Real-Time Management of Reconfigurable Optical Add-Drop Multiplexers (ROADMs)

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University of Pisa and Scuola Superiore Sant’Anna

Master Thesis

Real-Time Management of

Reconfigurable Optical Add-Drop

Multiplexers (ROADMs)

Master Degree in

Computer Science and Networking

Supervisors:

Prof. Nicola Sambo Prof. Filippo Cugini

Candidate:

Biruk Bekele Legamo

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Acknowledgment

I would like to express my deepest gratitude to my supervisors Prof. Filippo Cugini and Prof. Nicola Sambo. I feel fortunate to have them as my advisers who gave me the freedom to explore on my own and at the same time the guidance to recover when my steps faltered. Their useful comments and support helped me sort out the technical details of my work. I am also thankful to them for encouraging the use of correct grammar and consistent notation in my writings and for carefully reading and commenting on countless revisions of this manuscript.

I am grateful to Andrea Sgambelluri, whose work and setup paved the way for a great deal of this thesis work, for his valuable feedback, insight, and honest opinions on everything.

I would like to thank Prof. Francesco Paolucci for suggestions and for giving me access to the lab computer to run the experimental setup of the work.

Many good friends here in Pisa and Ethiopia have helped me stay sane through these difficult years. I would like to extend a heartfelt thank you for their encour-agement, words of advice, and support. Especially, Mohammed Behredin thanks for being my running partner, for your advice and support.

Most importantly, none of this would have been possible without the love and support of my family. Who has been proud and supportive of my work and who has shared many uncertainties and challenges for completing this thesis and the whole masters program. I would like to express my heartfelt gratitude to my beloved parents and my sister Nardos Bekele, for your endless guidance and love, and for supporting me in every aspect of my life.

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Abstract

The increasing demand for ultra-high rate reliable connectivity is driving the evolution of next generation optical networks. To support such evolution, effec-tive control, management and monitoring functionalities are required. On the one hand, Software Defined Networking (SDN) is emerging as a key candidate technology to enable effective control of optical networks, providing comprehen-sive programming capabilities of transmission parameters and network devices. On the other hand, management and monitoring are still based on traditional solutions, not adequate to cope with the high level of programmability of SDN networks. For example, existing management solutions typically retrieve optical parameters from Re-configurable Optical Add-Drop Multiplexers (ROADM) every fifteen minutes. Such time interval is not adequate to rapidly detect and localize minor impairments and transmission degradations (also called soft failures) or to efficiently improve network resource utilization (e.g., through accurate control and monitoring of system margin).

The goal of this thesis is to overcome the limitation of traditional management and monitoring solutions by dynamically retrieving accurate real-time monitoring information of optical network devices. In particular, a monitoring entity enabling the management of optical parameters has been designed, implemented and ex-perimentally validated. The monitoring entity consists in a software agent tool capable of retrieving ROADM physical interface and module attributes, such as optical power levels, coherent data, Optical Signal to Noise Ratio (OSNR), and Forward Error Correction (FEC) information. The software agent tool has been successfully interfaced with the main data plane devices, such as transponders and amplifier, providing monitoring parameters to the SDN controller at config-urable time intervals. The tool has been experimentally validated on a network testbed including commercial ROADMs, 100Gb/s coherent transponders and a 320km amplified optical link. Results have shown the capability to retrieve moni-toring parameters at up to just one second, limited by the proprietary commercial hardware solutions.

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This has the potential to overcome the limitations of traditional monitoring sys-tems, supporting the introduction of advanced management and control function-alities while providing the capability to cope with soft failures and accurate link margin assessments, successfully supporting the evolution towards next generation optical networks.

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List of Figures

1.1 ABNO architecture . . . 2

2.1 3-Degree ROADM - broadcast and select . . . 7

2.2 EDFA scheme . . . 8

2.3 SPO module ROADM architecture . . . 9

2.4 SPO Muxponder View . . . 10

2.5 Management Tree of SPO GUI . . . 11

2.6 Attached Units . . . 12

2.7 Simple CLI Monitoring Parameters Exchange Flow . . . 16

3.1 Software agent monitoring tool architecture . . . 21

3.2 Flowchart of software agent monitoring tool . . . 23

3.3 Sample key value optical module parameters mapping . . . 25

3.4 Flow chart of automation tool for BV-WSS configuration . . . 30

4.1 Schematic of the experimental setup . . . 34

4.2 OSNR vs. Pre-FEC BER introducing filter size . . . 35

4.3 Pre-FEC BER of 4 filters applying automated attenuation control 37 4.4 Estimated OSNR of 4 filters applying automated attenuation control 38 4.5 Input Power vs. BER at 50GHz filter width . . . 39

4.6 Input Power of 4 filters with attenuation . . . 39

4.7 Estimated OSNR vs. Pre-FEC BER with random filtering . . . . 41

4.8 Estimated OSNR vs. Pre-FEC BER with random attenuation . . 43

4.9 Estimated OSNR vs. Q factor with random attenuation . . . 44

4.10 Estimated OSNR vs. Pre-FEC BER applying randomly-timed at-tenuation and filtering . . . 45

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List of Tables

2.1 CLI Monitoring Parameters Methods. . . 17

4.1 Random filter width selection activities take placed in the specified

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List of Abbreviations

ABNO Application Based Network Operations

AU Attached Units

AWG Arrayed Waveguide Gratings

BV-WSS Bandwidth-Variable Wavelength Selective Switches

BER Bit Error Rate

EDFA Erbium Doped Fiber Amplifier

FEC Forward Error Correction

IETF Internet Engineering Task Force

NMS Network Management System

OAM Operation Administration and Maintenance

OSNR Optical Signal to Noise Ratio

QOT Quality of Transmission

PM Performance Monitoring

ROADM Reconfigurable Add and Drop Multiplexer

SDN Software Defined Networking

SPO Smart Packet Optical Module

TDM Time Division Multiplexing

WSS Wavelength Selective Switch

WDM Wavelength Division Multiplexing

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RPC Remote Procedure Call

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1 Introduction

1.1 Motivations and Scenarios

The advancement of optical networking in both physical and the control lay-ers is leading the introduction of emerging technologies, capable of enabling high flexibility and programmability. These turning up data plane technologies are going to support flexible transmission with transponders enabling high bit rates, optimizing the spectral efficiency based on the required optical reach, thus sup-porting multiple modulation formats or forward error correction (FEC) [2]. Such transponders, based on coherent detection strategy, also support monitoring of transmission parameters (e.g., pre-FEC bit error rate - pre-FEC BER). Besides this, other data plane technologies such as Reconfigurable Add and Drop Multi-plexers (ROADMs) add flexibility by supporting dynamic optical channel switch-ing and add-drop operations. At the control plane layer, Software Defined Net-working (SDN) is developing as an architecture to remotely set network devices and services with programmable network control [24]. Even though, data and control planes have experienced such advances, the innovations in the manage-ment plane still need improvemanage-ments to develop managemanage-ment mechanisms to reduce deployment, operational complexity and maximize benefits of optical network ca-pabilities [4]. For example, several issues in the management and monitoring of a network are related to the presence of network devices from different vendors and the lack of standard solutions (e.g., for operation administration and maintenance - OAM) [21]. To address these issues of network management, the Application Based Network Operations (ABNO) [1] has been proposed by Internet Engineer-ing Task Force (IETF) as an architecture that identifies the support for the most relevant control and management functionalities.

