Performance Analysis and Optimization for Time Critical Networking

Abstract: Future communication systems will be characterized by heterogeneous traffic and requirements. Time critical applications like cyberphysical systems, augmented and virtual reality, raise the need for a low-latency based network. At the same time, conventional devices requiring for high throughput will co-exist with time critical applications. Besides the new technologies, new scheduling and optimization techniques are needed to face these challenges. In this thesis, we investigate the issues arising from the deployment of these technologies. In Paper I, we explore the benefits of dynamic Transmission Time Interval (TTI) selection in a heterogeneous network environment. We consider packets with deadlines and we optimize jointly the TTI length and the channel allocation. After proving the NP-hardness of the problem, we propose a greedy algorithm taking decisions in polynomial time. The first work opens new questions regarding the deadline-constrained traffic such as how the minimum average drop rate can be achieved. In Paper II, we consider power-limited devices with deadline-constrained traffic. Lyapunov optimization methods are explored to solve the problem with time average objective and constraints. We develop a dynamic, polynomial time, algorithm that finds an approximation of the dropping rate minimization problem under average power constraints. Besides the new techniques, future communication systems will require the development of new technologies for a more exible and elastic network. Multi-access Edge Computing (MEC) and Virtual Network Function (VNF) technologies are considered two of the key technologies for next generation networks. In Paper III, we analyze the performance of a network that hosts VNF and consists of MEC servers and servers at the core. As a first step, we consider a simple end-to-end communication system and provide analytical expressions for the end-to-end delay and system throughput by applying tools from queueing theory. Based on the first step, we provide the methodology for analyzing scaled-up systems with arbitrary number of servers. Simulation results show that our analytical model performs well. Furthermore, this work provides insights for the design and performance optimization of such systems such as optimal ow control and resource allocation.