Edge computing

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

Scalability and Performance Evaluation of Edge Cloud Systems for Latency Constrained Applications

Abstract: This paper presents an analysis of the scalability and performance of an edge cloud system designed to support latency-sensitive applications. A system model for geographically dispersed edge clouds is developed by considering an urban area such as Chicago and co-locating edge computing clusters with known Wi-Fi access point locations. The model also allows for provisioning of network bandwidth and processing resources with specified parameters in both edge and the cloud. The model can then be used to determine application response time (sum of network delay, compute queuing and compute processing time), as a function of offered load for different values of edge and core compute resources, and network bandwidth parameters. Numerical results are given for the city-scale scenario under consideration to show key system level trade-offs between edge cloud and conventional cloud computing. Alternative strategies for routing service requests to edge vs. core cloud clusters are discussed and evaluated. Key conclusions from the study are: (a) the core cloud-only system outperforms the edge-only system having low inter-edge bandwidth, (b) a distributed edge cloud selection scheme can approach the global optimal assignment when the edge has sufficient compute resources and high inter-edge bandwidth, and (c) adding capacity to an existing edge network without increasing the inter-edge bandwidth contributes to network wide congestion and can reduce system capacity.

Knowledge-Centric Edge Computing Based on Virtualized D2D Communication Systems

Abstract: MEC has been gaining great interest in addressing the limited computational capability in 5G mobile networks. Although there are several existing works about MEC, for example, utilizing WNV and SDN, virtualized D2D communication together with learning-based D2D communication systems, such as social and smart D2D networking systems, have not been investigated by researchers, while knowledge of the edge is lacking. In this article, we introduce an innovative framework, called KCE, to dynamically detect network structure and manage communication resources, leveraging the insightful knowledge obtained from D2D communications among mobile users. Further, to support trustworthy network access and to meet different QoS requirements in the proposed architecture, specific SPs are selected from InPs by a KCE server. As such, the chosen SPs' resources can be dynamically allocated to meet various service requirements by virtualized D2D communication techniques. Simulation results demonstrate the effectiveness of the proposed framework under different scenarios and parameter settings.

Blockchain framework for IoT data quality via edge computing

Abstract: Smart home presents a challenge in control and monitoring of its wireless sensors networks (WSN) and the internet of things (IoT) devices which form it. The current IoT architectures are centralized, complex, with poor security in its communications and with upstream communication channels mainly. As a result, there are problems with data reliability. These problems include data missing, malicious data inserted, communications network overload, and overload of computing power at the central node. In this paper a new architecture is presented. This architecture based in blockchain introduce the edge computing layer and a new algorithm to improve data quality and false data detection.

Modeling and Simulation of Fog and Edge Computing Environments Using iFogSim Toolkit

Abstract: This chapter focuses on delivering a tutorial on iFogSim. iFogSim simulation toolkit is developed upon the fundamental framework of CloudSim. CloudSim is one of the wildly adopted simulators to model cloud computing environments. The chapter briefly discusses the iFogSim simulator and its three basic components: physical components, logical components, and management components. It explores high‐level steps to model and simulate fog computing environment. The chapter revisits the way of installing iFogSim and provides a guideline to model fog environment. Some fog scenarios and their corresponding user extensions are included in the chapter. Finally, the chapter concludes with the simulation of a simple application placement policy and a case study in smart healthcare. The case study includes a schematic illustration of the system architecture and application model for the Internet‐of‐Things (IoT)‐enabled healthcare solutions.