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1.1.1 ABNO

ABNO refers to the modular architecture standardized by IETF providing con-trol and management functionalities, such as coordination and optimization of applications and network resources, programmability and management of network services [19].

1.1.2 Architecture of ABNO

The ABNO architecture, as shown below in Fig 1.1, is mainly composed by the following elements: Network Management System (NMS), Application Service Coordinator, ABNO Controller, Databases (DBs), Path Computation Element (PCE), OAM Handler and Provisioning Manager. In this thesis, we mainly refer to the OAM Handler and on its OAM functionality. For more details of the other functions the reader can refer to the details of the ABNO architecture reported in [1].

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OAM Handler : enacts mainly detecting faults, and taking the necessary action to react to problems in the network. It is a key functional block to verify the actual quality of transmission (QoT) at the data plane layer and the service level according to specific agreements (SLAs) at the application layer. The OAM Han-dler is responsible for receiving alerts about potential problems, correlating them (e.g., for fault localization), triggering other components of the ABNO, such as the path computation element (PCE), to take actions to preserve the services that are affected by the fault or the degradation [2]. For example, the OAM Handler has to trigger proper actions (e.g., adaptation of transmission parameters, re-routing) to react against soft or hard failures (e.g., link degradation or faults, respectively) which degrade QoT and, in turn, service level. Additionally, the OAM Handler interacts with the devices in the network to initiate OAM actions within the data plane, such as monitoring and testing [1]. To efficiently operate, the OAM Handler requires the network elements to continuously provide monitoring data, particu-larly in the case of high network programmability or optical transmission systems having limited margin. Margins are mandatory to ensure that optical networks support the planned demand capacity and operation over the full network life, which may span several decades, with guaranteed performance (e.g., to meet a service level agreement) [26]. However, margins come with a cost as they incur network over-dimensioning.

1.2 Margin Reduction

Vendors and operators are now oriented to reduce system margins that ac-count for aging, model inaccuracies, cross-phase modulation and other degrada-tion. Such margins cause the underestimation of the optical reach, thus increases the number of regenerators in a network and in turns the costs [2]. The reduction of system margins is the most challenging task because the related impairments vary during system life. In such dynamic adaptation schemes, the transponder capacity can be traded against margins to adapt to fiber aging or time varying impairments based on real-time performance monitoring [5]. System margins in-clude mainly aging and non-linear interference [9]. Aging is typically considered to play a role in the performance characteristics of fibers and transceivers, and

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might also account for additional losses due to network maintenance. Both aging and non-linear interference margins can be reduced to close to the system margin values and yield substantial network capacity efficiency. As the network evolves and ages, it is possible to consider the current physical state of the network, e.g. based on monitored information and remedy related problems as they appear, by dynamically modifying the connection parameters (e.g., modulation format, baud-rate, FEC, or even the path and the transmission) or adding equipment when actually needed [7] (i.e., postponing the investments).

For example, a reduction of system margins can decrease the number of installed regenerators at the beginning of the network life. However, with limited mar-gins, a more frequent generation of alarms may occur. For this reason, the OAM Handler and the management of margin reduction through accurate monitoring is attracting more and more attention from network operators.

1.3 Contribution of Thesis Work

The objective of this thesis is the development of a software agent tool located at network devices able to provide the OAM Handler of the ABNO Architec-ture with accurate real-time monitoring information. In particular, monitoring of ROADMs physical impairments, transmission parameters and module attributes is addressed. Note that the implementation of the OAM Handler of the ABNO Architecture is outside the scope of this thesis, which focuses on the development of agent modules at network elements.

The existing management solutions typically rely on optical parameters pro-vided by network elements as ROADMs every fifteen minutes. Such time interval is not adequate to rapidly detect and localize minor impairments and transmission degradation (also called soft failures) or to efficiently improve network resource utilization through margin reduction. To overcome the aforementioned limitation, we have implemented a software agent tool capable of monitoring the optical link failures, coherent data attributes (such as q factor), Optical Signal to Noise Ratio (OSNR), attached unit parameters (i.e. optical amplifier power levels) and trans-mission parameters (Pre-FEC BER), thus retrieving such monitoring parameters information at up to just one second. In addition, the work includes automation

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of Bandwidth-Variable Wavelength Selective Switches (BV-WSS) that are used for providing fine control of frequency filter and attenuation values, which can be applied to the real-time network scenario in order to observe parametric behaviors of monitoring attributes.

1.4 Thesis organization

This thesis is organized as follows:

Chapter 2 presents the monitoring systems for optical networks. Specifically, a commercially available ROADM and its optical module management system is considered, focusing on both software and hardware architecture and parameters. Chapter 3 presents the implementation of the software agent tool carried out in this thesis for monitoring the ROADMs attributes, including the implementation of automated BV-WSS configurations.

Chapter 4 shows the experimental validation of the implemented tool. In par-ticular, the tool provides real-time optical signal quality parameters and physical parameters monitoring, enabling accurate performance evaluations also in the case of impaired conditions due to filtering effects or excessive attenuation.

Chapter 5 discusses the conclusions of the thesis based on the experimental results and highlights possible future works.

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2 Monitoring Systems in Optical

Networks

This chapter presents the commercially available optical modules that have been used in this thesis as references for the state of the art of the management of today’s optical networks. Initially, this chapter provides an overview of the consid-ered optical technologies and ROADM components including their architecture. Afterwards, an example of commercially available ROADM is considered, namely Ericsson Smart Packet Optical (SPO). The main SPO module architecture and monitoring features are discussed, as well. Furthermore, details of selected SPO monitoring parameters are also presented.

2.1 Optical Technologies

Optical networks are based on optical technologies and components that pro-vide routing and restoration at the wavelength level as well as wavelength-based services [12]. The following section mainly focuses on optical component technolo-gies such as ROADM, amplifier, Wavelength Selective Switch (WSS), transponder and Arrayed Waveguide Gratings (AWG).

ROADM: is used to add, drop and switch optical signals while guaranteeing the pass-through of transit signals along the optical lines [19]. It is categorized in terms of degrees of switching, ranging from a minimum of two to, typically, around eight degrees, and occasionally more than eight degrees [20]. A degree implies a switching direction and is generally associated with a transmission fiber pair. For example, a four-degree ROADM switches in four directions, typically called North, South, East, and West. Fig 2.1 describes a three-degree WSS-based ROADM ar-chitecture. In this architecture, a splitter distributes wavelengths to a drop path fixed wavelength demultiplexer (AWG) and to each express direction. For each

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outgoing direction, a WSS is used to selectively combine add wavelengths from the add path fixed-wavelength multiplexer with channels selected from each express direction.

Figure 2.1: 3-Degree ROADM - broadcast and select

ROADM is composed of hardware components such as amplifiers, transponders, WSS and AWG [16]. In the following subsection, these components are detailed. WSS : it is used to perform the wavelength switching. It consists of a 1xN archi-tecture where each incoming (/outgoing) wavelength in the ingress (/egress) port can be selectively routed along one of the N outgoing (/incoming) ports [13]. Transponder : it transmits/receives optical signals at a specific wavelength in a Dense Wavelength Division Multiplexing (DWDM) network. It performs optical-electrical-optical (O-E-O) elaboration of client (tributary) traffic [22].

