mlsanityData to 13 Oct 2026

Resource Scheduling in Edge Computing: A Survey

arXiv 2108.08059 ↗First posted 18 Aug 2021cs.NI eess.SPcited by 17

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Abstract (arXiv)

With the proliferation of the Internet of Things (IoT) and the wide penetration of wireless networks, the surging demand for data communications and computing calls for the emerging edge computing paradigm. By moving the services and functions located in the cloud to the proximity of users, edge computing can provide powerful communication, storage, networking, and communication capacity. The resource scheduling in edge computing, which is the key to the success of edge computing systems, has attracted increasing research interests. In this paper, we survey the state-of-the-art research findings to know the research progress in this field. Specifically, we present the architecture of edge computing, under which different collaborative manners for resource scheduling are discussed. Particularly, we introduce a unified model before summarizing the current works on resource scheduling from three research issues, including computation offloading, resource allocation, and resource provisioning. Based on two modes of operation, i.e., centralized and distributed modes, different techniques for resource scheduling are discussed and compared. Also, we summarize the main performance indicators based on the surveyed literature. To shed light on the significance of resource scheduling in real-world scenarios, we discuss several typical application scenarios involved in the research of resource scheduling in edge computing. Finally, we highlight some open research challenges yet to be addressed and outline several open issues as the future research direction.

Key facts. Cited by 17 papers in the map (data through 13 Oct 2026). First posted on arXiv 18 Aug 2021.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 5; 2024: 6; 2025: 2; 2026: 1.

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  1. 2024Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computingcited by 15
  2. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  3. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 13
  4. 2024SplitLLM: Collaborative Inference of LLMs for Model Placement and Throughput Optimizationcited by 11
  5. 2023A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communicationcited by 8
  6. 2023Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiencycited by 8
  7. 2023Industrial Internet of Things Intelligence Empowering Smart Manufacturing: A Literature Reviewcited by 8
  8. 2025Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Reviewcited by 4
  9. 2023Human-Centric Resource Allocation for the Metaverse With Multiaccess Edge Computingcited by 3
  10. 2024Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systemscited by 2
  11. 2021Learning to Schedule in Parallel-Server Queues with Stochastic Bilinear Rewardscited by 0
  12. 2022Lightweight Strategy for XOR PUFs as Security Primitives for Resource-constrained IoT devicecited by 0
5 more
  1. 2023Deep Reinforcement Learning Based Resource Allocation for Cloud Native Wireless Networkcited by 0
  2. 2024A Narrative Review of Identity, Data, and Location Privacy Techniques in Edge Computing and Mobile Crowdsourcingcited by 0
  3. 2024Designing Short-Stage CDC-XPUFs: Balancing Reliability, Cost, and Security in IoT Devicescited by 0
  4. 2024CloudEye: A New Paradigm of Video Analysis System for Mobile Visual Scenarioscited by 0
  5. 20263D-Learning: Diffusion-Augmented Distributionally Robust Decision-Focused Learningcited by 0