IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 31, NO. 1, JANUARY 2013
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Adaptive Spectrum Control and Management in Elastic Optical Networks Ke Wen, Xinran Cai, Yawei Yin, Member, IEEE, David J. Geisler, Student Member, IEEE, Roberto Proietti, Member, IEEE, Ryan P. Scott, Member, IEEE, Nicolas K. Fontaine, Member, IEEE, and S. J. B. Yoo, Fellow, IEEE Abstract—Elastic optical networking (EON) has emerged in recent years as a promising solution for implementing flexible bandwidth channels (flexpaths) that efficiently match the allocated bandwidth with the traffic demand using agile granularities of spectrum allocation. However, the additional flexibility in such networks raises challenges in terms of efficient control and management of spectrum resources. Among them, three important issues are (1) mitigation of spectral fragmentation, (2) implementation of impairment awareness and enhancement of robustness against impairments for potentially large-bandwidth flexpaths, and (3) design of an efficient restoration scheme to combat network failures. This paper presents an adaptive spectrum control and management scheme, which includes: dynamic on-demand spectral defragmentation, adaptive combinational quality of transmission (QoT) restoration (ACQR) and supervisory channel-assisted active restoration, to account for the three issues above. We present scalable networking algorithms and experimental demonstrations that address these issues in an EON testbed. Simulation results show that the defragmentation technique is capable of reducing the provisioning blocking probability by half with only one defragmentation module on each link. Then, we also show that the ACQR can efficiently restore many degraded flexpaths on the same impaired link while reducing the restoration blocking probability by a factor of 10 compared with the conventional rerouting method. At last, we show via simulation the advantages of using supervisory channels to determine restoration path quality and selection in EON restorations. This paper also presents experimental demonstrations to corroborate the effectiveness and feasibility of implementing these capabilities in next generation optical networks. Index Terms—Elastic optical networks, Flexible bandwidth, Impairment awareness, Modulation format switching, Next generation networking, Network control and management, Optical fiber networks, Optical performance monitoring, Quality of transmission, Restoration, Spectral defragmentation, Supervisory channel, Wavelength conversion.
I. I NTRODUCTION
T
HE SUSTAINED exponential growth of Internet traffic calls for a spectrum-efficient, performance-adaptive and
Manuscript received 25 December 2011; revised 1 September 2012. This work was supported in part by DARPA and SPAWAR under OAWG contract HR0011-05-C-0155, under NSF ECCS grant 1028729, under the CISCO University Research Program, under the Google Faculty Research Program, and under the Ericsson POPCORN project. K. Wen, X. Cai, Y. Yin, D. J. Geisler, R. Proietti, R. P. Scott and S. J. B. Yoo are with the Department of Electrical and Computer Engineering, University of California, Davis, CA 95616 USA (e-mail: kwen, xrcai, yyin, djgeisler, rproietti, rpscott,
[email protected]). N. K. Fontaine was with the University of California, Davis. He is now with Alcatel-Lucent Bell Laboratories, Holmdel, NJ 07733 USA (e-mail:
[email protected]). Digital Object Identifier 10.1109/JSAC.2013.130105.