Optical Amplifiers: amplifies optical signals without the need to first convert them to an electrical signal. They are one of building blocks in the wavelength switch optical networks with the feature of amplifying the entire Wavelength Division Multiplexing (WDM) set, aiming at providing a uniform gain in function of fre-quency [19]. There are two basic technologies for the optical amplifier.

• Erbium Doped Fiber Amplifier (EDFAs): It provides in-line amplifica-tion of signal without requiring electronics (i.e. the amplificaamplifica-tion is entirely optical). A particular feature of EDFAs is their large gain bandwidth, which is typically tens of nanometers and thus actually enough to amplify data channels within the C band [10]. The fig. 2.2 shows how an EDFA works.

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When a beam of light that carrying signals passes the Erbium-doped optical fiber, a pump laser provides the amplifier energy at erbium absorption peaks through the use of wavelength selective couplers.

Figure 2.2: EDFA scheme

• Raman Amplifiers: Raman amplification relies on the stimulated Raman scattering phenomenon, when a lower frequency signal photon induces the inelastic scattering of a higher-frequency pump photon in an optical medium in the nonlinear regime. In an optical transmission system raman amplifiers are often used to complete/complement EDFA-based amplification.

AWG : is used as optical multiplexer and demultiplexer in WDM systems. This de-vice is capable of multiplexing a large number of wavelengths into a single optical fiber, thereby increasing the transmission capacity of optical networks consider-ably.

The above section gives an overview of optical technologies and network elements. These elements are typically equipped with monitoring functionalities of transmis-sion parameters, thus enabling the assessment of the quality of optical connections. These relevant parameters are OSNR, Pre-FEC BER and optical power levels that are monitored from the ROADMs (i.e. transponder). Optical power is also mon-itored by the amplifiers. In this thesis, we focus on the commercially available Ericsson ROADM called SPO. The SPO can be managed through Graphical User

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Interface (GUI) and Command-Line Interface (CLI). In the following section, the architecture and monitoring parameters of SPO module is described.

2.2 SPO Module Architecture

SPO module provides carrier class Ethernet and Time division multiplexing (TDM) services with edge, metro and core networks based on synchronous digital hierarchy, Ethernet or WDM [11]. It supports the Optical Transport Network (OTN) optical switching with modules of Photonic Attachment Unit (PAU) to provide functional ROADM and WDM. All necessary units of the ROADM are placed in each module PAU, WSS, Optical Channel Monitoring (OCM) and Op-tical Supervisory Channel (OSC). In the control system, they appear as a single

Figure 2.3: SPO module ROADM architecture

network element [18], that is composed of Muxponder, AWG, WSS, Amplifier and Variable Optical Attenuators (VOA).

In the following section, these SPO ROADM components are described.

2.2.1 Muxponder Features

The first SPO module ROADM component is a specific type of transponder called Muxponder. It multiplexes lower rate client signals together and sends

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them out over a higher rate trunk port. As shown on (Fig 2.4) the muxponder has a 100Gb/s line interface with 8 tributaries. A second type of muxponder is also available, equipped with two 10Gb/s line interfaces and 6 tributary interfaces at 1Gb/s.

Figure 2.4: SPO Muxponder View

2.2.2 Amplifier

The SPO module Amplifier is based on EDFA technology and is used to set the proper input and output power when detecting or launching the optical signal respectively. It provides high power transfer efficiency from pump to signal power. 2.2.3 WSS

The WSS is used to filter the wavelength of WDM comb and selecting the wavelength to different switching interfaces.

2.2.4 VOA

It is used for attenuation, when needed, of the power level of optical signals. The SPO ROADM network management system and methods are described in the following section.

2.3 SPO Module Network Management System

The module has two network management interfaces: GUI and CLI. The GUI is a user-friendly interface, which allows commissioning module optical devices and performs management and configuration of SPO parameters [14]. It can display name, location and alarm status. Through the GUI it is possible to access various

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operations such as saving a configuration, user’s view preferences, general config-uration tools, system information and monitoring. The GUI is partitioned into different areas within a Management Tree, i.e. a directory tree-shaped represen-tation. By means of this menu, all module configuration tools are reachable, such as bridge (which allows configuring global bridge parameter and Ethernet ring protection), optical amplifier, interface module, WDM and service OAM which allows configuring maintenance domain entities.

Figure 2.5: Management Tree of SPO GUI

In the following subsection, the main SPO module GUI based management solu-tion is detailed.

2.3.1 GUI Management of SPO Craft

It consists of Attached Units, Interface Module, and FEC Performance Moni-toring (PM) units. The following subsections specify management tasks for the three functional areas of this GUI system management.

Attached Units (AU): is a unit which consists of different attributes such as hardware and software inventory, bulk port configuration and optical amplifier. This unit mainly concentrates on the AU optical amplifier attributes such as AU optical amplifier, AU-analogue all time amplifier.

AU Optical Amplifier : is the tool which allows configuring optical amplifier plug-gable modules parameters with their own modes. (Analogue All-Time Amplifier, Analogue Current Amplifier 15Min, and Monitored Data) are parent

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enti-ties used to invoke relevant parameters of the AU optical amplifier.

Figure 2.6: Attached Units

1) AU Analogue All Time Amplifier: allows displaying performance monitor-ing of the minimum and maximum records associated to the optical amplifier (such as the minimum and maximum recorded temperature of the optical amplifier). 2) AU Analogue Current Amplifier 15-Minute: it displays performance mon-itoring of the current 15-minute records associated with the optical amplifier. Ad-ditionally, this entity includes AU Analogue Previous Amplifier 15-Minute, which is used to show performance monitoring of the previous 96x15-minute records associated with the optical amplifier (e.g., End time sampling of one of the 96 historical time periods and the status of the record). The third attribute which invokes the GUI optical amplifier is AU Amplifier Monitored Data.

3) AU Amplifier Monitored Data: This tool allows displaying all the monitored data associated with the optical amplifier. The monitored data is driven as a result of defects and not alarm events. This means that disabling, inverting, inhibiting alarms will have no impact on the Amplifier Monitored Data window. It includes read-only parameters such as input power (with input power min and input power max), input noise ratio, output power (with output power min and output power max) and output noise ratio. In the input power section,

• Input Power Min, represents the lowest input power recorded on the rele-vant optical amplifier port and

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the relevant optical amplifier port. Additionally, the power section parame-ters include

• Output Power Min, which shows the lowest output power recorded on the

relevant optical amplifier port and

• Output Power Max, which is associated with the highest output power recorded on the relevant optical amplifier port.

Interface Module: This is the second functional area of GUI management entity that is capable of displaying physical attributes of an interface module installed on the port of the relevant optical module. It consists of three attributes Info Interface Module, Coherent Data, Lane Measures that are used to invoke associated result of the optical attributes. In the following subsection, two of parameters concerned with optical signal quality metrics are specified.

A) Info Interface Module: it displays hardware inventory of an interface module installed on the port of the relevant module.

B) Coherent Data: which is available for WDM interface modules and it is engaged in giving measurement result of WDM data installed on the port of appropriate module. The parameters measured by this entity are those used for determin-ing the quality of optical connection and performance of the link. As read-only parameters, it includes

• Estimated Local Oscillator Frequency Offset, which shows an esti-mation of the local oscillator laser offset on the coherent receiver,

• Estimated Electrical Signal to Noise Ratio (OSNR), which is taken to provide an estimation of the electrical signal-to-noise ratio. (OSNR is typically used as figure of merit to characterize the performance of optical link).

