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A Survey on Observability of Distributed Edge & Container-Based Microservices
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Edge computing is proposed as a technical enabler for meeting emerging network technologies (such as 5G and Industrial Internet of Things), stringent application requirements and key performance indicators (KPIs). It aims to alleviate the problems associated with centralized cloud computing systems by placing computational resources to the network’s edge, closer to the users. However, the complexity of distributed edge infrastructures grows when hosting containerized workloads as microservices, resulting in hard to detect and troubleshoot outages on critical use cases such as industrial automation processes. Observability aims to support operators in managing and operating complex distributed infrastructures and microservices architectures by instrumenting end-to-end runtime performance. To the best of our knowledge, no survey article has been recently proposed for distributed edge and containerized microservices observability. Thus, this article surveys and classifies state-of-the-art solutions from various communities. Besides surveying state-of-the-art, this article also discusses the observability concept, requirements, and design considerations. Finally, we discuss open research issues as well as future research directions that will inspire additional research in this area.
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“However, these scalability and resource efficiency benefits obtained through adoption of the MSA come at a cost of increased operational complexity in terms of monitoring and observability [4].”
From Toward Context-Aware Anomaly Detection for AIOps in Microservices Using Dynamic Knowledge Graphs (2026) · cites this paper as [4]
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Tonge, A. S., Baniya, B. K., & Deepak, G. C. (2025) · Network · doi:10.3390/network5030036 · Cited by 2
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The Capability of Observing Performance in Healthcare Systems
Ramu, V. B., & Yeruva, A. R. (2023) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-031-23683-9_39 · Cited by 2
Information Leakages of Docker Containers: Characterization and Mitigation Strategies
Zuppelli, M., Repetto, M., Caviglione, L., & Cambiaso, E. (2023) · IEEE International Conference on Network Softwarization (NetSoft) · doi:10.1109/netsoft57336.2023.10175435 · Cited by 2
“, the ability to perform advanced monitoring to gain detailed insights on the state of a system [11].”
BackgroundCalvio, A., Sabbioni, A., Bujari, A., & Foschini, L. (2023) · International Conference on Information Technology for Social Good (GoodIT) · doi:10.1145/3582515.3609568 · Cited by 1
“The presence of different services exposes multiple points of attack, and vulnerabilities in one service can compromise the security of the entire system [26].”
BackgroundUsing Observability to Detect Anti-patterns in Benchmarking Application with OpenTelemetry
Gomes, F. A. A., Rocha, L. S., Rêgo, P. A. L., & Trinta, F. A. M. (2024) · IEEE International Conference on Cloud Networking · doi:10.1109/cloudnet62863.2024.10815858 · Cited by 1
Effortless Lifecycle Management for Experimental IIoT Workloads in Containerized Environments
Usman, M. (2025) · Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) · doi:10.1109/icin64016.2025.10942663 · Cited by 1
“Similarly, containerization technologies like Kubernetes have revolutionized the deployment of real-world applications by facilitating the transition to microservices architectures [3].”
Self-citationCollaborative and Observability Enhanced Fault Injection for Resilient Cloud-Edge Computing
Ruan, J., Yang, X., & Fan, Y. (2025) · International Journal of Software Engineering and Knowledge Engineering · doi:10.1142/s0218194025410050 · Cited by 1
An Architecture for Vehicle Diagnostics in Software-Defined Vehicles
Bickelhaupt, S., Hahn, M. G., Weyrich, M., & Morozov, A. S. (2025) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2025-01-0280 · Cited by 1
Explainable Performance Analysis of Open-Source 5G Core Network Implementations Via Observability
Menin, C. E., Silva, I. M., Alves, V. B., Lando, G., Both, C. B., Nogueira, J. M. S., & Wickboldt, J. A. (2025) · IEEE International Conference on Network Softwarization (NetSoft) · doi:10.1109/netsoft64993.2025.11080540 · Cited by 1
“Observability is defined as “ the ability to measure the internal state of a system only by its external outputs ” [17].”
CLO5ER: a Composable Lightweight Observability for 5g RAN Environment Inside Near RT RIC
Montebugnoli, S., Sabbioni, A., Callegati, F., Foschini, L., & Bellavista, P. (2025) · International Conference on Communications · doi:10.1109/icc52391.2025.11162022 · Cited by 1
“Data sources of observability entail the collection of logs metrics and traces, each contributing to understanding and managing system performance [11].”