scalable transport platform for high-capacity communications in future optical networks. In order to achieve high-capacity communication within a limited spectral bandwidth, the conventional trend in optical communications has been to increase spectral efficiency by adopting advanced modulation formats. However, the rigid ITU-T spectrum grid (G.694.1) within current wavelength division multiplexed (WDM) networks limits further increases in achievable efficiency of spectral utilization, due to the need for spectral guard bands between channels, and due to a mismatch between the client demand and channel granularity. Flexible bandwidth elastic optical networking (EON) [1] has recently emerged as a promising method to achieve spectrum-efficient and adaptive networking using agile granularities of spectrum. Such networks have the capability of provisioning both subwavelength and superwavelength channels with arbitrary bandwidth according to user demands. Ref. [1] has shown that EON can achieve higher spectral utilization efficiency by eliminating stranded bandwidth caused by the bandwidth mismatch, and by reducing the spectral guard bandwidth. In contrast, conventional WDM networks are limited by the fixed bandwidth per channel due to the ITU-T grid spacing. Additionally, EON supports adaptively changing the modulation format of individual channels to retain high-bitrate transmission under varying link conditions [2]–[4]. However, the added flexibility in such networks also raises challenges regarding the network’s control and management of spectral resources. First, the use of arbitrary bandwidth channels (flexpaths) can result in spectral fragmentation [5], [6], which increases the blocking probability and limits the overall network capacity. Secondly, the potentially large bandwidth flexpaths (≥ 100 GHz) in elastic optical networks are increasingly susceptible to physical layer impairments (PLIs) [7], [8]. A major challenge for EON is to rapidly detect PLIs and adjust the modulation format and/or route of the affected flexpaths accordingly. Furthermore, EON requires adaptation of traditional restoration schemes for combating network failures. Previously, we have proposed the concept of spectral defragmentation in EON [6], and the technique of imposing wide-spectrum supervisory channels on flexpaths for optical performance monitoring [3]. Extending these techniques, this paper proposes an adaptive spectrum control and management scheme for EON, which is capable of adapting flexpaths to different network conditions. Specifically, Fig. 1 shows three important capabilities considered in the proposed scheme, including spectral defragmentation, real-time impairment aware-
c 2013 IEEE 0733-8716/13/$31.00
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ness combined with adaptive QoT restoration, and supervisory channel-assisted restoration. The presented networking algorithms, simulation results, and experimental demonstrations corroborate the effectiveness and feasibility of implementing these capabilities in next generation optical networks. The remainder of this paper is organized as follows: Section II describes the concept of adaptive spectrum control and management in elastic optical networks. Section III discusses spectral defragmentation and presents a dynamic on-demand defragmentation algorithm based on the proposed enabling technologies and node architectures. Section IV presents the performance monitoring technology for impairment awareness, followed by a new adaptive combinational QoT restoration scheme (ACQR), which jointly uses the methods of modulation format switching and lightpath rerouting for QoT restorations. In addition to simulation results, both sections also provide experimental results. Section V presents the supervisory channel-assisted restoration scheme and simulation results. Section VI concludes the paper. II. EON WITH A DAPTIVE S PECTRUM C ONTROL A ND M ANAGEMENT Fig. 2 shows a properly designed combination of distributed yet centralized control planes that can potentially support optimal network functionality through well-coordinated control of the network. As a basic building block, an example flexible bandwidth wavelength cross connect (FB-WXC) capable of adaptive network reconfiguration includes spectral defragmentation and QoT monitoring modules at each input followed by an N × N wavelength selective switch (WSS). The localized control entity is capable of configuring these components and the optical transponder (OTP) according to the optimized spectrum allocation. In addition, the localized control entity monitors lightpath performance using QoT monitors. The centralized control plane monitors all flexpaths and performs adaptive spectrum control and management according to different network states, such as arrival of new connection demands, change of link conditions caused by impairments or failures, etc. Since EON requires the ability to generate arbitrary bandwidth flexpaths, the physical layer must employ a flexible bandwidth-capable technology. One effective solution is to use dynamic optical arbitrary waveform generation (OAWG) and measurement (OAWM) technologies [9]–[12] to implement the optical transponders. Dynamic OAWG and OAWM can scale to terahertz bandwidth through the parallel synthesis of many lower bandwidth spectral slices. What’s more, they are modulation format independent and capable of implementing both single- and multi-carrier flexpaths. While this paper will provide experimental demonstrations of the adaptive EON scheme based on the OAWG/OAWM technology, the proposed networking concepts, architectures and algorithms can also
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Fig. 2. Flexible-bandwidth wavelength cross connect (WXC) node schematic and the signaling within the control plane. WSS: wavelength-selective switch. OAWG: optical arbitrary waveform generation. OAWM: optical arbitrary waveform measurement. OTP: optical transponder.