• Q Factor, that returns estimation of the receiver performance. For coherent optical detection links, the Q Factor is a more reliable method to estimate the link performance. This method estimates the OSNR at the receiver by measuring the BER-versus voltage threshold at voltage levels where BER can be accurately measured.

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FEC PM : FEC is another determinant factor in detecting the optical network physical performance situation. This parameter is provided by FEC PM entity which manages FEC information every 15 minutes including the 24 hours mea-surement result that has been recorded every 15 minutes. It contains attributes used to invoke the important parameters such as PM Current FEC 15-Minute, which is used for performance monitoring of the current 15-minute FEC data. The following read-only parameters are included under this sub-tree,

• CorrectedBits, which returns the number of bits error corrected in the DWDM trunk line in the current 15-minute,

• Max and Min Error Time-stamp, that is used to represent the date and time when maximum and minimum values were reached in the current 15-minute,

• Elapsed-time, that shows time since the current error measurement period started in the current 15-minute

• Corrected Bits Ratio, it is used to provide the BER in the current 15-minute. Here the BER is calculated as

BER = F EC corrected errors during time of most error occurred second

( current 15minute ∗ line rate)

In addition, PM FEC Monitoring unit includes FEC Counters as the determinant parameter for measuring the quality of optical networks.

FEC Counters: it allows in displaying and configuring all the FEC information collected by an optical system for the relevant OTN port. It includes the following read-only parameters.

• Elapsed Time: which returns the duration since the last reset was per-formed.

• Corrected Bits Last Second: it represents the last seconds count, either when the window was opened or when refresh button was selected and it is unaffected by the reset counters function.

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• Corrected Bits Ratio: it retrieves the value of BER since the last reset with the following equation:

F EC − BER = Corrected Bits Last Second

( Elapsed T ime ∗ line rate)

The SPO module alternative means of optical network management method is through CLI. In the section below these CLI, monitoring approach is detailed.

2.3.2 SPO Craft CLI Monitoring

SPO Module uses CLI as an alternative management interface to monitor op-tical network transmission parameters and functionalities. The CLI is capable of managing parameters which can be customized and can be used to integrate with tools like software agent in making the management entity. The parameters un-der CLI are not monitor driven like GUI, but they are formed in form of scripting attribute with an identifier for each node of modules in each class. That is the parameters to retrieve the result from the optical module, first make a request to Jsytem Automation framework (which is layered test automation framework and used in this case in order to interface to the optical module). The communica-tion from this framework to optical module is done based on Extensible Markup Language Remote Procedure Call (XML-RPC). XML-RPC uses XML to encode its calls and HTTP transport mechanism, so the call request is encoded and uses tags for the monitoring parameters as identification from the module database. These parameters are invoked to return their relevant values from windows shell (wish.exe) using CLI that pass through Jsystem automation framework. Then the framework bypasses these parametric requests to the SPO module in the form Get-request and waits for a reply, the module will check for the parameter em-bedded request values and returns to the Jsystem in the form of RPC-reply, if the requested parameter value exists, SPO returns with their true value or false if the requested parameter doesn’t exist. Finally, the Jsystem pass these reply to window shell that is displayed as reply notification of CLI request.

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Figure 2.7: Simple CLI Monitoring Parameters Exchange Flow

The CLI is able to monitor important optical link transmission parameters and physical attributes. These attributes are (Operational status, coherent data, Am-plifier monitored data and FEC counters) which are considered as important pa-rameters having high polling value in finding out the performance monitoring of the module. Thus, these parameters are explained as follows,

• Operational status, it returns the state of OTN port if it is up or down. • Node-Port-GetCoherentData, retrieves all the coherent data attributes of

optical module (such as OSNR, Q factor, Chromatic Dispersion).

• GetAmplifierMonitoredData-Input-Power, this one retrieves the relevant input power of the amplifier where mac-address is used to identify this at-tached unit.

• GetAmplifierMonitoredData-Output-Power, concerned with returning the output power of the amplifier based on mac-address of the optical node. • Stats-GetFECCounters-slot-correctedbits, returns the status of

rele-vant port FEC correct-bits information.

• Stats-GetFECCounters-slot-corrected-bitratio, it gives the measure-ment result of FEC bit error rate of specified OTN enabled port.

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These parameters as stated are among all configurable attributes with high polling value. Hence, Tab. 2.1 shows some commands of parameters where CLI uses to monitor the optical module.

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As concluding remarks of this chapter, the existing CLI and GUI management interfaces besides monitoring they are capable in the configuration of optical mod-ules entities such as cross connection manager, but we have concentrated on their monitoring parameters method because the current system has time variant limi-tation to rapidly detect and localize minor impairments and transmission degra-dation in optical networks. Thus, in chapter 3 we introduce a software agent tool able to solve this limitation.

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3 Software Agent Tool for Monitoring

ROADMs Attributes

This chapter outlines the functionalities of the software agent tool implemented in thesis work for monitoring optical module attributes. It presents the design of this monitoring tool for real-time management of ROADMs attributes. The chapter starts with a description of management plane extension that is needed for the implementation. Then, it continues by giving concrete details, explaining how the implemented management tool works and its architectural specification. Moreover, the design of automated BV-WSS is presented. Automated BV-WSS is used for experimental verification of the proposed management tool.

3.1 Extending the Capability of Management Plane

As anticipated in previous sections, the management plane in optical networks is needed to provide configuration, link management and optical performance moni-toring (e.g., optical signal quality and degradation). Currently, available manage-ment solutions are not designed to support advanced dynamic programmability. In particular, they are characterized by:

• Vendor lock-in issues, having vendor specific control elements • Lack of standardization (e.g., lack of common data modeling).

• Incapability of real-time optical module parameter monitoring. For example, the current management solution typically retrieves optical parameters from ROADM every fifteen minutes. Such time interval is not adequate to rapidly detect and localize minor impairments and transmission degradations (also called soft failures) or to efficiently improve network resource utilization (e.g., through accurate control and monitoring of system margin).

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In order to solve the aforementioned limitation, the proposed solution considers extending the capability of management plane.

For such extension, the following is taken into consideration in advancing the management and control functionalities.

• Use interactive programmable language which eases the management and operational complexity avoiding direct configuration, management done on device.

The driving force behind using this approach is the need for a programmatic inter-operable interface to manipulate management, which supports real-time optical performance monitoring of ROADM attributes. In this context, optical perfor-mance monitoring is categorized as monitoring of transmission parameters such as Pre-FEC BER (i.e. parameter for direct signal quality estimation), optical signal parameter monitoring (such as OSNR monitoring, Q factor), optical power monitoring and in addition controlling the status the optical module.

Subsequently, a software agent tool has been implemented that can handle the proposed management extension functionalities.

The following section gives details about this designed tool, explaining how it works and its capability of providing real-time optical modules monitoring.

3.2 Software Agent Monitoring Tool

The objective of the implemented monitoring tool is to provide real-time infor-mation about link transmission performance and accurate control and monitor-ing of system margins. In addition, the tool has to rapidly detect and localize minor impairments and transmission degradations in order to maintain carrier uptime and quality of service (QoS) agreements. Such information is determined by monitoring physical parameters of the optical module. The fig. 3.1 shows the architecture design of the developed software agent tool in finding out ROADM attributes information.

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Figure 3.1: Software agent monitoring tool architecture

As depicted in Figure 3.1 the architecture is composed of monitoring application, interface framework an optical module. The following section gives some overview of the monitoring application layer.