InfluentialMemory Anomaly Detection and Analysis Based on eBPF and Isolation Forest
Ma, Y., Shu, X., & Xie, R. (2025) · Chinese Control Conference (CCC) · doi:10.23919/ccc64809.2025.11179698 · Cited by 1
“Usman et al. suggest that combining eBPF with a service grid structure can provide multi-tier, end-to-end observability and behavioural anomaly detection capabilities, especially for distributed container environments [13] .”
Artificial Intelligence based Orchestration of Containerized Microservices for Autonomous Vehicles
Bashir, A., Mustafa, E., & Ali, M. (2025) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit67061.2025.11333601 · Cited by 1
“The dynamic decision of where and when to execute each microservice directly impacts latency, deadline adherence, and service continuity [8].”
RootScan: Unveiling Microservice Anomalies through Fine-Grained, Interpretable Root Cause Analysis
Li, C., Ye, C., & Zhou, H. (2025) · IEEE Symposium on Reliable Distributed Systems (SRDS) · doi:10.1109/srds69199.2025.00024 · Cited by 1
A Multicloud Observability Support Based on ElasticSearch for Cloud-native Smart Cities Services
Montebugnoli, S., & Foschini, L. (2023) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc58397.2023.10218075 · Cited by 1
MPDP: A Probabilistic Architecture for Microservice Performance Diagnosis and Prediction
Noor, T. H. (2024) · Computer Systems Science and Engineering · doi:10.32604/csse.2024.052510 · Cited by 1
Analysis of Revolutionizing IoT Solutions through Cloud Computing Integration
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Fog-Based IoP Application Monitoring and Failure Detection
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COMPREHENSIVE APPROACH TO PROTECTING DATA AND THE INFORMATION SYSTEM INTEGRITY
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Evaluating the Performance of Sidecar-based and Sidecarless Cloud-native Service Mesh Solutions
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“Additionally, the initial development overhead for microservices can delay time-to-market [16], and monitoring and observability become increasingly difficult as the number of services grows [17].”
A review of observability issues in hospital information system
Song, H., Ji, H., Yu, Y., & Xie, B. (2022) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc55942.2022.10004053 · Cited by 1
“[1] 2022 Observability and Microservice A survey on observability and microservices /”
BackgroundLi, B., Shi, Z., Deng, X., Wang, X., & Yang, Z. (2023) · International Conference on Electromechanical Control Technology and Transportation (ICECTT) · doi:10.1117/12.2689846
“Driven by the integration of both [9-10], microservice data from different services can be offloaded to multiple containers for parallel processing, thus improving resource utilization efficiency and reducing service data processing latency.”
BackgroundArchitectural Patterns for NFRs in Cloud Microservices
Kumari, A., Rao, K. B., & Kumar, S. M. (2023) · IEEE International Conference on Contemporary Computing and Communications (InC4) · doi:10.1109/inc457730.2023.10263213
Shao, P., Huang, L., Weng, L., & Liu, Z. (2024) · Processes · doi:10.3390/pr12061270
“Optimizing data storage access is a key step in improving the backend data access capability of trading systems [24].”
BackgroundHeterogeneous Data Processing in All-Digital Phased Array Radar
Garner, S., & Yeary, M. B. (2024) · IEEE International Symposium on Phased Array Systems and Technology (PAST) · doi:10.1109/array58370.2024.10880442
“Additional details for these are given by [16]–[19], etc., and especially for radar applications [20].”
Decimavilla-Alarcón, D. C., & Marcillo-Franco, P. F. (2025) · Revista Científica Episteme & Praxis · doi:10.62451/rep.v3i1.73
dos Reis, M. V. D., Moreira, R., Moreira, L. F. R., & Silva, F. D. O. (2025) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-87772-8_8
The Transformative Impact of Container Technology in Public Sector Organizations
Témolé, F., & Atanasova, D. (2025) · Science Technology & Public Policy · doi:10.11648/j.stpp.20250901.13
Adaptação Automática de Pipelines de Video Analytics com Suporte de Observabilidade
dos Santos, L. A., Gomes, F., Bonfim, M., Maia, J. G. R., Trinta, F. A. M., & Rêgo, P. A. L. (2025) · doi:10.5753/sbrc.2025.6389
Distributed Tracing for Service Function Chaining in Named Data Networking
Kobayashi, H., Kanai, K., Nakazato, H., & Detti, A. (2025) · IEEE International Conference on High Performance Switching and Routing (HPSR) · doi:10.1109/hpsr64165.2025.11038905
Driving Autonomous Networks: 5G Observability Experiment Architecture for Network-on-Wheels
Collins, T., Buysse, J., Shahid, A., & Moerman, I. (2025) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms57970.2025.11073653
“Traditional monitoring suffers from predefined output rules, considered unfit for modern distributed systems [17].”
