Edge computing

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

Pruning deep convolutional neural networks for efficient edge computing in condition assessment of infrastructures

Abstract: Health monitoring of civil infrastructures is a key application of Internet of things (IoT), while edge computing is an important component of IoT. In this context, swarms of autonomous in...

Firework: Data Processing and Sharing for Hybrid Cloud-Edge Analytics

Abstract: Now we are entering the era of the Internet of Everything (IoE) and billions of sensors and actuators are connected to the network. As one of the most sophisticated IoE applications, real-time video analytics is promising to significantly improve public safety, business intelligence, and healthcare & life science, among others. However, cloud-centric video analytics requires that all video data must be preloaded to a centralized cluster or the cloud, which suffers from high response latency and high cost of data transmission, given the scale of zettabytes of video data generated by IoE devices. Moreover, video data is rarely shared among multiple stakeholders due to various concerns, which restricts the practical deployment of video analytics that takes advantages of many data sources to make smart decisions. Furthermore, there is no efficient programming interface for developers and users to easily program and deploy IoE applications across geographically distributed computation resources. In this paper, we present a new computing framework, Firework , which facilitates distributed data processing and sharing for IoE applications via a virtual shared data view and service composition. We designed an easy-to-use programming interface for Firework to allow developers to program on Firework . This paper describes the system design, implementation, and programming interface of Firework . The experimental results of a video analytics application demonstrate that Firework reduces up to 19.52 percent of response latency and at least 72.77 percent of network bandwidth cost, compared to a cloud-centric solution.

Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments

Abstract: In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, e.g., cell-phone towers, transmission delays between edge servers and edge clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, in such a way that their tasks are completed with minimum energy consumption and minimum processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay.

MyiFogSim: A Simulator for Virtual Machine Migration in Fog Computing

Abstract: Low latency in IT applications is an important aspect of improving the quality of the user's experience. Frequently, applications are run in a virtual machine in the cloud. Because cloud providers are datacentre facilities that are often distant from users, unacceptably high latencies are experienced in some applications. Fog computing can be seen as a cloud computing extension, namely cloudlets, located in access points at the edge of the network and hence able to provide lower latencies than the cloud. However, as mobile devices and applications become more popular, users' computing and data capacities should be maintained close to the user to keep latencies as low as possible. This paper discusses resource allocation in fog computing in the face of users' mobility and introduces MyiFogSim, an extension of iFogSim to support mobility through migration of virtual machines between cloudlets. Moreover, a migration policy is proposed, and MyiFogSim is used to analyze the policy impact on application quality of service. Results suggest that the policy can promote lower latencies when compared to a scenario without the migration policy.

Mobility Support for Fog Computing: An SDN Approach

Abstract: The emerging real-time and computation-intensive services driven by the Internet of Things, augmented reality, automatic driving, and so on, have tight quality of service and quality of experience requirements, which can hardly be supported by conventional cloud computing. Fog computing, which migrates the features of cloud computing to the network edge, guarantees low latency for location-aware services. However, due to the locality feature of fog computing, maintaining service continuity when mobile users travel across different access networks has become a challenging issue. In this article, we propose a novel software-defined-networking-based fog computing architecture by decoupling mobility control and data forwarding. Under the proposed architecture, we design efficient signaling operations to provide seamless and transparent mobility support to mobile users, and present an efficient route optimization algorithm by considering the performance gain in data communications and system overhead in mobile fog computing. Numerical results from extensive simulations have demonstrated that the proposed scheme can not only guarantee service continuity, but also greatly improve handover performance and achieve high data communication efficiency in mobile fog computing.