Monitoring application is layered on the top level of the architecture that holds all management tasks. This task includes:

• monitoring of optical signal parameters, which is concerned with generating optical performance information and detecting physical impairments (i.e. OSNR) degradations

• optical signal quality monitoring, which covers BER, Q factor monitoring primarily, i.e. method to evaluate the overall quality of the signal rather than a specific optical parameter.

• optical power monitoring, it involves processing input power at the receiver end

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by the monitoring application in determining the state of the optical module OTN port.

The monitoring application combines this task and sends a parametric request to the mediator interface (i.e. system framework) and in another end, it provides the result of monitoring parameters to the SDN controller. The system framework is used as mediator interface to the optical module. When there is a request from monitoring application, first it checks for the availability of parameter based on their identifier in the local database. If it is available, it embeds the request and sends to the SPO module interface. Then, the SPO module returns the result after matching with the integrated optical module attributes. Afterwards, the mediator interface returns the value of requested parameter to the main application tool. During verification of the availability of parameters in the local database, the me-diator interface returns the undefined message for those parameters at which their identifier is not found in the database. This is done without interfacing to the optical module.

The following section describes the working scenario of the software agent moni-toring tool.

3.2.1 Monitoring tool functionalities

Several ROADMs monitoring parameters are considered to assess the signal quality as well as the origin of the observed signal degradations. They include:

OSNR, Pre-FEC BER, Q factor and input power. The figure 3.2 shows the

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The tool activates monitoring operation by calling the communication framework to the optical module. Once the framework opens the connectivity to the mod-ule, the monitoring tool can pass the attributes request. This initialization of the framework is started by the run () function of Pexpect python module. Thus, the Pexpect module spawns child application of the component and auto-matically controls them. In this case, the SPO module (i.e. node1 and node2) are considered as the child entities and their connectivity to the tool is interfaced when the spawn object is executed. The following function shows how this method initializes the connectivity to the optical module and returns response from the module interface.

def run():

command1 = "lappend auto_path C:/Home /JSystemTestAutomationSuite/tcl"

command2 = "package require 1410apiCES" command3 = "oms1410 connect ne1 10.0.0.11" command4 = "oms1410 connect ne2 10.0.0.21" try ex = wexpect.spawn(’C:\\Tcl/bin/tclsh.exe’) ex.expect(’%’) Mycommand(ex,command1) Mycommand(ex,command2) Mycommand(ex,command3) except: errorMessage = str(sys.exc_info()[1]) print errorMessage

def Mycommand(ex, command): gc.enable()

ex.sendline(command) ex.expect(’%’)

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print >> fl, ’# Command ....: %s’ % command print >> fl, ’# Replay...: %s’ % response

return response run()

The response from the optical module will be the status of the connectivity with the node name.

• Mapping Optical Parameters, in structuring the optical module parame-ters data, the tool uses mapping (i.e. maps the monitoring parameparame-ters in a biunique way to ease the management). It stores a parameter value with some key and extracting the value given the key. As the biunique mapping is in the form key-value pair, for each parameter values the keys will be sent to the ROADM module rather than sending the long command line interface script. This mapping makes the management easy in finding out parameters value by easily matching from the list of defined optical parameters.

The fig. 3.3 shows sample key value pair mapping from the list of optical param-eters.

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The algorithm in finding out the optical parameters of 100Gb/s coherent card looks like below.

def slotcard(100G): d ={ ’10Ga’: ’1/1’, ’10Gb’: ’1/2’, ’100G’: ’18/11’, } for number in 100G:

return d.get(100G,’No valid data’)

for cards in slotcards: try:

threads[cards]= thread.start_new_thread(requester, (ex,cards,))

except thread.error as msg:

print ’thread error. Error Code :’+ str(msg[0]) + ’ Message ’+ msg[1] sys.exit()

As shown in the function the mapping has been done also for each card in the SPO module, that we have two 10Gb/s cards and one coherent 100Gb/s card in the main SPO module. Using threading mechanism, the program selects from the list of the cards and make parameter request using requester function. In this part, we have been working on the 100Gb/s card only.

• Reset FEC Counters, after the parameters mapping is predefined, there is an option for resetting the FEC counters. This method will reset all FEC counter associated attributes (corrected bit ratio, elapsed time). This object is mainly used in the calculation of Pre-FEC BER.

• Timer, to assign a recurring parameter event, the monitoring component uses the timer class incorporated in the system current time. In the case

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of both route options, the monitoring component starts a polling timer for every second. Whenever the iteration timer expires it fires parameters event until it is over the predefined time. During this event, a data collection of parameters value is used. Afterwards, a sequence of parameter request to the chosen optical module card is sent.

The sequence of parameter is composed of Coherent data, Pre-FEC BER and Input power. These listing methods are described as follows,

• Coherent Data, the coherent data parameter request message is sent to the optical module. Upon reception of a request, the optical module generates a response. The response is a list of attributes with their value. This list of attributes are estimated OSNR, Q factor, estimated chromatic dispersion and local oscillator power. The tool splits the generated value list and then extract OSNR and Q factor using their index from the list. Thus, the two parameters are extracted taking their high polling value as a mean of mon-itoring signal integrity. Apart from this, the polling timer is also adjusted. The tool tracks the exact time when OSNR and Q factor measurement re-sults are returned and it updates the parameter collector file by writing the response time including measured values. The discussed splitting of coherent data parameters by this method is made using this request.

command=nodes(str(1))+ option(’CoherentData’) + ’18/11’ datax = Mycommand(ex,command) time.sleep(short_sleep) [LocPower,LocBi,LocTempA1bs,EstLOC,EstCD, EstDGD,EstSNR,EstBER,Qfact,EstOSNR]= datax.split(’,’) data3= datax.split(’,’) data4 = data3[9] datak = data3[8] data1= "EstOSNR: " datar= "Qfactor: " data6 = data1 + data4 dqfact= datar+ datak

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• Pre-FEC BER monitoring object, the BER parameter result is sent to the optical module card in a different way to that of coherent data. This is because the bits in a signal are random in nature and therefore it is prac-tically impossible to monitor the exact BER from the optical module card reading. Rather this monitoring tool is able to get approximate value of Pre-FEC BER. This value obtained by comparing the BER polling with the last measured utilization of each elapsed time. The Pre-FEC BER monitoring request flow starts by resetting the FEC counters. Then on each iteration, it resets the counter and set the process to sleep for 2 seconds for compar-ing their pollcompar-ing value. For each request, the optical module card returns the estimated value to the tool. Finally, each second Pre-FEC BER result including the response time is written on parameter collector file.