apply to other physical layer implementation technologies such as OFDM [13] and CoWDM [14]. III. S PECTRAL D EFRAGMENTATION Spectral fragmentation inevitably occurs in EON due to the dynamic allocation of spectrum resources for new connections and the release of expired connections [5], [6], [15]. Since most spectral fragments are neither contiguous in the frequency domain nor aligned along the links, they become difficult to utilize. This can potentially increase blocking probabilities over time and limit the maximum traffic volume a network can accommodate. Spectral defragmentation aims to consolidate these fragmented spectral resources to enable more efficient spectrum utilization. The application can be classified into two categories: static and dynamic on-demand defragmentation. Static defragmentation can take place via offline computation and re-optimization. In contrast, dynamic on-demand defragmentation allows for the reconfiguration of the network for connection requests otherwise blocked due to the lack of a contiguous frequency band across all required links. Compared with the static case, dynamic defragmentation can adapt to the specific demand of each connection request. Results in this manuscript implement dynamic spectral defragmentation using wavelength conversion. This includes both algorithm design and proof-of-concept experimental demonstrations. A. Enabling Technologies and Node Architectures There are several wavelength conversion technologies [17] that can implement spectral defragmentation. Wave-mixing based techniques, such as four-wave mixing (FWM) and difference-frequency generation (DFG), are capable of converting coherent (amplitude/phase) information for multichannels simultaneously. A major challenge for spectral defragmentation is implementing wavelength conversion in a bandwidth-scalable fashion. The following demonstrates FWM-based wavelength conversion as the means to perform bandwidth-scalable and phase-sensitive spectral defragmentation in EON. As Fig. 3(a) indicates, network nodes equipped with FWM elements and WSS’s support spectral defragmentation functionality. With M parallel FWM converters on a link, the node has the capability to convert M connections’ spectra
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0 100 200 300 400 500 600 700 offered Load(Erlang) = avg. arrival rate * avg. hold time Fig. 3. (a) Pre-switch architecture for FB-WXC with defragmentation functionality; (b) Blocking probability using the architecture in (a) with different defragmentation degrees [6].
simultaneously (defined as a defragmentation degree of M ). The FWM process here utilizes two pump lasers, which provide a spectral shift equal to the frequency difference between the two lasers. In contrast to single-pump FWM methods, using two pumps creates an exact replica of the signal without spectral inversion. Furthermore, previous work has shown that FWM-based wavelength conversion is scalable to ≥ 3 THz bandwidth [18]. The FWM process in our experiment is sensitive to the polarization state of the signal; however, polarization insensitivity can be achieved by using orthogonally polarized pump laser sources [19], which results in polarization-independent wavelength conversion at lower efficiency. After wavelength conversion, the reconfigurable bandwidth and central frequency of each WSS passband enables filtering of unwanted conjugate FWM component and other FWM products. B. Dynamic On-Demand Defragmentation Algorithm The goal of dynamic defragmentation is to consolidate the fragmented spectral resources and accommodate the incoming connection request. To create sufficient contiguous spectrum, the control plane tries to convert the least amount of existing connections in order to minimize service interruptions. Hence, the algorithm starts by searching on the prospective path for a spectral range (SR) that can accommodate the new connection’s bandwidth with a minimum number of existing connections interrupted. The algorithm then computes the necessary defragmentation operations by searching for alternative available SRs for these affected connections. This requires a one-to-one, non-overlapping matching between the affected connections and other available SRs. In [20], we successfully mapped this problem into a problem of finding the maximum independent set (MIS) in an auxiliary graph. Several effective algorithms for solving MIS problems exist in the literature [21]–[24]. A parallel algorithm proposed in [24] can achieved a time complexity as low as O((log n)4 ), in which n is the number of nodes in the auxiliary graph. C. Experimental Demonstration and Simulation Results In [16], we showed implementations of FB-WXCs with a spectral defragmentation degree of 1. This particular imple-
