Alba P. Vela

Designing, Operating, and Reoptimizing Elastic Optical Networks

Emerging services and applications demanding high bitrate and stringent quality of service requirements are pushing telecom operators to upgrade their core networks based on wavelength-division multiplexing (WDM) to a more flexible technology for the more dynamic and variable traffic that is expected to be conveyed. Finally, academy- and industry-driven research on elastic optical networks (EON) has turned out into a mature enough technology ready to gradually upgrade WDM-based networks. Among others, key EON features include flexible spectrum allocation, connections beyond 100 Gb/s, advanced modulation formats, and elasticity against time-varying traffic. As a consequence of the variety of features involved, network design and algorithms for EONs are remarkably more complex than those for WDM networks. However, new opportunities for network operators to reduce costs arise by exploiting those features; in fact, the classical network life cycle based on fixed periodical planning cycles needs to be adapted to greatly reduce overprovisioning by applying reoptimization techniques to reconfigure the network while it is in operation and to efficiently manage new services, such as datacenter interconnection that will require provisioning multicast connections and elastic spectrum allocation for time-varying traffic. This paper reviews and extends mathematical models and algorithms to solve optimization problems related to the design, operation, and reoptimization of EONs. In addition, two use cases are presented as illustrative examples on how the network life cycle needs to be extended with in-operation planning and data analytics thus adding cognition to the network.

Distributing data analytics for efficient multiple traffic anomalies detection

Traffic anomalies can create network congestion, so its prompt and accurate detection would allow network operators to make decisions to guarantee the network performance avoiding services to experience any perturbation. In this paper, we focus on origindestination (OD) traffic anomalies; to efficiently detect those, we study two different anomaly detection methods based on data analytics and combine them with three monitoring strategies. In view of the short monitoring period needed to reduce anomaly detection, which entails large amount of monitoring data to be collected and analyzed in a centralized repository, we propose bringing data analytics to the network nodes to efficiently detect traffic anomalies, while keeping traffic estimation centralized. Once an OD traffic anomaly is detected, a network reconfiguration can be triggered to adapt the network to the new traffic conditions. However, an external event might cause multiple related traffic anomalies. In the case of triggering a network reconfiguration just after one traffic anomaly is detected, some Key Performance Indicators (KPI) such as the number of network reconfigurations and the total reconfiguration time would be unnecessarily high. In light of that, we propose the Anomaly and Network Reconfiguration (ALCOR) method to anticipate whether other ODs are anomalous after detecting one anomalous OD pair. Exhaustive simulation results on a realistic network scenario show that the monitoring period should be as low as possible (e.g., 1min) to keep anomaly detection times low, which clearly motivates to place traffic anomaly detection function in the network nodes. In the case of multiple anomalies, results show that ALCOR can significantly improve KPIs such as the number of network reconfigurations, total reconfiguration time, as well as traffic losses.

Soft failure localization during commissioning testing and lightpath operation

In elastic optical networks (EONs), effective soft failure localization is of paramount importance to early detection of service level agreement violations while anticipating possible hard failure events. So far, failure localization techniques have been proposed and deployed mainly for hard failures, while significant work is still required to provide effective and automated solutions for soft failures, both during commissioning testing and in-operation phases. In this paper, we focus on soft failure localization in EONs by proposing two techniques for active monitoring during commissioning testing and for passive in-operation monitoring. The techniques rely on specifically designed low-cost optical testing channel (OTC) modules and on the widespread deployment of cost-effective optical spectrum analyzers (OSAs). The retrieved optical parameters are elaborated by machine learning-based algorithms running in the agents node and in the network controller. In particular, the Testing optIcal Switching at connection SetUp timE (TISSUE) algorithm is proposed to localize soft failures by elaborating the estimated bit-error rate (BER) values provided by the OTC module. In addition, the FailurE causE Localization for optIcal NetworkinG (FEELING) algorithm is proposed to localize failures affecting a lightpath using OSAs. Extensive simulation results are presented, showing the effectiveness of the TISSUE algorithm in properly exploiting OTC information to assess BER performance of quadrature-phase-shift-keying-modulated signals, and the high accuracy of the FEELING algorithm to correctly detect soft failures as laser drift, filter shift, and tight filtering.

Early pre-FEC BER degradation detection to meet committed QoS

Early optical layer BER degradation detection is proposed to trigger affected demands re-routing, targeting at reducing SLA violation. Results show that the proposed detection and re-routing algorithms noticeably reduce bandwidth and number of demands affected.

Traffic generation for telecom cloud-based simulation

With the incremental amount of applications running over the telecom cloud architecture it is becoming of paramount importance being able to run simulations aiming at evaluating the performance of such applications. To that end, one of the key elements in the simulation is how to generate network traffic. In this paper we propose realistic traffic functions that can be used for such purposes and present how those functions have been integrated in our OMNET++-based simulator.

