of ILP andheuristic approaches with different demand sets(i.e., with different network loads). The result indicatesthat, as expected, the ILP achieves better performanceon link-load balancing compared to the heuristicapproach, especially at heavy loads. However, it suffersfrom long execution time. When the traffic has 200demands, ILP approach was found to take about 59 min,

S. Rai et al. / Optical Switching and Networking 5 (2008) 170–176 175

Fig. 2. Primary capacity length of ILP and heuristic methods.

Table 2Execution times of ILP and heuristic approaches

Demand setsize

Execution time(ILP) (s)

Execution time (heuristic) (s)

40 159 <160 288 <180 557 <1

100 879 <1120 856 <1150 760 <1200 3005 <2

while the heuristic takes much shorter time to obtainsolutions on a platform with a 2 GHz Pentium-4processor and 1 GB RAM.

Next, we investigate the resource-usage efficiencyof the two methods. We use capacity length (productof number of used wavelengths on a link and thelink length in km) of all links to evaluate capacityoptimization performance. Capacity length indicatesoverall bandwidth resource allocation in a network.

Figs. 2–4 show the primary capacity length, backupcapacity length, and total capacity length of the twomethods with different demand sets, respectively.

We observe that ILP offers better capacity savingsthan the heuristic. It can be seen from Fig. 2 thatthe ILP achieves 48%–54% improvement in bandwidthallocated for primary paths over the heuristic. Theresults are similar for backup bandwidth (26%–52%,Fig. 3) and total wavelength (44%–50%, Fig. 4)allocations. It is straightforward to see that heuristic isfixed-alternate-routing-based so its performance is notas good as that of the ILP where routing is not limitedby the number of candidate routes. Therefore, ILP canachieve global optimization and consume less resourcescompared to the heuristic.

Furthermore, the ILP outperforms the heuristic evenmore when the network load gets heavier. One possiblereason is that the order of routing connections willaffect capacity lengths in the heuristics as demandsare routed sequentially. The impact of demand orderon capacity length may become heavier when moredemands need to be routed, i.e., when the networkgets more heavily loaded. Again, we observe thatthe ILP and heuristic approaches demonstrate tradeoffbetween optimal bandwidth utilization and computationcomplexity.

5. Conclusion

This work is intended as an early study onsurvivability of IP-over-WDM networks. Based on atypical IP-over-WDM backbone mesh network anddifferentiated traffic categories, we studied a novelstatic bandwidth-provisioning algorithm to supportthree categories of demands — wavelength traffic,Guaranteed-Bandwidth (GB) IP traffic, and Best-Effort (BE) IP traffic. We applied the powerfullink-vector technique for backup-path provisioningto achieve maximal backup sharing potential. BothILP and heuristic approaches were presented anddiscussed. In the heuristic, we further investigatedthe effects of rapid backup reprovisioning to support100% failure recovery against single-node or multiple-link failures. We presented illustrative examples tocompare the ILP and heuristic approaches in termsof network capacity usage and computation time.Future works will study possible improvement in theILP’s performance by allowing differentiated protection(1:2 and 1:3 shared-path protection schemes) andcorresponding comparisons with the heuristic method.

176 S. Rai et al. / Optical Switching and Networking 5 (2008) 170–176

Fig. 3. Backup capacity length of ILP and heuristic methods.

Fig. 4. Total capacity length of ILP and heuristic methods.

Another interesting topic is to optimize the bandwidthon a link to avoid over-utilizing or congesting links in anetwork.

Acknowledgements

We gratefully acknowledge the comments from theeditors and reviewers which served to improve thispaper.

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