Recent advancements enable fine-grained energy measurements in cloud-native environments (e.g., at container or process level) beyond traditional coarse-grained scopes. However, service-level energy measurement for microservice-based applications remains underexplored. Such measurements must include compute, network, and storage energy to avoid underestimating consumption in distributed setups. We present GOXN (Green Observability eXperiment eNginE), an energy experimentation engine for Kubernetes-based microservices that quantifies compute, network, and storage energy at the service level. Using GOXN, we evaluated the OpenTelemetry Demo under varying configurations (monitoring, tracing, service mesh) and steady synthetic load, collecting metrics from Kepler and cAdvisor. Our additive energy model derives service-level energy from container-level data. Results show that excluding network and storage can underestimate auxiliary-service energy by up to 63%, and that high tracing loads shift energy dominance toward network and storage.
CloudNativeSim: a toolkit for modeling and simulation of cloud-native applications
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Southern University of Science and Technology（南方科技大学）；State Key Lab of IOTSC, Department of Computer Science, University of Macau（澳门大学计算机科学与信息技术学院物联网与智慧城市重点实验室）
Auto-scaling Approaches for Microservice Applications: A Survey and Taxonomy
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Tianjin University（天津大学）；Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory, School of Computing and Information Systems, the University of Melbourne（墨尔本大学计算与信息系量子云计算与分布式系统（qCLOUDS）实验室）；State Key Lab of IOTSC, University of Macau（澳门大学物联网智慧应用国家重点实验室）
A Cloud-Native Architecture for Human-in-Control LLM-Assisted OpenSearch in Investigative Settings
AI Research Unit of the State Police of Schleswig-Holstein（石勒苏益格-荷尔斯泰因州警察AI研究部）；Faculty of Computer Science and Electrical Engineering Kiel University of Applied Sciences（基尔应用科技大学计算机科学与电气工程学院）
A Qualitative Evaluation of Service Mesh-based Traffic Management for Mobile Edge Cloud
Service mesh正被广泛采用为基于微服务的应用程序中的流量管理的云原生机制，特别是对于托管在更集中的云环境中的通用IT工作负载。性能要求高的应用程序继续推动现代应用程序执行环境的去中心化，例如移动边缘云。本文对最先进的服务网格进行了系统的定性分析，以评估其设计是否适合解决移动边缘云环境中托管的性能要求苛刻的应用程序工作负载的流量管理需求。通过这种分析，我们认为，当今以可靠性为中心的服务网格设计无法满足不同类型的新兴移动边缘云工作负载的需求，并激励进一步研究高效的架构，更强的QoS保证和更高复杂性的云原生流量管理框架的抽象。
Cloud-native and Distributed Systems for Efficient and Scalable Large Language Models -- A Research Agenda
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Hyderabad（海德拉巴大学）；Western Sydney University（西悉尼大学）；Newcastle University（纽卡斯尔大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Electronic Science and Technology of China（电子科技大学）；University of Macau（澳门大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Carleton University（卡尔顿大学）；University of Ljubljana（卢布尔雅那大学）；University of Melbourne（墨尔本大学）
Rethinking AI Cloud Infrastructure for Agentic Serving Systems with the Aries Experimentation Framework
NTU Singapore（南洋理工大学）；AWS（亚马逊网络服务）；Microsoft（微软）；AMD（超威半导体）；Ant Group（蚂蚁集团）；NCSpeech（新加坡国立大学语音实验室）；Imperial College London（帝国理工学院）；University of Edinburgh（爱丁堡大学）
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning
Columbia University（哥伦比亚大学）；University of California, Berkeley（加州大学伯克利分校）；Shenzhen Inst. of Advanced Tech.（深圳先进技术研究院）；Northwestern University（西北大学）；Bell labs（贝尔实验室）；Amazon（亚马逊）；IDEA Research（IDEA研究院）
Reliable Microservice Tail Latency Prediction via Decoupled Dual-Stream Learning and Gradient Modulation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；Polytechnic Institute, Zhejiang University（浙江大学 polytechnic 院）；School of Computer Science, University of Sydney（悉尼大学计算机科学学院）