Using the function below this method retrieves estimated BER information. def requester (ex,cards):

try:

gc.enable()

if k==reset-fec

command8=nodes(str(1))+ option(’ResetFEC’)+ ’1/18/11’ datay= Mycommand(ex,command8)

data3= "ResetFEC-Counters: " + datay k = data3

print data3 time.sleep(2)

command=nodes(str(1))+ option(’FECcorrectedBER’) + ’1/18/11’+ ’ correctedBitsRatio’

datax3 = Mycommand(ex, command) data1 = "FEC-BER: "

dataw = data1 + datax3 print dataw

else:

command=nodes(str(1))+ option(’FECcorrectedBER’) + ’1/18/11’+ ’ correctedBitsRatio’

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data1 = "FEC-BERm: " dataw = data1 + datax3

• Input power, this method makes optical amplifier input power monitoring

request to the optical module. It takes input power key that has been

already initialized in mapping object. This method does not use def card () function since it is categorized under the attached unit. The way it finds the node is by it mac- address. So, there is no need to access the card from this method. Thus, using attribute function and node function it manages the input power of the optical amplifier. The former function maps input power command request with the mac address of the node, while the latter is used to make the selection of the nodes. Then, it concatenates both functions and passes the request to SPO module interface for managing the input power. The request flow and data splitting technique are similar to the coherent data object. The following functional code shows how this method makes a request.

command=nodes(str(1))+ option(’AmplifierInputPower’) dataip = Mycommand(ex, command)

time.sleep(short_sleep) m = dataip.split()

dataipp= m[1] + m[2] + " "+ m[3] print dataipp

Currently, the software agent tool polls monitoring result of the specified param-eters every second until the end of predefined time and when there has not been any activity over the specified period. In addition, the monitoring tool through the operational status method controls the state of optical module card (i.e. if it is up or down) for example, when there is a failure, it notifies with status down. In addition to implementation of software agent monitoring tool, automation of BV-WSS functionalities have be done.

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3.3 Automating BV-WSS functionalities

BV-WSS enable the reservation of the minimum required portion of spectrum resources. The automation of BV-WSS is concerned with automatic fine control of frequency filters and it is used in currently active connection (i.e. experimental setup), which the implemented software agent tool is monitoring.

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We have used this tool for checking the direct detection capability of our proposed software agent tool. In that, the automated BV-WSS involve shaping filters, de-fragmentation techniques which affect the state of the network by changing some of the relevant parameters value. Such parameters typically correspond to, op-tical performance monitoring entities and changing them may involve, shifting the nominal central frequency of the frequency slot allocated to a connection or adjusting its allocated frequency slot width or even the attenuation value periodi-cally. Hence, using this concept the following section presents the implementation of automated BV-WSS. As shown in figure 3.4 the automated tool supports the following functionalities.

• Random function, automatic selection options to apply shaping of filters with channel spacing or attenuation size and to randomly select from pre-defined available filters width or attenuation values.

• Timer, through this option periodical changes of channel spacing or attenu-ation value, will be applied to the connection.

• Logfile writer, it is the functionality of file object function with the write and update mode of each time applied filter profiles.

• The application creates filter shaper (FS) instance before loading profile this done by FS-create method, this creates the object instance with a user specified name. The user can choose any name containing one or more letters or digits or underscore, provided that distinct names are used for each BV-WSS device. The FS-create method will not open the communication port or make connections to the BV-WSS device. Opening a connection to the device is done by invoking FS-open function

• Carrier vector data profile, this is concerned with setting central fre-quency granularity and a creation of frefre-quency from selected filter gap. It includes generation of slot width in multiple of 2 in the consecutive frequency slots with low-frequency slot and upper-frequency slot. In this method, the frequency range for selected filter is set in 1 GHz increments from low-frequency slot up to the upper-low-frequency slot. If the selected filter is with

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attenuation it sets the size of attenuation on the selected frequency range. The code of this function is shown below.

gap=float("{0:.3f}".format(uniform(0.013, 0.025)))

lfreq=carrier-gap ufreq=carrier+gap

wsFreq = np.arange(lfreq, ufreq, 0.001) wsAttn = np.sin(45*(wsFreq-lfreq)/2/np.pi) wsPhase = np.pi*np.cos(5*(wsFreq-lfreq)) wsPort = np.ones(np.size(wsFreq))

print "Gap= "+str(gap)

print "attenuation= "+str(wsAttn)

WSPfile = open(’TrigProfile.wsp’, ’w+’) for x in range(np.size(wsFreq)):

WSPfile.write("%0.3f\t%0.3f\t%0.3f\t%0.3f\n" % (wsFreq[x], wsAttn[x],wsPhase[x], wsPort[x])) WSPfile.close()

Afterwards, loading of created filter profile is done on the device through FS-Load-Profile method, which applies the created profile filter and waits for completion. It calculates the filter profile based on created text, then loads filter profile to BV-WSS device. Finally using Close-FS option it dis-connects from the BV-WSS. But the program continues with last uploaded filter profile until the scheduled time expires.

As a concluding remark, during the monitoring process of the optical module, both the software agent tool and automated tool start at equal time-stamp and changes made at the automated tool is symmetric to the software agent monitoring tool. The next section presents the experimental results of proposed software agent monitoring tool passing through the automated BV-WSS.

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4 Experimental Results

The following chapter presents the experimental results from the proposed soft-ware agent monitoring tool. The chapter begins with a description of the exper-imental scenario and the tools that were used to test the implementation. Then, it describes the behavior and comparison of optical module relevant parameters, mainly OSNR, Pre-FEC BER and optical input power with filter narrowing and attenuation. The experimental measurement result also shows the correlation be-tween OSNR, Q factor, Pre-FEC BER for random filtering and attenuation values. Finally, this chapter illustrates the behavior of Pre-FEC BER introducing OSNR degradation in real-time monitoring.

4.1 Test Environment

The validation of the developed software agent tool has been tested by running on the following environment variables.

The programming language selected and utilized to implement the tool is Python (version 2.7.13), a multi-paradigm programming language. Such a language has been chosen because it is particularly better for network monitoring and data pro-cessing. The tool uses an Intel(R) Core i5-2440 with four 3.10 GHz cores and 4 GB RAM. Each processor has 7 MB L3 and 1 MB L2 cache. Besides, the automation tool for BV-WSS uses Intel Atom mininet with i686 architecture, L1i cache of 32 K, L2 cache of 512 K.

The tests have been conducted on the testbed that includes ROADMs, 100Gb/s coherent transponders and a 320-km amplified optical link. Time values are ob-tained by calling a python function that gives the system’s current time-stamp since epoch in seconds. Such values are used because they give accurate time to see the behavior of monitored parameters on the elapsed time.

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Figure 4.1: Schematic of the experimental setup

Figure 4.1 shows the specified testbed scheme. SPO1 and SPO2 are the com-mercial ROADMs as receiver and transmitter respectively. Each ROADMs have 100Gb/s coherent transponder passing through automated BV-WSS, and the opti-cal link is 320 km long with EDFA. It is on first BV-WSS (BV-WSS1) automation script of filtering and attenuation implemented which runs corresponding to the software agent monitoring tool.

The following section presents the comparison and characteristics of OSNR, Pre-FEC BER and optical amplifier input power introducing different filtering and attenuation values.

4.2 Measurement with Filtering

The monitoring tool uses the following filter setup during measurement of opti-cal module parameters. The configuration of BV-WSS filter has been automated by adapting the filter bandwidth around the same central channel frequency of 193.1 THz configured at optical module (SPO1 Tx and Rx). In particular, the program selects 50 GHz as larger filter width and it changes the width every five minutes by reducing the bandwidth in 2 GHz granularity difference until it reaches 26 GHz frequency filter, which is the minimum limit the signal is correctly de-tected. Indeed, for values smaller than 26 GHz, the signal is not correctly detected and the SPO1 and the card reports signal down. On all filter grids, the change in the bandwidth is symmetric to within +/- 1 GHz around the center of the filter. The following subsection describes evaluation of OSNR and Pre-FEC BER by applying filtering.

