Intelligent Intent-based Network Slicing for IoT Systems: A Framework for Traffic Modeling, Autonomous Resource Management, and Privacy-Preserving Orchestration

Abstract

The emergence of the Internet of Things (IoT) has introduced a multitude of services into our daily routines, encompassing smart cities, eHealth, and smart homes. These services exhibit varying Quality of Service (QoS) and functional requirements, posing distinctive challenges for contemporary Management and Orchestration (MO) systems. Envisioned MO systems are characterized by intelligence, abstract user interactions, and autonomous adaptability with cost optimization. This thesis aims to advance the development of efficient MO systems tailored for IoT networks. At the core of this research is the introduction of a pioneering mathematical framework, the Tiered Markov Modulated Stochastic Process (TMMSP), that comprehends application-specific characteristics of IoT traffic and enables the generation of realistic IoT traffic data. The TMMSP framework serves as a linchpin in the research, enabling comprehensive simulations and evaluations of network performance. Furthermore, the thesis presents an Intelligent and Autonomous Edge Slicing (IAES) system, a novel approach designed to recognize diverse IoT environments and implement per-slice resource allocation policies. The IAES enables intelligent automation within edge systems by policy optimization that enhances resource efficiency while accommodating the diverse needs of IoT services. The IAES leverages the TMMSP framework for generating IoT data for the IAES system’s evaluation. Lastly, the thesis proposes the Harmony Slice Master (H-SliceMaster), a privacy-preserving Intelligent Intent-Based Network Slicing (I-IBNS) framework that enables end-to-end MO while preserving data privacy and control autonomy in multi-administrator and multi-tenant environments. The H-SliceMaster comprises several integral components: the knowledge management framework, intent propagation framework, Promise and Price Network Operation (PPNO) principle, and Soft Network Control (SNC) approach. These components provide a foundation for dynamic MO of end-to-end IoT system, meeting services' unique quality and functional demands in a cost-effective manner while preserving the privacy of system domains