Edge computing

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

EdgeEye: An Edge Service Framework for Real-time Intelligent Video Analytics

Abstract: Deep learning with Deep Neural Networks (DNNs) can achieve much higher accuracy on many computer vision tasks than classic machine learning algorithms. Because of the high demand for both computation and storage resources, DNNs are often deployed in the cloud. Unfortunately, executing deep learning inference in the cloud, especially for real-time video analysis, often incurs high bandwidth consumption, high latency, reliability issues, and privacy concerns. Moving the DNNs close to the data source with an edge computing paradigm is a good approach to address those problems. The lack of an open source framework with a high-level API also complicates the deployment of deep learning-enabled service at the Internet edge. This paper presents EdgeEye, an edge-computing framework for real-time intelligent video analytics applications. EdgeEye provides a high-level, task-specific API for developers so that they can focus solely on application logic. EdgeEye does so by enabling developers to transform models trained with popular deep learning frameworks to deployable components with minimal effort. It leverages the optimized inference engines from industry to achieve the optimized inference performance and efficiency.

Energy-Latency Tradeoff for Dynamic Computation Offloading in Vehicular Fog Computing

Abstract: Vehicular Fog Computing (VFC) provides solutions to relieves overload cloudlet nodes, reduces service latency during peak times, and saves energy for battery-powered cloudlet nodes by offloading user tasks to a vehicle (vehicular node) by exploiting the under-utilized computation resources of nearby vehicular node. However, the wide deployment of VFC still confronts several critical challenges: lack of energy-latency tradeoff and efficient resource allocation mechanisms. In this paper, we address the challenges and provide an Energy-efficient dynamic Computation Offloading and resources allocation Scheme ( ECOS ) to minimize energy consumption and service latency. We first formulate the ECOS problem as a joint energy and latency cost minimization problem while satisfying vehicular node mobility and end-to-end latency deadline constraints. We then propose an ECOS scheme with three phases. In the first phase, we propose an overload cloudlet node detection policy based on resource utilization. In the second phase, we propose a computational offloading selection policy to select a task from an overloaded cloudlet node for offloading, which minimizes offloading cost and the risk of overload. Next, we propose a heuristic approach to solve the resource allocation problem between the vehicular node and selected user tasks for energy-latency tradeoff. Extensive simulations have been conducted under realistic highway and synthetic scenarios to examine the ECOS scheme's performance. In comparison, our proposed scheme outperforms the existing schemes in terms of energy-saving, service latency, and joint energy-latency cost.

CoopEdge: A Decentralized Blockchain-based Platform for Cooperative Edge Computing

Abstract: Edge computing (EC) has recently emerged as a novel computing paradigm that offers users low-latency services. Suffering from constrained computing resources due to their limited physical sizes, edge servers cannot always handle all the incoming computation tasks timely when they operate independently. They often need to cooperate through peer-offloading. Deployed and managed by different stakeholders, edge servers operate in a distrusted environment. Trust and incentive are the two main issues that challenge cooperative computing between them. Another unique challenge in the EC environment is to facilitate trust and incentive in a decentralized manner. To tackle these challenges systematically, this paper proposes CoopEdge, a novel blockchain-based decentralized platform, to drive and support cooperative edge computing. On CoopEdge, an edge server can publish a computation task for other edge servers to contend for. A winner is selected from candidate edge servers based on their reputations. After that, a consensus is reached among edge servers to record the performance in task execution on blockchain. We implement CoopEdge based on Hyperledger Sawtooth and evaluate it experimentally against a baseline and two state-of-the-art implementations in a simulated EC environment. The results validate the usefulness of CoopEdge and demonstrate its performance.

Resource Allocation for Vehicular Fog Computing Using Reinforcement Learning Combined With Heuristic Information

Abstract: Internet of Vehicles (IoV) has emerged as a key component of smart cities. Connected vehicles are increasingly processing real-time data to respond immediately to user requests. However, the data must be sent to a remote cloud for processing. To resolve this issue, vehicular fog computing (VFC) has emerged as a promising paradigm that improves the quality of computation experiences for vehicles by offloading computation tasks from the cloud to network edges. Nevertheless, due to the resource restrictions of fog computing, only a limited number of vehicles are able to use it while it is still challenging to provide real-time responses for vehicular applications, such as traffic and accident warnings in the highly dynamic IoV environment. Therefore, in this article, we formulate the problem of allocating the limited fog resources to vehicular applications such that the service latency is minimized, by utilizing parked vehicles. We then propose a heuristic algorithm to efficiently find the solutions of the problem formulation. In addition, the proposed algorithm is combined with reinforcement learning to make more efficient resource allocation decisions, leveraging the vehicles’ movement and parking status collected from the smart environment of the city. Our simulation results show that our VFC resource allocation algorithm can achieve higher service satisfaction compared to conventional resource allocation algorithms.