ICTON 2009 Mo.D3.1

⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯ The work described in this paper was carried out with the support of the Network of Excellence BONE project (“Building the Future Optical Network in Europe”), and the STREP DICONET project, both funded by the European Commission through the 7th ICT-Framework Programme. 978-1-4244-4826-5/09/$25.00 ©2009 IEEE 1

Lightpath Establishment in PCE-Based Dynamic Transparent Optical Networks Assisted by End-to-End

Quality of Transmission Estimation Nicola Sambo1, Yvan Pointurier2, Filippo Cugini3, Piero Castoldi1, Ioannis Tomkos2

1 Scuola Superiore Sant'Anna, Pisa, Italy 2 Athens Information Technology, Athens, Greece

3 CNIT, Pisa, Italy e-mail: nicola.sambo@sssup.it

ABSTRACT We propose a lightpath establishment technique for transparent optical networks, exploiting the path computation element (PCE) architecture, which leverages an end-to-end estimation framework called \network kriging". The scheme relies on the exploitation of probing data from past lightpath establishments to estimate the Quality of Transmission (QoT) of new lightpaths before they are established. We use network kriging to estimate end-to-end metrics such as Quality of Transmission (QoT) for a lightpath to be established, based on measured data from other, previously established or probed, lightpaths. Network kriging exploits the correlation between QoT metrics of lightpaths that share the same links in order to perform QoT estimation. In our lightpath establishment scheme, several attempts are performed to establish a lightpath. Our technique does not attempt to establish lightpaths with estimated poor QoT. As a consequence, the technique reduces the probability that a lightpath establishment attempt is unsuccessful, and reduces the number of successive attempts to successfully establish lightpaths. Simulation results show on a sample network that, to achieve a given blocking rate, only two establishment attempts are needed if network kriging is used, as opposed to three when it is not. Keywords: PCE, monitoring, estimation, QoT.

1. INTRODUCTION Dynamic transparent optical networks, where lightpaths must be provisioned and established on-demand in near-real time, are widely seen as the key enabling technology for the core network of the future [1]. Indeed in such networks the removal of the Optical/Electrical/Optical (OEO) conversion step can decrease CAPEX while the increased available bandwidth and flexibility can decrease OPEX.

Transparency increases physical propagation distances over which physical layer impairments accumulate, potentially resulting in unacceptable lightpath Quality of Transmission (QoT). Several techniques exist to estimate QoT; analytical models can predict QoT, before a lightpath is established, based on the network physical parameters (measured at network installation time, or in real-time with monitors) and the network state. Monitoring techniques can be used during or after lightpath establishment to check that the signal’s QoT is acceptable. In a previous work [2], a lightpath establishment scheme was assisted by monitoring and probing, in which probe traffic was sent and QoT was measured just before the actual lightpath activation. Probing aims at removing any inaccuracy stemming from analytical models. However, a drawback is that if the probe QoT is unacceptable, the lightpath must be blocked or another lightpath establishment trial must take place, thereby delaying the lightpath establishment and wasting the resources used during probing.

In this paper, we utilize an estimation technique based on the monitoring data gathered from the probing performed during prior lightpath establishments, to estimate whether a lightpath will experience unacceptable QoT before an establishment attempt takes place. A PCE computes the paths to route lightpath that have been requested. PCE exploits available past probing data to estimate a new lightpath’s QoT. The estimation technique used in this paper, based on “network kriging” [3], leverages this past knowledge as well as the correlation between the QoT of lightpaths that share the same links [4], to estimate new lightpath’s QoT. We show through simulations that the establishment scheme assisted by network kriging decreases the probability that a lightpath setup attempt is unsuccessful, and thus reduces the number of successive attempts to successfully establish lightpaths. In particular, on a large-scale, Pan-European topology, we show that in order to achieve a given blocking rate, only two establishment attempts are needed if network kriging is used, as opposed to three when it is not.

2. PHYSICAL LAYER PARAMETERS ESTIMATION The problem of estimating end-to-end metrics, such as lightpath Quality of Transmission, given only partial observations (or monitoring data) was tackled in [3], where the estimation framework is called “network

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kriging”. We review here the network kriging technique and show how we can adapt it to the estimation of QoT parameters. Network kriging relies on the correlation between end-to-end metrics that is induced by a network topology: the QoT of two lightpaths sharing a large number of links are highly correlated, while there is no correlation a priori between the QoT of lightpaths that do not share any link. Formally, consider a network with E (unidirectional) links, and where L lightpaths are established. The network is abstracted through the routing matrix { }0,1 L EG ×∈ ,

where Gi, j = 1 when lightpath i uses link j. Consider the end-to-end metrics L∈y , where yi is the metric for lightpath i. We assume in addition that the end-to-end metrics y can be written as linear combinations of link-level metrics E∈x . In particular, we assume that metric yi is equal to the sum of the metrics x over all links used by lightpath i, that is: G=y x . The QoT metrics that we consider in this work are link-additive, as will be seen shortly.