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4.2.1 OSNR and Pre-FEC BER using Filtering

The figure 4.2 shows Pre-FEC BER and OSNR measured by the developed tool at receiver side with changes in values of filters. In both parametric measurements, the filters (50-26GHz) have 2 GHz width difference. The tool computes monitoring parameters every 2 seconds and changes in a filter width will be made after five minutes. Thus, in this evaluation of each filters size, averaged parameters values of the specified time is considered.

Figure 4.2: OSNR vs. Pre-FEC BER introducing filter size

As it can be observed from Fig. 4.2(a), for filter width values larger than 44 GHz,

the signal experiences the lowest Pre-FEC BER value, which is around 2.6 x 10-6.

This value is much lower than the FEC limit (equal to 1 x 10-4_{). With 26 GHz}

width filter, the signal is still correctly detected and almost half of the bandwidth can be saved. On the other hand, from Fig. 4.2(b) the effect of changing the filter

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width with respect to OSNR is seen. As the width of filter linearly increases, the performance of the optical link becomes better with the increase of OSNR values. In particular, at 50 GHz filter width the signal experiences 21.74 dB OSNR value, which is the maximum value where best signal quality is reached.

4.3 Measurement applying Automated Attenuation Control

The Attenuation control provides a power control function for the current filter. In this scheme, the selection of filter width follows the ITU-T flex-grid standard (i.e., it is a multiple of 12.5 GHz). So, attenuation values are applied to these selected filters of 50 GHz, 38 GHz (thus, 37.5 GHz in the ITU-T flex grid), 32GHz as best sample rate between the flex grids and 26GHz (translates to an ITU-T flex-grid width of 25 GHz). Moreover, the connection is like the previous one with a central frequency of 193.1 THz. For each selected filter width, the automated program applies every five minutes 2 dB attenuation multiples on the BV-WSS.

4.3.1 Pre-FEC BER with Attenuation and filtering effects

Figure 4.3 demonstrates the comparison of BER for selected four filter con-figurations. Attenuation value is set in 2 dB multiples starting at 0 dB (linear reference point) to 16 dB. It is possible to notice from the graph that increasing the attenuation value on the link maximizes the BER, in turn, high uncorrectable bit errors. The effect of attenuation on Pre-FEC BER is excessively high (i.e.

averagely 1.25 x 10-2_{) on 26 GHz frequency filter introducing 14 dB attenuation}

value. This Pre-FEC BER value is higher than the transponder critical FEC

threshold and FEC degradation limit (i.e. 1 x 10-3 _{and 1 x 10}-4 _{respectively).}

Thus, the module raises Pre-FEC exceed threshold warning and an outage is ex-perienced for attenuation values above 14 dB. Besides, for the 32 GHz, 38 GHz, 50 GHz filters width, the BER gets larger when attenuation value increases. On these filters width, the signal is correctly detected up to 16 dB attenuation size. Above this value, the link experience in loss of signal and the status of receiver card goes down.

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0 2 4 6 8 10 12 14 16 Attenuation Size [dB] 10-6 10-5 10-4 10-3 10-2 10-1 Log [BER] 50GHz Frequency filter 38GHz Frequency filter 32GHz Frequency filter 26GHz Frequency filter

Figure 4.3: Pre-FEC BER of 4 filters applying automated attenuation control

The gap between [38GHz 50GHz] filters is smaller compared to [26 GHz 32GHz] filters, this is because the monitoring tool first considers the effect of filter changes before applying the attenuation values such that on these selected filters the BER

difference on the first range is 1.28 x 10-6 _{and the latter has 6.25 x 10}-5_{. Thus, the}

effect of attenuation does not alter the gap rather its effect is on each single filter BER values.

4.3.2 OSNR Comparison with Attenuation

The measurement result for the OSNR is shown in figure 4.4. when attenua-tion value increases for the selected filters, the estimated OSNR value decreases: thus, the quality of signal reduces. The gap difference between [26GHz 32GHz] and [38 GHz 50GHz] follows the same scenario of Pre-FEC BER but in indirect proportionality. Here the first range has 1.2516 dB OSNR value difference, which is higher compared to 0.3214 dB OSNR value difference of the second range.

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It is observed from the result that the link experiences highest OSNR value at 50 GHz filter of 0 dB attenuation (i.e. 21.7423 dB) and lowest value at 26 GHz filter width with 14 dB attenuation (i.e. 13.9404 dB).

0 2 4 6 8 10 12 14 16 Attenution size [dB] 14 15 16 17 18 19 20 21 22 OSNR [dB] 50GHz Frequency Filter 38GHz Frequnecy Filter 32Ghz Frequency Filter 26GHz Frequecny Filter Attenuation Size [dB]

Figure 4.4: Estimated OSNR of 4 filters applying automated attenuation control

4.3.3 Input Power to BER Performance

Figure 4.5 shows the effect of optical amplifier input power on BER performance in a variety of attenuation. Thus, 50 GHz frequency filter is given the linear reference point. The purpose of this characterization is to identify the behavior of BER measurement for the operation pass through optical amplifier input power. Attenuation value is set between 0 dB to 16 dB thus the values of BER and input power are averaged values on this attenuation size. From the result, it is identified minimum input power required by the device ROADM (SPO1 Rx) to operate in a satisfactory condition. As it can be seen from the figure at -26.1 dBm input

power value gives BER measurement reading 2.27 x 10-6, which is lower than the

transponder FEC limit (i.e. equal to 1 x 10-4_{). In addition, it is analyzed the}

smaller is the BER, the greater the power required to transmit the signal with a good quality.

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-32 -31.5 -31 -30.5 -30 -29.5 -29 -28.5 -28 -27.5 -27 -26.5 -26 10-6 10-5 10-4 10-3 10-2

Optical Amplifier (A1) Input Power

Figure 4.5: Input Power vs. BER at 50GHz filter width

The following fig. 4.6 shows the behavior of optical amplifier input power with different attenuation values in four selected frequency filters. This measurement result helps to extract the effect of input power on BER. It can be noticed from the graph at 0 dB attenuation, all frequency filters return the same input power results of -26.1 dBm. 0 2 4 6 8 10 12 14 16 -32 -31 -30 -29 -28 -27 -26 50GHz Frequency Filter 38GHz Frequency Filter 32GHz Frequency Filter 26GHz Frequency Filter Addd Attenuation [dB]

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This value is equal to the value of the previous result where 2.27 x 10-6 BER reading is obtained. This shows the receiver (i.e. SPO1 Rx) experiences the best quality of signal in the value of -26.1 dBm at which maximum 21.74 dB OSNR value is reached.

4.4 Evaluation applying Random Technique

This section presents the transmission parameters result obtained from apply-ing random function. The random function is used to check the validity of the developed tool in measuring physical impairments and transmission parameters when there is random degradation on the link. Moreover, these tests have been specifically required to train a machine learning algorithm aiming at detecting soft-failures in optical transmission systems (note that such algorithm is outside the scope of this thesis). The application randomly selects from the list of filters or attenuation values and every five minutes applies to the BV-WSS where the receiver link is passing on. Using this scheme, the test has been categorized into random filtering, random attenuation and timed-random.

The following subsection presents the result obtained applying these three meth-ods.