From Dashboards to Decisions:Teaching Observability in a Simulation-Based Graduate Analytics CoursePreprint
Castelblanco, A. X. R., & Galarza, J. I. S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5399979
From Digital Services to Sustainable Ones: Novel Industry 5.0 Environments Enhanced by Observability
Sabbioni, A., Antonio Corradi, A., Monti, S., & Rolt, C. R. D. (2025) · Information · doi:10.3390/info16090821
Scalable Data Archival & Purging Mechanism for HighVolume Applications
Sayyed, Z. (2025) · International Journal of Sustainability and Innovation in Engineering · doi:10.56830/ijsie202412
Rumo a um Catálogo de Anti-padrões de Observabilidade: Uma Revisão da Literatura
Gomes, F., Rêgo, P. A. L., & Trinta, F. A. M. (2025) · Simpósio Brasileiro de Engenharia de Software (SBES) · doi:10.5753/sbes.2025.9779
“…é um con-junto crescente de práticas combinadas com ferramentas que vão além do mero monitoramento, fornecendo insights sobre o estado interno de um sistema por meio da análise de suas saídas externas, predominantemente conhecidas como dados de telemetria, como métricas, logs e traces [42].”
InfluentialObservability and Auding in a Scalable Multi-Tenant Distributed Systems
Punugoti, R., & Manam, S. (2025) · IntechOpen eBooks · doi:10.5772/intechopen.1011667
Tang, X., Zhu, Y., Wang, J., Li, B., & Hung, P. C. K. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-1581-3_11
Applying OpenTelemetry Metrics to Monitor Urban Air Quality Sensors
Ghitulan, A., Petruc, S.-I., Ionascu, M.-E., Bogdan, R., & Marcu, M. (2025) · RoEduNet Conference: Networking in Education and Research (RoEduNet) · doi:10.1109/roedunet68395.2025.11208437
“OpenTelemetry (OTel) is an open-source observability framework designed to standardize the collection of telemetry data in distributed systems [10], [11].”
Architecting Observability for AI-Driven Microservices at Scale
Gaddam, M. K. (2025) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici65217.2025.11252857
“This section and Figure 1 details the data flow, core equations involved in telemetry processing and anomaly detection, and how AI-specific observability metrics are computed [2].”
Menghnani, M. (2025) · IEEE Madhya Pradesh Section Conference (MPCON) · doi:10.1109/mpcon66082.2025.11256601
“In conclusion, they delve into open research questions and potential research avenues that will stimulate more study in this field [16].”
Remote-Access GPU-Isolation Docker Architecture for LLMs Enhanced Programming Education
Chan, K., & Im, S. (2025) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale66047.2025.11346766
“Without proper isolation or observability, a single container can overwhelm the shared memory bandwidth, which can trigger performance degradation for other tenants [38], [39].”
Metadata
- Authors
- Usman, M., Ferlin, S., Brunstrom, A., & Taheri, J.
- Affiliations
- Karlstad University (SE)
- Department of Computer Science, Karlstad University, Karlstad, Sweden
- Venue
- IEEE Access
- Published
- 2022
- Volume
- 10
- Pages
- 86904-86919
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4286544722
- Cited by
- 124
- References
- 75
- Citations by year
- 2026: 23 · 2025: 48 · 2024: 29 · 2023: 22 · 2022: 2
- FWCI
- 11.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- Digitala vetenskapliga arkivet (Diva) (Karlstad University) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Knowledge Foundation of Sweden (KKS) through the Synergy Project AIDA—A Holistic AI-Driven Networking and Processing Framework for Industrial IoT (Rek:20200067)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- observability, edge computing, microservices, containerization, distributed systems, industrial automation, 5G, Industrial Internet of Things
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