Cloud-Native Computing: A Survey from the Perspective of Services
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Abstract (arXiv)
The development of cloud computing delivery models inspires the emergence of cloud-native computing. Cloud-native computing, as the most influential development principle for web applications, has already attracted increasingly more attention in both industry and academia. Despite the momentum in the cloud-native industrial community, a clear research roadmap on this topic is still missing. As a contribution to this knowledge, this paper surveys key issues during the life-cycle of cloud-native applications, from the perspective of services. Specifically, we elaborate the research domains by decoupling the life-cycle of cloud-native applications into four states: building, orchestration, operate, and maintenance. We also discuss the fundamental necessities and summarize the key performance metrics that play critical roles during the development and management of cloud-native applications. We highlight the key implications and limitations of existing works in each state. The challenges, future directions, and research opportunities are also discussed.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Jun 2023.
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Citing papers per year: 2024: 1; 2025: 4; 2026: 1.
6 papers.
- 2025Agentic Services Computingcited by 6
- 2025Edge Large AI Models: Revolutionizing 6G Networkscited by 2
- 2024Hierarchical Learning and Computing over Space-Ground Integrated Networkscited by 1
- 2025Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functionscited by 0
- 2025Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directionscited by 0
- 2026STLGT: A Scalable Trace-Based Linear Graph Transformer for Tail Latency Prediction in Microservicescited by 0
- 2026STLGT: A Scalable Trace-Based Linear Graph Transformer for Tail Latency Prediction in Microservices29 Apr 2026
- 2025Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directions27 Oct 2025
- 2025Agentic Services Computing29 Sep 2025
- 2025Edge Large AI Models: Revolutionizing 6G Networks1 May 2025
- 2025Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functions30 Jan 2025
- 2024Hierarchical Learning and Computing over Space-Ground Integrated Networks26 Aug 2024