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

Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021

As almost all human activities have been moved online due to the pandemic, novel robust and efficient approaches and further research have been in higher demand in the field of computer science and telecommunication. Therefore, this (reprint) book contains 13 high-quality papers presenting advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity

Automatic discovery of complex causality

This study entails the understanding of and the development of a computational method for automatically extracting complex expressions in language that correspond to event to event sequential relations in the real world. We here develop component procedures of a system that would be capable of taking raw linguistic input (such as those from narrative writings or social network data), and find real-world semantic relations among events. Such an endeavor is applicable to many types of sequential relations, for which we use causality as a case study, both for its importance as a prominent type of sequential relation between events, as well as for its general prevalence in natural language. But we also demonstrate that the idea is also applicable in principle to other major types of event to event relations, such as reciprocity. The study primarily focuses on those types of causalities that contain complex structures and require in-depth linguistic analyses to discover and extract. Designing an automated method for the extraction of structurally complex causal expressions entails methodologies and theories that are beyond conventional methods used in computational semantics. The classes of adjunctive causal structure, and embedded causal structure are types that are hard to access using traditional methods, but more amenable for methods developed in this study. The principal procedures employed for the extraction of these are a heavily mod- ified form of Hidden Markov Model (HMM), which we use to deal with causal structures that have sequentially complex makeup. We also designed a highly modified Genetic Algo- rithm (GA) adapted for embedded context-free structures, used to rank and extract those causal structures that have deep embedding at the syntax-semantics interface. These will be reformulated, augmented, and explored in depth. With these methods using unsupervised and semi-supervised learning, we were able to obtain reasonable results in terms of discrimination of causal pairs ⟨ei,ej⟩ pairs and some longer chains of causation from corpora. From these results, we were also able to perform additional linguistic analysis over their theoretical semantic structure, and observe aspects of each that allows us to sub-classify the relations according to standard ideas in formal logic as well as from behavioral psychology. These methods would be critical to a system for building a graph theoretic representation of a social network, from corpora produced by entities within that network, which would utilize the methods described in this project, and similar approaches can be extended to model and discover other types of complex event- relations. These types of fundamental technologies, would in turn, help us to design and build the types of on-line and mobile services that provide increased machine awareness of user behavior and to be able to target and cater to users individually