EdgeFlow: Open-Source Multi-layer Data Flow Processing in Edge Computing for 5G and Beyond
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TL;DR: In this article, a multi-layer edge computing framework called EdgeFlow is proposed, where different nodes ranging from edge devices to cloud data centers are categorized into corresponding layers and cooperate for data processing.
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Abstract: Edge computing has evolved to be a promising avenue to enhance system computing capability by offloading processing tasks from the cloud to edge devices. In this article, we propose a multi-layer edge computing framework called EdgeFlow. In this framework, different nodes ranging from edge devices to cloud data centers are categorized into corresponding layers and cooperate for data processing. EdgeFlow can deal with the trade-off between the computing and communication capabilities so that the tasks can be assigned to each layer optimally. At the same time, resources are carefully allocated throughout the whole network to mitigate performance fluctuation. The proposed open-source data flow processing framework is implemented on a platform that can emulate various computing nodes in multiple layers and corresponding network connections. Evaluated on the face recognition scenario, EdgeFlow can significantly reduce task finish time and perform more tolerance to run-time variations, compared with pure cloud computing, pure edge computing, and Cloudlet. Potential applications of EdgeFlow, including network function virtualization, Internet of Things, and vehicular networks, are also discussed at the end of this article.
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Citations
Joint Task Assignment, Transmission, and Computing Resource Allocation in Multilayer Mobile Edge Computing Systems
TL;DR: The EdgeFlow system is implemented on the universal software radio peripheral and the Intel next units of computing and indicates that the EdgeFlow can achieve a low latency and recovery time than the previous distributed frameworks, e.g., the Cloudlet and the Markov decision process.
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Task Scheduling for Mobile Edge Computing Using Genetic Algorithm and Conflict Graphs
TL;DR: Results demonstrate that the sequential offloading yields a reduced offloading failure probability when compared to the parallel offloading, and as the dependency among sub-tasks increases, the latency gap between parallel and sequential schemes decreases.
Cooperative Computation Offloading for Multi-Access Edge Computing in 6G Mobile Networks via Soft Actor Critic
TL;DR: To support low-latency services, this paper model the cooperative computation offloading problem as a Markov decision process, and proposes two intelligent computation off loading algorithms based on Soft Actor Critic (SAC), i.e., centralized SAC offloading and decentralized Sac offloading.
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A survey on vehicular task offloading: Classification, issues, and challenges
TL;DR: In this article , the authors present a comprehensive survey on the vehicular task offloading techniques under a communication perspective, i.e., vehicle to vehicle, vehicle to roadside infrastructure (V2I), and vehicle to everything.
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A Multi-user Cost-efficient Crowd-assisted VR Content Delivery Solution in 5G-and-beyond Heterogeneous Networks
TL;DR: In this article , the authors proposed an innovative multi-user cost-efficient crowd-assisted delivery and computing (MEC-DC) framework, which leverages mobile edge computing and end-user resources to support high performance VR content delivery over 5G and beyond heterogeneous networks (5G-HetNets).
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