Edge computing

About: Edge computing is a research topic. Over the lifetime, 11657 publications have been published within this topic receiving 148533 citations.

Cost-Efficient Deployment of Fog Computing Systems at Logistics Centers in Industry 4.0

Abstract: In Industry 4.0, the factories become increasingly smart and efficient through intelligent cyber-physical systems based on the deployment of Internet of Things (IoT), mobile devices, and cloud computing systems. In practice, the cloud computing system in a factory is managed in a centralized way, and hence may not afford heavy computing loads from thousands of IoT devices in the factory. An approach to address this issue is to deploy fog/edge computing resources nearby IoT devices in a distributed way to provide real-time computing responses on sites. This paper investigates deployment of an intelligent computing system consisting of a cloud center, gateways, fog devices, edge devices, and sensors attached to facilities in a logistics center. Except for locations of the cloud center and sensors that have been determined based on the factory layout, this paper establishes an integer programing model for deploying gateways, fog devices, edge devices in their respective potential sites, so that the total installation cost is minimized, under the constraints of maximal demand capacity, maximal latency time, coverage, and maximal capacity of devices. This paper further solves this NP-hard facility location problem by a metaheuristic algorithm that incorporates discrete monkey algorithm to search for good quality solutions and genetic algorithm to increase computational efficiency. Simulation verifies high performance of the proposed algorithm in deployment of intelligent computing systems in moderate-scale instances of intelligent logistics centers.

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

Abstract: The highly demanding Over-The-Top (OTT) multimedia applications pose increased challenges to Internet Service Providers (ISPs) for assuring a reasonable Quality of Experience (QoE) to their customers due to lack of flexibility, agility and scalability in traditional networks. The future networks are shifting towards the cloudification of the network resources via Software Defined Networks (SDN) and Network Function Virtualization (NFV). This will equip ISPs with cutting-edge technologies to provide service customization during service delivery and offer QoE which meets customers’ needs via intelligent QoE control and management approaches. Towards this end, we provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks. We start with a high-level description of QoE management for multimedia services, which integrates QoE modelling, monitoring, and optimization. This followed by a discussion of HTTP Adaptive Streaming (HAS) solutions as the dominant technique for streaming videos over the best-effort Internet. We then summarize the key elements in SDN/NFV along with an overview of ongoing research projects, standardization activities and use cases related to SDN, NFV, and other emerging applications. We provide a survey of the state-of-the-art of QoE management techniques categorized into three different groups: a) QoE-aware/driven strategies using SDN and/or NFV; b) QoE-aware/driven approaches for adaptive streaming over emerging architectures such as multi-access edge computing, cloud/fog computing, and information-centric networking; and c) extended QoE management approaches in new domains such as immersive augmented and virtual reality, mulsemedia and video gaming applications. Based on the review, we present a list of identified future QoE management challenges regarding emerging multimedia applications, network management and orchestration, network slicing and collaborative service management in softwarized networks. Finally, we provide a discussion on future research directions with a focus on emerging research areas in QoE management, such as QoE-oriented business models, QoE-based big data strategies, and scalability issues in QoE optimization.

Fog Following Me: Latency and Quality Balanced Task Allocation in Vehicular Fog Computing

Abstract: Emerging vehicular applications, such as real-time situational awareness and cooperative lane change, demand for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Fog Following Me (Folo), a novel solution for latency and quality balanced task allocation in vehicular fog computing. Folo is designed to support the mobility of vehicles, including ones generating tasks and the others serving as fog nodes. We formulate the process of task allocation across stationary and mobile fog nodes into a joint optimization problem, with constraints on service latency, quality loss, and fog capacity. As it is a NP-hard problem, we linearize it and solve it using Mixed Integer Linear Programming. To evaluate the effectiveness of Folo, we simulate the mobility of fog nodes at different times of day based on real-world taxi traces, and implement two representative tasks, including video streaming and real-time object recognition. Compared with naive and random fog node selection, the latency and quality balanced task allocation provided by Folo achieves higher performance. More specifically, Folo shortens the average service latency by up to 41\% while reducing the quality loss by up to 60\%.

Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges

Abstract: The fourth industrial revolution heralds a paradigm shift in how people, processes, things, data and networks communicate and connect with each other. Conventional computing infrastructures are struggling to satisfy dramatic growth in demand from a deluge of connected heterogeneous end points located at the edge of networks while, at the same time, meeting quality of service levels. The complexity of computing at the edge makes it increasingly difficult for infrastructure providers to plan for and provision resources to meet this demand. While simulation frameworks are used extensively in the modelling of cloud computing environments in order to test and validate technical solutions, they are at a nascent stage of development and adoption for fog and edge computing. This paper provides an overview of challenges posed by fog and edge computing in relation to simulation.