An Architecture to Support Autonomic Slice Networking

Network slices combine resource virtualization with the isolation level required by future 5G applications. In addition, the use of monitoring and data analytics help to maintain the required network performance, while reducing total cost of ownership. In this paper, an architecture to enable autonomic slice networking is presented. Extended nodes make local decisions close to network devices, whereas centralized domain systems collate and export metered data transparently to customer controllers, all of them leveraging customizable and isolated data analytics processes. Discovered knowledge can be applied for both proactive and reactive network slice reconfiguration, triggered either by service providers or customers, thanks to the interaction with state-of-the-art software-defined networking controllers and planning tools. The architecture is experimentally demonstrated by means of a complex use case for a multidomain multilayer multiprotocol label switching (MPLS)-over-optical network. In particular, the use case consists of the following observe–analyze–act loops: 1) proactive network slice rerouting after bit error rate (BER) degradation detection in a lightpath supporting a virtual link (vlink); 2) reactive core network restoration after optical link failure; and 3) reactive network slice rerouting after the degraded lightpath is restored. The proposed architecture is experimentally validated on a distributed testbed connecting premises in UPC (Spain) and CNIT (Italy).

Combining a machine learning and optimization for early pre-FEC BER degradation to meet committed QoS

Monitoring the physical layer is key to detect bit error rate (BER) degradation caused by failures and to identify the cause of the failure and localize the failed elements. Once the failure has been detected, actions can be taken to reduce as much as possible its impact on the network. Commercially available optical equipment are able to correct degraded optical signals by means of Forward Error Correction (FEC) algorithms. A value of pre-FEC BER over a pre-defined threshold would imply a non-error-free post-FEC transmission and, as a result, communication would be disrupted. Therefore, a prompt detection of lightpaths with excessive pre-FEC BER can help to greatly reduce such SLA violations, in particular when supporting vlinks. As a result of the above, it would be desirable to anticipate such degradations and apply re-optimization to re-route those affected demands according to their SLAs in order to reduce the affected traffic after the degradation is detected. Designing algorithms capable of promptly detect distinct BER anomaly patterns would be desirable. The objective would be to anticipate intolerable BER values as much as possible aiming at leaving enough time to plan a re-routing procedure during off-peak hours. In this paper, we propose an effective machine learning-based algorithm to localize and identify the most probable cause of failure impacting a given service, as well as a re-optimization algorithm to re-route affected demands, targeting at reducing SLA violation. Results show that the proposed detection and re-routing algorithms noticeably reduce bandwidth and number of demands affected.

BER Degradation Detection and Failure Identification in Elastic Optical Networks

Optical connections support virtual links in MPLS-over-optical multilayer networks and therefore, errors in the optical layer impact on the quality of the services deployed on such networks. Monitoring the performance of the physical layer allows verifying the proper operation of optical connections, as well as detecting bit error rate (BER) degradations and anticipating connection disruption. In addition, failure identification facilitates localizing the cause of the failure by providing a short list of potential failed elements and enables self-decision making to keep committed service level. In this paper, we analyze several failure causes affecting the quality of optical connections and propose two different algorithms: one focused on detecting significant BER changes in optical connections, named as BANDO, and the other focused on identifying the most probable failure pattern, named as LUCIDA. BANDO runs inside the network nodes to accelerate degradation detection and sends a notification to the LUCIDA algorithm running on the centralized controller. Experimental measures were carried out on two different setups to obtain values for BER and received power and used to generate synthetic data used in subsequent simulations. Results show significant improvement anticipating maximum BER violation with small failure identification errors.

Reducing Virtual Network Reconfiguration and Traffic Losses under Multiple Traffic Anomalies

Multiple OD traffic anomalies can arise exceeding pre-planned capacity and causing congestion. We propose to detect anomalous and suspicious ODs to reduce virtual network reconfiguration and total traffic losses. Results show savings higher than 25%.

Bringing data analytics to the network nodes for efficient traffic anomalies detection

Traffic anomalies can create network congestion, so its prompt and accurate detection would allow network operators to make decisions to guarantee the network performance avoiding services to experience any perturbation. In this paper, we focus on origin-destination (OD) traffic anomalies; to efficiently detect those, we study two different anomaly detection methods based on data analytics and combine them with three monitoring strategies. In view of the short monitoring period needed to reduce anomaly detection, which entails large amount of monitoring data to be collected and analyzed in a centralized repository, we propose bringing data analytics to the network nodes to efficiently detect traffic anomalies, while keeping traffic estimation centralized. Exhaustive simulation results on a realistic network scenario show that the monitoring period should be as low as possible (e.g., 1 min) to keep anomaly detection times low, which clearly motivates to place traffic anomaly detection function in the network nodes.