We now assume that some of the end-to-end metrics in y are already known, for instance through measurements, while others are not (measurements not – or not yet – available). We reorder the metrics within y and the rows of G, as follows: denote ym the metrics that are effectively monitored and yn the metrics that are not monitored and that should be estimated, such that: ,

TT Tm n⎡ ⎤= ⎣ ⎦y y y . Similarly, denote by Gm the matrix formed by

the rows of G that correspond to lightpaths for which monitoring information is available and Gn the rows of G corresponding to lightpaths for which no monitoring information is available, such that: ,

TT Tm nG G G⎡ ⎤= ⎣ ⎦ . Then:

m m

n n

GG

⎡ ⎤ ⎡ ⎤=⎢ ⎥ ⎢ ⎥

⎣ ⎦ ⎣ ⎦

yy x

y. (1)

Denote by (·)+ a pseudo-inverse. It is shown in [3] that the best linear estimator ˆ ny for yn is:

( )ˆ Tn n m m m mG G G G= +y y

(2) Hence using (2) we can estimate the metrics yn for unobserved lightpaths, based on measurements for other lightpaths (with ym) and the knowledge of the network topology and state (with Gm and Gn).

3. LIGHTPATH ESTABLISHMENT IN DYNAMIC TRANSPARENT OPTICAL NETWORKS WITH PCE

The proposed lightpath provisioning scheme, called lightpath provisioning with centralized network kriging scheme (NKSC), exploits network kriging to estimate QoT. We now show how the end-to-end estimation framework can be exploited to reduce blocking in the context of lightpath establishment in dynamic transparent optical networks. In a previous work [5], the BER related to a lightpath was determined using only 4 measurable parameters, accounting for the following transmission impairments: OSNR, PMD, CD, and SPM. OSNR degradations are caused by the accumulation of Amplified Spontaneous Emission (ASE) noise; the inverse of OSNR is link-additive, i.e., denoting by ri the OSNR of a lightpath using only the link i, then the OSNR Rℓ of a lightpath ℓ made by 2 consecutive links i and j is such that ,1 1 1i j i jR r r= + . PMD accumulates over links; denoting by pi the PMD parameter of link i (for instance, in ps), then the PMD parameter Pℓ seen by lightpath ℓ is such that 2 2 2

i jP p p= + . Hence, PMD is link-additive through the square of the links PMD parameters. Similarly, CD is link-additive through the dispersion parameter di for link i (for instance, in ps/nm), that is,

i jD d d= + . SPM can be quantified through the nonlinear phase of the signal φ, which again is link-additive [6]: i jϕ ϕΦ = + .

All 4 end-to-end parameters R, P, D and Φ can be monitored using the appropriate hardware: OSNR monitor for R, PMD and CD monitors for P and D, respectively, and power monitor for Φ [6]. Hence, assuming that edge nodes in a network are equipped with such monitors, it is possible using network kriging to estimate the parameters Rn, Pn, Dn and Φn for a new lightpath n given measurements Rm, Pm, Dm and Φm for a set m of other lightpaths. This is done by applying (2) in turn to y = R, P, D and Φ. BER is then derived from the estimated metrics as shown in [5].

The considered network is a transparent wavelength routed optical network where a PCE is responsible for the path computation. PCE resorts to a database with topology and bandwidth information, and a centralized measurement database (MD) containing the performed end-to-end measurements Rm, Pm, Dm and Φm. Thus, MD has the global view of the past probing measurements of the whole network.

Upon lightpath request from source s to destination d, path computation is requested to PCE. If the MD holds the QoT parameters from previous monitoring for the computed path π, then BER is derived. Otherwise, by applying network kriging to the parameters (Rm, Pm, Dm and Φm) contained in the MD, the parameters related

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to the unobserved lightpath π are estimated and the BER is derived. In both cases, if the derived BER is acceptable, PCE sends to s the computed path, otherwise another path is computed. Then, s starts signaling along π, and link resources (i.e., a wavelength along π) are reserved and optical cross-connects configured. To verify that the lightpath QoT is acceptable, probing [2] is performed and QoT measurements are gathered at d. Then, d updates the measured values in MD. If the measured parameters indicate an unacceptable QoT for π, d sends an error message to s and resources are released along π. Otherwise, the lightpath is activated and data transmission begins. In case of error message, the PCE computes another path and s performs a successive set up attempt.