mentation of FWM-based wavelength conversion utilizes 500 m of highly nonlinear fiber (HNLF). The proof-of-concept demonstration included spectral defragmentation over 500 GHz of bandwidth by spectrally shifting a 200 GHz channel by 200 GHz. Fig. 4(a) and (b) show the spectrum before and after spectral defragmentation with accommodation of channel C, respectively. The defragmentation operation shifted channel B (200 GHz) to B . This resulted in moving the unused bandwidth between channel A and B by +200 GHz. Fig. 4(b) shows that the unused spectrum resources in the frequency domain were then able to accommodate another bandwidth request (channel C). Fig. 4(c) presents the biterror rate (BER) performance for channels B, B and C and indicates successful wavelength conversion. The slight power penalty for the Even vs. Odd channels (decorrelated neighboring subcarriers modulated by separate modulators) results from the slightly uneven power levels entering the FWM process. Nonetheless, for each measured subcarrier, all channels achieved a BER less than the forward error correction (FEC) limit of 2×10−3 for the Reed-Solomon (255, 239) FEC code. Building upon this 1-degree defragmentation technique, a scalable FB-WXC with a higher defragmentation degree will further reduce the blocking probability for new incoming requests while enabling greater flexibility. Simulation-based evaluation also verifies the reduced blocking probability enabled by FB-WXCs with different levels of defragmentation degrees. The simulations utilized the 14node NSFNET topology, with the assumptions of 5 THz of bandwidth per fiber, and a 12.5 GHz bandwidth per subcarrier. The required bandwidth for connections was uniformly distributed from the smallest granularity (12.5 GHz) to 500 GHz. The simulation is based on the M/M/1 birth-death model, in which new connection requests arrive according to a Poisson process at an average rate of λ (λ = 0.1, 0.2, ..., 1), while their holding time conforms to a negative exponential distribution. The average holding time is 730 time units in the simulation. Fig. 3(b) shows the network-wide blocking probabilities versus different offered loads, which are the product of λ and the average holding time. Defragmentation achieves a significant reduction in the blocking probability. In particular, with only one FWM element on one link, the blocking probability
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EON enables simultaneous transmission of connections with different data rates by using bandwidth-variant channels (flexpaths) that can each use one of many possible modulation formats [13], [25]. The flexpaths can occupy large bandwidths (≥ 100 GHz), but at the cost of increased sensitivity to physical layer impairments (PLIs). Although the optical fiber by itself is a relatively stable medium, dynamic impairments due to component degradations and polarization effects can possibly cause network outages or borderline failures [7], [8]. Some examples include: a pump laser failure in an EDFA with multiple pump lasers, component overheating due to HVAC (heating, ventilation, and air conditioning) failures or extreme environmental conditions, a damage to a fiber cable in the telecom point-of-presence (PoP) during maintenance operations, etc. Once impairments occur, the quality of transmission (QoT) degrades. Typical metrics of QoT, such as the optical signal-to-noise ratio (OSNR) and the bit-error rate (BER), are sensitive to OSNR impairments, chromatic dispersion and polarization mode dispersion, etc [7], [8]. Traditionally, methods such as impairment-aware routing and wavelength assignment (IA-RWA) have been proposed in WDM networks to incorporate impairment-related factors into the static service provisioning process [26]–[30]. Dynamically occurring impairments or degradations, however, still require optical performance monitoring (PM) and network reaction for network resiliency. In our impairment-aware scheme, each input to the node is equipped with a QoT monitor (Fig. 6(a)). Within the monitor, the input signal is tapped and a 1 × M WSS isolates each flexpath (Fig. 6(b)). These flexpaths can then undergo performance monitoring modules [8]. A remaining problem is to design an efficient method to monitor these potentially broadband flexpaths. Our previous research has shown that an overmodulation-based supervisory channel [25],
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[31] is effective for OSNR and chromatic dispersion monitoring by using simple and low-speed frequency components. Fig. 6(b) shows the amplitude of a high-speed signal after 20% 100 Mb/s OOK overmodulation. By using the supervisory channels and the field-programmable gate arrays (FPGAs), the control plane (CP) is capable of real-time impairment detection on the high-speed flexpaths. B. Adaptive Combinational QoT Restoration Scheme The other key to impairment awareness and network resiliency is a distributed yet centralized CP that can adaptively react to impairments. Such a CP manages flexpath performance using the PM information of the supervisory channels from the distributed PM modules. Using the premeasured correlation data between the supervisory channel QoT and the flexpath QoT [25], the CP can efficiently adapt the flexpaths to network degradations. As we have demonstrated in [25], an effective method is to dynamically adjust the modulation format of the flexpath according to the monitoring results. In other words, if the BER of a flexpath degrades below an acceptable threshold, the CP instructs the transmitter and receiver to use a less-efficient modulation format for that flexpath. It will also reconfigure the bandwidth-variable wavelength cross connects along the path for corresponding passband adjustments. As the spectral efficiency decreases, preservation of a constant data rate would require bandwidth expansion, which is only
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