4.4.1 OSNR and BER introducing Randomly-generated impairments

Figure 4.7 shows the behavior of OSNR and BER when random filtering is applied on the link. Taking selected thirteen filters and five minutes change, the test result is a total of one hour and five minutes. From the graph, it can be observed that at time 21:28:52, a 30 GHz filter width is applied, which results in 0.5 dB OSNR degradation taking 50 GHz as a reference filter point. As result,

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21:00 21:10 21:20 21:30 21:40 21:50 22:00 Time [Hour:Minute] 14 15 16 17 18 19 20 21 22 OSNR [dB] Est-OSNR Random Filter size

21:00 21:10 21:20 21:30 21:40 21:50 22:00 Time [Hour:Minute] 10-6 10-5 10-4 10-3 10-2 Log [BER] Pre-FEC BER Random Filter Size a) OSNR with random filtering

B) Pre-FEC BER with random filtering

Figure 4.7: Estimated OSNR vs. Pre-FEC BER with random filtering

Besides the results that have been used for the measurement comparison in figure 4.7, the tool also returns activities that are applied on each time in the form of a logfile. This reference of randomly selected filters at each time is illustrated in Table 4.1 below. The table is categorized based on filter width and the time-stamp of randomly applied filters width. The gap specified in this table shows the width of the filter in THz on both sides. With central channel frequency of 193.1 THz, the gap is applied on both sides (i.e. frequency + gap), (central-frequency - gap)) such that final width is two times the gap. For example, in the tab. 4.1 a gap (0.022) is to show 0.22 THz width is applied on both sides, which

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is equivalent to 44 GHz filter width.

Filter Width Time-Stamp

gap= 0.022 Sun May 28 20:58:42 2017

gap= 0.023 Sun May 28 21:03:43 2017

gap= 0.018 Sun May 28 21:08:45 2017

gap= 0.019 Sun May 28 21:13:47 2017

gap= 0.020 Sun May 28 21:18:48 2017

gap= 0.021 Sun May 28 21:23:50 2017

gap= 0.015 Sun May 28 21:28:52 2017

gap= 0.018 Sun May 28 21:33:53 2017

gap= 0.022 Sun May 28 21:38:55 2017

gap= 0.023 Sun May 28 21:43:57 2017

gap= 0.023 Sun May 28 21:48:58 2017

gap= 0.019 Sun May 28 21:54:00 2017

gap= 0.021 Sun May 28 21:59:02 2017

Table 4.1: Random filter width selection activities take placed in the specified time, i.e reference mark of the random filtering 4.7 experiment.) 4.4.2 OSNR and BER on Random Attenuation

The OSNR and BER measurement results by applying random attenuation is presented on fig. 4.8. The performance of optical link signal degrades when high attenuation value is applied. It can be observed from the figure at 11:15:43 time-stamp, the link experiences BER degradation with Pre-FEC BER value of 1.30 x

10-3. This value is above excessive FEC threshold limit (10 -3), which results to

have the lowest estimated OSNR value. In addition, the figure provides a clear view on the behavior of BER and OSNR in that for each random change as the value of Pre-FEC BER becomes greater, the value of OSNR becomes smaller and vice versa.

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Figure 4.8: Estimated OSNR vs. Pre-FEC BER with random attenuation

4.4.3 OSNR and Q factor using Random Attenuation

The correlation of Q factor and OSNR is presented in figure 4.9. It has been given a 50 GHz filter as a linear point and the random function is applied as the earlier setup. It can easily be noticed, as the performance of the OSNR value gets up, the value of the Q factor increases. Moreover, from results of OSNR, the receiver experiences a low quality of signal at the time-stamp of (23:16:43-23:21:44). This is because, in this time range the randomly applied attenuation value is (16 dB), which results to have averagely 14 dB OSNR and 8 dB Q factor values.

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22:30 22:40 22:50 23:00 23:10 23:20 23:30 8 10 12 14 16 18 20 22 OSNR Q Factor

Figure 4.9: Estimated OSNR vs. Q factor with random attenuation

4.5 Timed-Random Test

In the above section, we have random function individually applying filtering and attenuation. In this section, we use timed-random selection scheme where the program applies for the first one-hour random filtering only and the second-hour random list of attenuation only. This effect will be applied to BV-WSS at which receiver transmission link is passing on.

The following subsection describes the results of OSNR and Pre-FEC BER apply-ing this method.

4.5.1 OSNR and Pre-FEC BER based on Timed-Random

Figure 4.10 shows OSNR with respect to Pre-FEC BER applying timed random function. The total retrieved result is two hours since the program uses one hour for each random selection of attenuation and filtering. It is possible to notice that at 09:32:36 time-stamp OSNR decreased to 19 dB. As a result, the pre-FEC BER

value increases to 7.52 x 10-5_{. This Pre-FEC BER value is lower than minimum}

FEC threshold (i.e. 10-4_). _{Moreover, the largest BER degradation occurs at}

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BER reading is higher (i.e. 1.30 x 10-3) which is over FEC limit. To clarify the above results, the first one is due to filtering (i.e. 26GHz filter width) and the second one is due attenuation (i.e. 50GHz with 16 dB attenuation). Thus, the effect of filtering on both OSNR and Pre-FEC BER value is less compared to applying attenuation.

Figure 4.10: Estimated OSNR vs. Pre-FEC BER applying randomly-timed at-tenuation and filtering

The following section presents the behavior of Pre-FEC BER when there is OSNR degradation.

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4.5.2 Comparison of Pre-FEC BER on OSNR degradation

The comparison of Pre-FEC BER introducing OSNR degradation is illustrated in figure 4.11. This Pre-FEC BER observation is taken from the result of the timed-random function. The fig. 4.11 shows comparison of Pre-FEC BER tak-ing 1 dB OSNR degradation when there is filter narrowtak-ing and attenuation. As reference baseline, OSNR averaged maximum value (i.e. 21.34 dB) is considered. Nevertheless, it is difficult to find common correlation point for demonstrating more OSNR degradation points due to filtering and attenuation effect difference. Therefore, the case of the 1 dB OSNR degradation is interesting to assess Pre-FEC BER behavior. In this case, the 1 dB OSNR degradation is experienced for attenuation only at 50 GHz with 6 dB attenuation, and for filtering case at 32 GHz filter width. In both cases, the five-minutes results of each second averaged values are considered. As it can be seen the pre-FEC BER value of 50 GHz with

6 dB attenuation (i.e. 1.46 x 10-5_{) is greater than pre-FEC BER value of 32 GHz}

frequency filter (i.e. 1.14 x 10-5_{) with no attenuation. As a result, the 50 GHz}

filter with 6 dB attenuation will have a low quality of signal compared to the 32 GHz filter width with no attenuation. In addition, fig 4.11(a) shows at time of [38:25-39:00] Pre-FEC BER value slightly increases, it is the time at which we do not have 1 dB OSNR degradation and the remaining time presents when we have the 1 dB OSNR degradation that had been compared to 32 GHz filtered case. The result helps to conclude on the same OSNR degradation point the value of Pre-FEC BER with attenuation is relatively higher than the value of Pre-FEC BER with only filtering.

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39:00 40:00 41:00 42:00 43:00 Time [Minute] 10-6 10-5 10-4 Log [BER] Pre-FEC BER 50GHz Attenution=6dB 09:00 10:00 11:00 12:00 13:00 Time[Minute] 10-6 10-5 10-4 Log [BER] Pre-FEC BER 32GHz Filter Size Attenuation = 6dB 50GHz 32 GHz Filter Width

Real-Time Management of Reconfigurable Optical Add-Drop Multiplexers (ROADMs)

University of Pisa and Scuola Superiore Sant’Anna

Master Thesis