Failure Localization Aware Protection in All-Optical Networks

The recent development of optical signal processing and switching makes the all-optical networks a potential candidate for the underlying transmission system in the near future. However, despite its higher transmission data rate and efficiency, the lack of optical-electro-optical (OEO) conversions makes fault management a challenge. A single fiber cut can interrupt several connections, disrupting many services which results in a massive loss of data. With the ever-growing demand for time-sensitive applications, the ability to maintain service continuity in communication networks has only been growing in importance. In order to guarantee network survivability, fast fault localization and fault recovery are essential. Conventional monitoring-trail (m-trail) based schemes can unambiguously localize link failures. However, the deployment of m-trail requires extra transceivers and wavelengths dedicated to monitoring the link state. Non-negligible overhead makes m-trail schemes neither scalable nor practicable. In this thesis, we propose two Failure Localization Aware (FLA) routing schemes to aid failure localization. When a link fails, all traversing lightpaths become dark, and the transceiver at the end node of each interrupted ligthpath issues an alarm signal to report the path failure. By correlating the information of all affected and unaffected paths, it is possible to narrow down the number of possible fault locations to just a few possible locations. However, without the assistance of dedicated supervisory lightpaths, and based solely on the alarm generated by the interrupted lightpaths, ambiguity in failure localization may be unavoidable. Hence, we design a Failure Localization Aware Routing and Wavelength Assignment (FLA-RWA) scheme, the Least Ambiguous Path (LAP) routing scheme, to dynamically allocate connection requests with minimum ambiguity in the localization of a link failure. The performance of the proposed heuristic is evaluated and compared with traditional RWA algorithms via network simulations. The results show that the proposed LAP algorithm achieves the lowest ambiguity among all examined schemes, at the cost of slightly higher wavelength consumption than the alternate shortest path scheme. We also propose a Failure Localization Aware Protection (FLA-P) scheme that is based on the idea of also monitoring the protection paths in a system with path protection for failure localization. The Least Ambiguous Protection Path (LAPP) routing algorithm arranges the protection path routes with the objective of minimizing the ambiguity in failure localization. We evaluate and compare the ambiguity in fault localization when monitoring only the working paths and when monitoring both working and protection paths. We also compare the performance of protection paths with different schemes in regards to fault localization

ENHANCING THE PERFORMANCE AND SECURITY OF ANONYMOUS COMMUNICATION NETWORKS

With the increasing importance of the Internet in our daily lives, the private information of millions of users is prone to more security risks. Users data are collected either for commercial purposes and sold by service providers to marketeers or political purposes and used to track people by governments, or even for personal purposes by hackers. Protecting online users privacy has become a more pressing matter over the years. To this end, anonymous communication networks were developed to serve this purpose. Tors anonymity network is one of the most widely used anonymity networks online; it consists of thousands of routers run by volunteers. Tor preserves the anonymity of its users by relaying the traffic through a number of routers (called onion routers) forming a circuit. Tor was mainly developed as a low-latency network to support interactive applications such as web browsing and messaging applications. However, due to some deficiencies in the original design of Tors network, the performance is affected to the point that interactive applications cannot tolerate it. In this thesis, we attempt to address a number of the performance-limiting issues in Tor networks design. Several researches proposed changes in the transport design to eliminate the effect of these problems and improve the performance of Tors network. In our work, we propose "QuicTor," an improvement to the transport layer of Tors network by using Googles protocol "QUIC" instead of TCP. QUIC was mainly developed to eliminate TCPs latency introduced from the handshaking delays and the head-of-line blocking problem. We provide an empirical evaluation of our proposed design and compare it to two other proposed designs, IMUX and PCTCP.We show that QuicTor significantly enhances the performance of Tors network. Tor was mainly developed as a low-latency network to support interactive web browsing and messaging applications. However, a considerable percentage of Tor traffic is consumed by bandwidth acquisitive applications such as BitTorrent. This results in an unfair allocation of the available bandwidth and significant degradation in the Quality-of-service (QoS) delivered to users. In this thesis, we present a QoS-aware deep reinforcement learning approach for Tors circuit scheduling (QDRL). We propose a design that coalesces the two scheduling levels originally presented in Tor and addresses it as a single resource allocation problem. We use the QoS requirements of different applications to set the weight of active circuits passing through a relay. Furthermore, we propose a set of approaches to achieve the optimal trade-off between system fairness and efficiency. We designed and implemented a reinforcement-learning-based scheduling approach (TRLS), a convex-optimization-based scheduling approach (CVX-OPT), and an average-rate-based proportionally fair heuristic (AR-PF). We also compared the proposed approaches with basic heuristics and with the implemented scheduler in Tor. We show that our reinforcement-learning-based approach (TRLS) achieved the highest QoS-aware fairness level with a resilient performance to the changes in an environment with a dynamic nature, such as the Tor networ

Towards Zero Touch Next Generation Network Management

The current trend in user services places an ever-growing demand for higher data rates, near-real-time latencies, and near-perfect quality of service. To meet such demands, fundamental changes were made to the front and mid-haul and backbone networking segments servicing them. One of the main changes made was virtualizing the networking components to allow for faster deployment and reconfiguration when needed. However, adopting such technologies poses several challenges, such as improving the performance and efficiency of these systems by properly orchestrating the services to the ideal edge device. A second challenge is ensuring the backbone optical networking maximizes and maintains the throughput levels under more dynamically variant conditions. A third challenge is addressing the limitation of placement techniques in O-RAN. In this thesis, we propose using various optimization modeling and machine learning techniques in three segments of network systems towards lowering the need for human intervention targeting zero-touch networking. In particular, the first part of the thesis applies optimization modeling, heuristics, and segmentation to improve the locally driven orchestration techniques, which are used to place demands on edge devices throughput to ensure efficient and resilient placement decisions. The second part of the thesis proposes using reinforcement learning (RL) techniques on a nodal base to address the dynamic nature of demands within an optical networking paradigm. The RL techniques ensure blocking rates are kept to a minimum by tailoring the agents’ behavior based on each node\u27s demand intake throughout the day. The third part of the thesis proposes using transfer learning augmented reinforcement learning to drive a network slicing-based solution in O-RAN to address the stringent and divergent demands of 5G applications. The main contributions of the thesis consist of three broad parts. The first is developing optimal and heuristic orchestration algorithms that improve demands’ performance and reliability in an edge computing environment. The second is using reinforcement learning to determine the appropriate spectral placement for demands within isolated optical paths, ensuring lower fragmentation and better throughput utilization. The third is developing a heuristic controlled transfer learning augmented reinforcement learning network slicing in an O-RAN environment. Hence, ensuring improved reliability while maintaining lower complexity than traditional placement techniques