Novel Architectures and Networking Solutions for Intelligent Mobile Edge Computing Networks

University of Technology Sydney. Faculty of Engineering and Information Technology.Mobile edge computing (MEC) has emerged as a highly-effective solution to address the proliferation of smart devices and growing demands for computationally-intensive applications. The key idea of MEC networks is to distribute computing resources closer to mobile users (MUs) by deploying servers at the ``edge'' of the networks, i.e., mobile edge nodes (MENs). Nonetheless, the development of MEC networks has been facing various challenges including the decentralized nature, small coverage, unreliable computing/communication resources, and limited storage capacity of the MENs. This thesis aims to address the above challenges through developing novel collaborative architectures and intelligent networking strategies for MEC networks. Firstly, we introduce a novel MEC network architecture that leverages an optimal joint caching-delivering with horizontal cooperation among MENs. Particularly, we first formulate the content-access delay minimization problem by jointly optimizing content caching and delivering decisions under various network constraints, aiming at minimizing the total average delay for the MEC network. Then, we design centralized and distributed solutions to find the decisions of joint caching and delivering policy for the transformed problem. As the second contribution, we propose a novel economic-efficiency framework for the MEC network to maximize the profits for MENs. Specifically, we first introduce a demand prediction method for MENs leveraging federated learning (FL) approaches. Based on the predicted demands, each MEN can reserve demands from the MEC service provider (MSP) in advance to optimize its profit. Nonetheless, due to the competition among the MENs as well as unknown information from the MSP, we develop a multi-principal one-agent (MPOA) contract-based utility optimization under the MSP's constraints as well as other MENs' contracts. We then develop an iterative algorithm to find the optimal contracts for the MENs. Finally, we propose a novel dynamic FL-based framework leveraging dynamic selection of MENs for the FL process in the MEC network. Particularly, the MSP first implements an MU selection method to determine a set of the best MUs for the FL process according to the location and information significance at each learning round. Then, each selected MU can collect information and offer a payment contract to the MSP based on its collected QoI. For that, we develop an MPOA contract-based policy to maximize the profits of the MSP and learning MUs under the MSP's limited payment budget and asymmetric information between the MSP and MUs

Mitigating Interference in Content Delivery Networks by Spatial Signal Alignment: The Approach of Shot-Noise Ratio

Multimedia content especially videos is expected to dominate data traffic in next-generation mobile networks. Caching popular content at the network edge has emerged to be a solution for low-latency content delivery. Compared with the traditional wireless communication, content delivery has a key characteristic that many signals coexisting in the air carry identical popular content. They, however, can interfere with each other at a receiver if their modulation-and-coding (MAC) schemes are adapted to individual channels following the classic approach. To address this issue, we present a novel idea of content adaptive MAC (CAMAC) where adapting MAC schemes to content ensures that all signals carry identical content are encoded using an identical MAC scheme, achieving spatial MAC alignment. Consequently, interference can be harnessed as signals, to improve the reliability of wireless delivery. In the remaining part of the paper, we focus on quantifying the gain CAMAC can bring to a content-delivery network using a stochastic-geometry model. Specifically, content helpers are distributed as a Poisson point process, each of which transmits a file from a content database based on a given popularity distribution. It is discovered that the successful content-delivery probability is closely related to the distribution of the ratio of two independent shot noise processes, named a shot-noise ratio. The distribution itself is an open mathematical problem that we tackle in this work. Using stable-distribution theory and tools from stochastic geometry, the distribution function is derived in closed form. Extending the result in the context of content-delivery networks with CAMAC yields the content-delivery probability in different closed forms. In addition, the gain in the probability due to CAMAC is shown to grow with the level of skewness in the content popularity distribution.Comment: 32 pages, to appear in IEEE Trans. on Wireless Communicatio

Mobile edge computing in wireless communication networks: design and optimization

This dissertation studies the design and optimization of applying mobile edge computing (MEC) in three kinds of advanced wireless networks, which is motivated by three non-trivial but not thoroughly studied topics in the existing MEC-related literature. First, we study the application of MEC in wireless powered cooperation-assisted systems. The technology of wireless power transfer (WPT) used at the access point (AP) is capable of providing sustainable energy supply for resource-limited user equipment (UEs) to support computation offloading, but also introduces the double-near-far effect into wireless powered communication networks (WPCNs). By leveraging cooperation among near-far users, the system performance can be highly improved through effectively suppressing the double-near-far effect in WPCNs. Then, we consider the application of MEC in the unmanned aerial vehicle (UAV)-assisted relaying systems to make better use of the flexible features of UAV as well as its computing resources. The adopted UAV not only acts as an MEC server to help compute UEs' offloaded tasks but also a relay to forward UEs' offloaded tasks to the AP, thus such kind of cooperation between the UAV and the AP can take the advantages of both sides so as to improve the system performance. Last, heterogeneous cellular networks (HetNets) with the coexistence of MEC and central cloud computing (CCC) are studied to show the complementary and promotional effects between MEC and CCC. The small base stations (SBSs) empowered by edge clouds offer limited edge computing services for UEs, whereas the macro base station (MBS) provides high-performance CCC services for UEs via restricted multiple-input multiple-output (MIMO) backhauls to their associated SBSs. With further considering the case with massive MIMO backhauls, the system performance can be further improved while significantly reducing the computational complexity. In the aforementioned three advanced MEC systems, we mainly focus on minimizing the energy consumption of the systems subject to proper latency constraints, due to the fact that energy consumption and latency are regarded as two important metrics for measuring the performance of MEC-related works. Effective optimization algorithms are proposed to solve the corresponding energy minimization problems, which are further validated by numerical results

On the use of intelligent models towards meeting the challenges of the edge mesh

Nowadays, we are witnessing the advent of the Internet of Things (IoT) with numerous devices performing interactions between them or with their environment. The huge number of devices leads to huge volumes of data that demand the appropriate processing. The “legacy” approach is to rely on Cloud where increased computational resources can realize any desired processing. However, the need for supporting real-time applications requires a reduced latency in the provision of outcomes. Edge Computing (EC) comes as the “solver” of the latency problem. Various processing activities can be performed at EC nodes having direct connection with IoT devices. A number of challenges should be met before we conclude a fully automated ecosystem where nodes can cooperate or understand their status to efficiently serve applications. In this article, we perform a survey of the relevant research activities towards the vision of Edge Mesh (EM), i.e., a “cover” of intelligence upon the EC. We present the necessary hardware and discuss research outcomes in every aspect of EC/EM nodes functioning. We present technologies and theories adopted for data, tasks, and resource management while discussing how machine learning and optimization can be adopted in the domain