A Survey on Edge Computing Systems and Tools

Abstract

Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage and network resources at the edge of the network to provide computing infrastructure, enabling developers to quickly develop and deploy edge applications. Nowadays the edge computing systems have received widespread attention in both industry and academia. To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing systems and introduces representative projects. A comparison of open source tools is presented according to their applicability. Finally, we highlight energy efficiency and deep learning optimization of edge computing systems. Open issues for analyzing and designing an edge computing system are also studied in this survey.

24 pages, 21 figures, 4 tables, 87 references

References in corpus (6)

• TensorFlow: A system for large-scale machine learning
• Caffe: Convolutional Architecture for Fast Feature Embedding
• MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
• NFV: State of the Art, Challenges and Implementation in Next Generation Mobile Networks (vEPC)
• pCAMP: Performance Comparison of Machine Learning Packages on the Edges
• A Survey of FPGA Based Deep Learning Accelerators: Challenges and Opportunities

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• A Review on Computational Intelligence Techniques in Cloud and Edge Computing
• Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review
• Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey
• A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services
• Edge Federation: Towards an Integrated Service Provisioning Model
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey
• EnforceSNN: Enabling Resilient and Energy-Efficient Spiking Neural Network Inference considering Approximate DRAMs for Embedded Systems
• Democratizing the Edge: A Pervasive Edge Computing Framework
• MECKD: Deep Learning-Based Fall Detection in Multilayer Mobile Edge Computing With Knowledge Distillation
• PoPeC: PAoI-Centric Task Offloading with Priority over Unreliable Channels
• The AutoSPADA Platform: User-Friendly Edge Computing for Distributed Learning and Data Analytics in Connected Vehicles
• At the Edge of a Seamless Cloud Experience
• Stochastic Buffer-Aided Relay-Assisted MEC in Time-Slotted Systems