Abstract
The deployment of fifth-generation (5G) and 802.11-based networks have enabled a new class of smart applications, such as extended reality and real-time situational awareness. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to IoT devices. To address the requirements of these applications, several edge computing systems, such as cloudlet computing, mobile edge computing, and fog computing, have been proposed. The deployment of edge computing systems requires the addition of new infrastructure or the extension of existing infrastructure. Edge computing systems also do not utilize the capabilities of end devices, such as smartphones, mobile robots, and smart vehicles, which are equipped with multi-core central and graphical processing units, several sensors, or multiple wireless communication technologies.
In contrast to publicly available edge computing solutions, private or local edge cloud systems have recently been suggested to further reduce latency, security and privacy risks, and improve bandwidth and utilization of high-end devices. A private edge cloud system is a small-scale cloud data center in a local physical area, such as a house or an office. It consists of various stationary and mobile devices, such as personal computers, mobile robots, smartphones, and sensors, interconnected through single or multiple local area networks.
Nevertheless, to efficiently manage and utilize local edge cloud system infrastructures, intelligent architectures and platforms are required that differ from corresponding solutions for public edge cloud systems. In this tutorial, we will discuss the design of intelligent multi-network protocols, resource management algorithms, and platforms leveraging for instance approaches based on machine learning, software-defined network, or container technologies to efficiently manage heterogeneous compute and network resources in a private edge environment, and provide task processing, data collection, and data storage services to support emerging resource-intensive and non-resource intensive smart applications.
In contrast to publicly available edge computing solutions, private or local edge cloud systems have recently been suggested to further reduce latency, security and privacy risks, and improve bandwidth and utilization of high-end devices. A private edge cloud system is a small-scale cloud data center in a local physical area, such as a house or an office. It consists of various stationary and mobile devices, such as personal computers, mobile robots, smartphones, and sensors, interconnected through single or multiple local area networks.
Nevertheless, to efficiently manage and utilize local edge cloud system infrastructures, intelligent architectures and platforms are required that differ from corresponding solutions for public edge cloud systems. In this tutorial, we will discuss the design of intelligent multi-network protocols, resource management algorithms, and platforms leveraging for instance approaches based on machine learning, software-defined network, or container technologies to efficiently manage heterogeneous compute and network resources in a private edge environment, and provide task processing, data collection, and data storage services to support emerging resource-intensive and non-resource intensive smart applications.
| Original language | English |
|---|---|
| Title of host publication | IEEE Future Networks World Forum 2024 |
| Publication status | Published (VoR) - 17 Oct 2024 |