4. SIMULATIONS To evaluate the novel lightpath establishment scheme, we developed an event-driven simulator. We consider a Pan-European sized network of 17 nodes, 33 bidirectional links with 40 wavelengths in each direction. Lightpaths demands follow a Poisson process (average inter-arrival time: 1 / λ) and call durations are exponentially distributed (with mean 1 / µ) such that the total offered load in the network, in Erlang, is λ / µ. Upon request, PCE randomly selects a path π within a set P(s, d) of pre-computed paths. P(s, d) is the set of all paths connecting s and d that are within one hop from the shortest path and wavelength assignment is first fit.

To evaluate NKSC, we disable the estimation step and we call the resulting establishment scheme measurement database based scheme (MDS). With MDS, only measurements from the database MD are used to compute lightpaths, while NKSC uses in addition the correlation between lightpaths QoT as permitted by the network kriging framework. NKSC and MDS are compared in terms of blocking rate after a variable number of setup attempts n: blocking occurs if no wavelength (i.e., wavelength blocking) can be found on any path of P(s, d) or if the monitored QoT parameters (using probing, after establishment) indicate unacceptable lightpath QoT (i.e., QoT blocking).

We first simulate the establishment of 1500 lightpaths for a varying offered load in the network, allowing successively n = 1, then n = 2 and n = 3 lightpaths setup attempts, in Fig. 1. As expected, for a given load, blocking rate decreases when the allowed number of setup attempts increases. Blocking increases with traffic load since wavelength blocking contributes at high loads. Contrary to MDS, NKSC leverages the estimation framework and is seen to strongly decrease the number of required setup attempts to successfully establish lightpaths, compared with MDS. In particular, the blocking probability is much lower with NKSC than MDS for n = 1. MDS obtains with n = 3 the same blocking probability that NKSC obtains with n = 2. For instance, for a load of 400 Erlang, MDS requires 3 lightpath setup attempts to achieve a blocking rate lower than 10-3, while NKSC requires only 2 attempts.

Figure 1. Blocking rate after { }1, 2,3n ∈ setup attempts for MDS and NKSC.

Figure 2. Evolution of the blocking rate after { }1,2,3n ∈ setup attempts.

Then we study the dynamic behavior of the blocking rate for each of NKSC and MDS as new lightpath requests arrive in the network. This is illustrated in Fig. 2, where the establishment of 1500 calls is simulated, allowing for up to n = 3 successful lightpath establishment attempts. The simulation was repeated 100 times and 95% confidence intervals are given. The total offered load is set to a low value, 200 Erlang, such that wavelength blocking is negligible compared with QoT blocking. As new demands arrive, MD is populated, allowing MDS to know the QoT for more and more arrivals, and allowing NKSC to estimate QoT more and more accurately, resulting in a decrease of the blocking rate even for n = 1. For instance, NKSC obtains a 0% blocking with n = 1 after 400 lightpath requests, while MDS takes 1500 lightpath requests and a second request attempt (n = 2) per lightpath demand to achieve 0% blocking.

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5. CONCLUSIONS By leveraging past probing information made available through a PCE architecture, and the end-to-end estimation framework called \network kriging", we show that it is possible to estimate the QoT for a lightpath before it is established, thereby avoiding establishment attempts for lightpaths with (estimated) low QoT. A practical consequence is that, for a given target blocking rate, fewer establishment attempts are required thanks to the estimation step. For instance, the number of attempts to establish a lightpath is decreased from 3 to 2 thanks to the estimation step on a Pan-European network. Although lightpath establishment itself is initiated by nodes and signaling is distributed, a drawback of our technique is the reliance on a global database containing monitored QoT parameters for the whole network, which may hinder scalability. We are currently investigating a more scalable version in which each node has its own view about the networks physical layer parameters.

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[4] Y. Pointurier, M. Coates, and M. Rabbat, “Active monitoring of all-optical networks,” in Proc. ICTON, 2008, invited paper.

[5] F. Cugini, N. Sambo, N. Andriolli, A. Giorgetti, L. Valcarenghi, P. Castoldi, E. Le Rouzic, and J. Poirrier, “Enhancing GMPLS signaling protocol for encompassing quality of transmission (QoT) in all-optical networks,” J. Lightwave Technol., vol. 26, no. 19, pp. 3318{3328, Oct. 2008.

[6] J.-C. Antona, S. Bigo, and J.-P. Faure, “Nonlinear cumulated phase as a criterion to assess performance of terrestrial WDM systems,” in Proc. OFC, 2002.