Resource Management in Fog/Edge Computing: A Survey
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Abstract (arXiv)
Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013-2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources in fog/edge computing.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Sep 2018.
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Citing papers per year: 2021: 4; 2022: 3; 2023: 1; 2024: 2; 2025: 2; 2026: 1.
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- 2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestrationcited by 12
- 2021PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computingcited by 8
- 2025A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networkscited by 3
- 2021Management of Resource at the Network Edge for Federated Learningcited by 2
- 2021AI Back-End as a Service for Learning Switching of Mobile Apps between the Fog and the Cloudcited by 2
- 2023WW-FL: Secure and Private Large-Scale Federated Learningcited by 2
- 2025Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuumcited by 1
- 2021ScissionLite: Accelerating Distributed Deep Neural Networks Using Transfer Layercited by 0
- 2022Introducing Federated Learning into Internet of Things ecosystems -- preliminary considerationscited by 0
- 2022Lightweight Strategy for XOR PUFs as Security Primitives for Resource-constrained IoT devicecited by 0
- 2024Distributed genetic algorithm for application placement in the compute continuum leveraging infrastructure nodes for optimizationcited by 0
- 2024Designing Short-Stage CDC-XPUFs: Balancing Reliability, Cost, and Security in IoT Devicescited by 0
1 more
- 2026Self-Evolving Multi-Agent Network for Industrial IoT Predictive Maintenance17 Feb 2026
- 2025A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks26 Aug 2025
- 2025Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum27 Jan 2025
- 2024Designing Short-Stage CDC-XPUFs: Balancing Reliability, Cost, and Security in IoT Devices26 Sep 2024
- 2024Distributed genetic algorithm for application placement in the compute continuum leveraging infrastructure nodes for optimization13 Jun 2024
- 2023WW-FL: Secure and Private Large-Scale Federated Learning20 Feb 2023
- 2022Lightweight Strategy for XOR PUFs as Security Primitives for Resource-constrained IoT device4 Oct 2022
- 2022Introducing Federated Learning into Internet of Things ecosystems -- preliminary considerations15 Jul 2022
- 2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration3 May 2022
- 2021PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computing4 Dec 2021
- 2021AI Back-End as a Service for Learning Switching of Mobile Apps between the Fog and the Cloud2 Oct 2021
- 2021Management of Resource at the Network Edge for Federated Learning7 Jul 2021
1 more
- 2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestrationcited by 12
- 2021PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computingcited by 8
- 2025A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networkscited by 3
- 2021Management of Resource at the Network Edge for Federated Learningcited by 2
- 2021AI Back-End as a Service for Learning Switching of Mobile Apps between the Fog and the Cloudcited by 2
- 2023WW-FL: Secure and Private Large-Scale Federated Learningcited by 2
- 2025Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuumcited by 1
- 2021ScissionLite: Accelerating Distributed Deep Neural Networks Using Transfer Layercited by 0
- 2022Introducing Federated Learning into Internet of Things ecosystems -- preliminary considerationscited by 0
- 2022Lightweight Strategy for XOR PUFs as Security Primitives for Resource-constrained IoT devicecited by 0
- 2024Distributed genetic algorithm for application placement in the compute continuum leveraging infrastructure nodes for optimizationcited by 0
- 2024Designing Short-Stage CDC-XPUFs: Balancing Reliability, Cost, and Security in IoT Devicescited by 0
- 2025A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks26 Aug 2025
- 2025Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum27 Jan 2025
- 2024Designing Short-Stage CDC-XPUFs: Balancing Reliability, Cost, and Security in IoT Devices26 Sep 2024
- 2024Distributed genetic algorithm for application placement in the compute continuum leveraging infrastructure nodes for optimization13 Jun 2024
- 2023WW-FL: Secure and Private Large-Scale Federated Learning20 Feb 2023
- 2022Lightweight Strategy for XOR PUFs as Security Primitives for Resource-constrained IoT device4 Oct 2022
- 2022Introducing Federated Learning into Internet of Things ecosystems -- preliminary considerations15 Jul 2022
- 2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration3 May 2022
- 2021PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computing4 Dec 2021
- 2021AI Back-End as a Service for Learning Switching of Mobile Apps between the Fog and the Cloud2 Oct 2021
- 2021Management of Resource at the Network Edge for Federated Learning7 Jul 2021
- 2021ScissionLite: Accelerating Distributed Deep Neural Networks Using Transfer Layer5 May 2021