We propose a data-driven approach based on Machine Learning (ML) to predict control signals of a photonic switching system. The proposed ML agent is trained and tested in a completely topological and technological agnostic way and we envision its application in real-time control-planes

Bardella, Paolo

Carena, Andrea

Curri, Vittorio

Ghillino, Enrico

Khan, Ihtesham

Masood, Muhammad Umar

Tunesi, Lorenzo

English

PORTO@iris (Publications Open Repository TOrino - Politecnico di Torino)

16 July 2022POLITECNICO DI TORINORepository ISTITUZIONALEA Data-Driven Approach to Autonomous Management of Photonic Switching System / Khan, Ihtesham; Masood,Muhammad Umar; Tunesi, Lorenzo; Ghillino, Enrico; Bardella, Paolo; Carena, Andrea; Curri, Vittorio. - ELETTRONICO.- (2021), pp. 1-2. ((Intervento presentato al convegno IEEE Photonics Society Summer Topicals Meeting Series (SUM)tenutosi a Cabo San Lucas, Mexico nel 19-21 July 2021 [10.1109/SUM48717.2021.9505780].OriginalA Data-Driven Approach to Autonomous Management of Photonic Switching SystemIEEE postprint/Author's Accepted ManuscriptPublisher:PublishedDOI:10.1109/SUM48717.2021.9505780Terms of use:openAccessPublisher copyright©2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in anycurrent or future media, including reprinting/republishing this material for advertising or promotional purposes, creatingnew collecting works, for resale or lists, or reuse of any copyrighted component of this work in other works.(Article begins on next page)This article is made available under terms and conditions as specified in the  corresponding bibliographic description inthe repositoryAvailability:This version is available at: 11583/2917842 since: 2021-08-15T14:41:49ZIEEEA Data-Driven Approach to AutonomousManagement of Photonic Switching SystemIhtesham KhanDET, Politecnico di Torino, Italy,ihtesham.khan@polito.itM Umar MasoodDET, Politecnico di Torino, Italy,muhammad.masood@polito.itLorenzo TunesiDET, Politecnico di Torino, Italy,tunesi.lorenzo@gmail.comEnrico GhillinoSynopsys, Inc., 400,United States.enrico.ghillino@synopsys.comPaolo BardellaDET, Politecnico di Torino, Italypaolo.bardella@polito.itAndrea CarenaDET, Politecnico di Torino, Italy,andrea.carena@polito.itVittorio CurriDET, Politecnico di Torino, Italycurri@polito.itAbstract—We propose a data-driven approach based on Ma-chine Learning (ML) to predict control signals of a photonicswitching system. The proposed ML agent is trained and testedin a completely topological and technological agnostic way andwe envision its application in real-time control-planes.Index Terms—Data-Driven Science, Machine Learning, OpticalSwitches, Photonic Integrated Circuits.I. INTRODUCTIONNowadays, network components are extensively exploitingPICs to perform complex functions at the photonic level,avoiding the bottleneck of opto-electronic conversion. PICsare a promising technology for next generation of photonicswitches systems due to their low energy consumption, lowerlatency and small foot-print. These attributes of photonicswitching systems suggest a large adoption in future coreoptical networks and data centers, allowing dynamic pro-visioning to satisfy connection requests. This progressivelyincreasing trend requires an automatic management of PIC-based switching devices as their complexity will increase. Inthis scenario, Software defined networking (SDN) becomesan important paradigm for the softwarized and autonomousmanagement of photonic switches based on PICs.Typically, PIC based switches rely internally on elementary2×2 cells such as Mach-Zehnder interferometers switches(MZI) or optical Micro ring resonators based filters (MRR).The flow of light inside these building units can be routedto different paths by an electrical control signal [1], [2].Given a N×N switch, control signals can be represented bya binary vector of M elements, where M is the number ofelementary 2×2 blocks. When Mi = 1, the i-th individualswitch element is set to cross condition ([0, 1] → [1, 0])while for Mi = 0 it is set to bar condition([0, 1] → [0, 1]).The considered generic switch structure and application isdepicted in Fig. 1: by varying the M control signals, theN input signals can be routed to the output ports orderedas any possible permutation. These 2×2 switches can beinterconnected according to different topologies to obtain aN × N transparent switch. Each topological configuration ischaracterized by some special properties: collision avoidanceamong the input channels, minimization of optical losses, pla-narity, reduction of the circuit footprint and of the operationalpower consumption [3]. Most optical non-blocking switchingnetworks shown in the literature are based on the Banyanswitch or the Clos network: they can be described as multi-stage crossover switches obtained through a 2×2 base element,with alternating stages of differently arranged switches andinterconnect crossings. Starting from these general topologies,through optimization and substitution of elements inside thenetwork, ad-hoc optimized configurations can be obtained.The Honey-Comb Rearrangeable Optical Switch (HCROS)[4], is an example of these type of switches. With respectto traditional topological implementations [5], this networkshows an overall less regular structure, as well as stageasymmetry, typically absent in traditional switching networks.As for any crossbar-based network, the number of availableunique control signals correspond to 2M , while, given thenon-blocking property, each of the N ! unique permutations ofthe N input signals is achievable. The original 6×6 HCROShas been expanded to a 12×12 structure, to increase its com-plexity to a non-trivial case for ML approach. Deterministicalgorithms can efficiently calculate the control states of theinternal switches to obtain the requested signals permutation,unfortunately each algorithm is specifically designed for theFig. 1: Generic N×N optical switch fabric.Optical Network ControllerOptical SwitchingFabricHW abstractionFig. 2: Software-defined Open Optical Networks.internal topology of the network, and no general-purpose algo-rithm exists [3] with a reasonable and scalable computationalcost.In this work, we propose to train a topology agnosticmachine learning (ML) agent to predict control signal states.The proposed approach is adopted in the context of a SDN: weintroduce the concept of a softwarized and autonomous con-figuration of any PIC based optical switching system depictedin Fig. 2. Given a N ×N photonic switch with arbitrary, andpotentially unknown, internal structure, the proposed techniqueassumes it as a black-box component and it efficiently allowsto determine control states with accuracy exceeding 97%.II. ML FRAMEWORK, RESULTS AND CONCLUSIONTo generate the ML training and testing dataset, we con-sidered a N × N HCROS switch. A subset of the total 2Mcontrol combinations is used for the generation of the dataset,as reported in Tab. Ia. The dataset is used to train a supervisedneural network in the learning phase. The proposed ML model,explicitly a deep neural network (DNN), is developed byusing TensorFlow© platform: it incorporates three hidden-layers with 35 neurons for each hidden layers. The proposedDNN model exploited ReLU as activation function and itis evaluated by mean square error (MSE) as loss function.The DNN model is configured for training-steps of 1,000and learning-rate of 0.01. The train set for 12 × 12 HCROStopology consists of 70% of the dataset, while the test setconsists of 30% of dataset, as reported in Tab. Ia. Furthermore,the proposed ML agent exploit the different combination ofwavelengths at the output ports as features and M controlsignals as labels.The effect of various ML parameters, especially the increaseof the hidden-layer size of the proposed DNN model, is ana-lyzed in terms of single ring training time depicted in Fig. 3a,for a an Intel® Core™ i7 6700 3.4 GHz CPU workstationequipped with 32 GB of 2133 MHz RAM. The results revealthat the single switching unit training time linearly increaseswith increasing the number of neurons per hidden-layer. Alongwith this, the effect of increasing the number of neurons perhidden-layer is also shown in Fig. 3b as an improvement inthe prediction ability of ML model, up to certain extent, afterwhich it remains constant. Finally in Fig. 3c, the effect ofthe total considered training data size reported in Tab. 1 is5 10 15 20 25 30 35 40"Neurons/Hidden layer"3070110150190230Training time (min)(a)5 10 15 20 25 30 35 40Neurons/Hidden layer0255075100Correct Prediction %(b)10 20 30 40 50 60 70 80 90 100Training Data %405060708090100Correct Prediction %(c)Fig. 3: Neural Network Analysis.also revealed. The trend shows that the prediction ability ofML model improves with the increasing training data size.The encircled values reported in Fig. 3 are considered fordeveloping the cognition in DNN engine. Promising results areachieved with the considered HCROS topology, as reported inTab. Ib. and an excellent level of accuracy (>97%) is achieved.To improve the prediction capabilities of the ML approach,we added an auxiliary step based on a simple heuristic thatwe derived from observing wrong configurations. The majorityof errors in the prediction of the control state are due to asingle switch element in the incorrect state. The heuristic wepropose is to simply try the correction of single switch errorsby flipping one switch at a time and comparing the outputsequence against the desired output. For HCROS, using MLassisted by heuristic the accuracy improves to 100%.In conclusion, we have demonstrated that a ML approachcan effectively determine control states for a generic N ×Nphotonic switch without any knowledge required on the topol-ogy. The ML is scalable to large N as we show high level ofaccuracy with limited size dataset.TABLE I: Dataset and results for a 12×12 HCROS network.(a) Dataset StatisticsPermutations (N !) 4,79×106Switches (M ) 36Combinations (2M ) 68×109Dataset 3×105Training set 210,000Test set 90,000(b) ML prediction resultsNeurons / hidden layer 35Accuracy (no heuristic) 97.83%Single ring error 2.17%Multiple rings error 0%Accuracy (with heuristic) 100%REFERENCES[1] E. Ghillino et.al, “Assessing the impact of design options for an opticalswitch in network routing impairments,” in 21st ICTON, (2019), pp. 1–4.[2] I. Khan et.al, “Machine learning assisted abstraction of photonic inte-grated circuits in fully disaggregated transparent optical networks,” in22nd ICTON, (2020), pp. 1–4.[3] S. Arora et.al, “On-line algorithms for path selection in a nonblockingnetwork,” in Proceedings of the twenty-second annual ACM symposiumon Theory of computing, (1990), pp. 149–158.[4] M. Yahya et.al, “Honeycomb ROS: A 6 × 6 non-blocking optical switchwith optimized reconfiguration for ONoCs,” Electronics 8, 844 (2019).[5] R. A. Spanke and V. E. Benes, “N-stage planar optical permutationnetwork,” Appl. Opt. 26, 1226–1229 (1987).

A Data-Driven Approach to Autonomous Management of Photonic Switching System

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A Data-Driven Approach to Autonomous Management of Photonic Switching System

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