A Survey on Observability of Distributed Edge & Container-Based Microservices
Open accessAbstract
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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Adopting a microservice architecture increases operational complexity in terms of monitoring and observability.
“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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Edge computing: Vision and challenges
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2579198 · Cited by 8,733 · cited 2× · see citing works
“Furthermore, considerable developments have occurred since then [2], [14], [15], and several aspects of distributed IT systems and application patterns have revamped.”
BackgroundA Survey on the Edge Computing for the Internet of Things
Yu, W., Liang, F., He, X., Hatcher, W. G., Lu, C., Lin, J., & Yang, X. (2018) · IEEE Access · doi:10.1109/access.2017.2778504 · Cited by 1,520 · cited 1× · see citing works
“Therefore, it is more than just a cloud extension and also functions as an integration platform for cloud and a multitude of services to facilitate and ensure successful system interaction [20].”
BackgroundUsing magpie for request extraction and workload modelling
Barham, P., Donnelly, A., Isaacs, R., & Mortier, R. (2004) · Operating Systems Design and Implementation · Cited by 571 · see citing works
Aceto, G., Alessio Botta, B., de Donato, W., & Pescapè, A. (2013) · Computer Networks · doi:10.1016/j.comnet.2013.04.001 · Cited by 559 · cited 3× · see citing works
“technologies, only a few studies such as [7], [12], [13] survey this field.”
BackgroundEbert, C., Gallardo, G., Hernantes, J., & Serrano, N. (2016) · IEEE Software · doi:10.1109/ms.2016.68 · Cited by 504 · see citing works
Dapper, a Large-Scale Distributed Systems Tracing Infrastructure
Sigelman, B. H., Barroso, L. A., Burrows, M., Stephenson, P., Plakal, M., Beaver, D., Jaspan, S., & Shanbhag, C. (2010) · Cited by 477 · see citing works
X-trace: a pervasive network tracing framework
Fonseca, R., Porter, G., Katz, R. H., Shenker, S. J., & Stoica, I. G. (2007) · Cited by 438 · see citing works
Foundations and Evolution of Modern Computing Paradigms: Cloud, IoT, Edge, and Fog
Donno, M. D., Tange, K., & Dragoni, N. (2019) · IEEE Access · doi:10.1109/access.2019.2947652 · Cited by 372 · cited 1× · see citing works
“To address centralized cloud computing constraints, research interests are shifting towards distributed paradigms [19].”
BackgroundHurwitz, J., Bloor, R., Kaufman, M., & Halper, F. (2009) · Cited by 241 · see citing works
Serverless Edge Computing: Vision and Challenges
Aslanpour, M. S., Toosi, A. N., Cicconetti, C., Javadi, B., Sbarski, P., Taibi, D., Assuncao, M., Gill, S. S., Gaire, R., & Dustdar, S. (2021) · 2021 Australasian Computer Science Week Multiconference · doi:10.1145/3437378.3444367 · Cited by 236 · cited 1× · see citing works
“These are all diverse application scenarios in which an observability system must timely support dynamic configurations and decision-making based on collected data [104], [105].”
BackgroundGannon, D. B., Barga, R., & Sundaresan, N. (2017) · IEEE Cloud Computing · doi:10.1109/mcc.2017.4250939 · Cited by 222 · cited 1× · see citing works
“The cloud computing era introduced application developers to the concept of cloud-native [21], a model in which developers are more concerned with how applications are created, deployed, and maintained at hyper-scale rather than where they are physically deployed.”
Site Reliability Engineering: How Google Runs Production Systems
Beyer, B., Jones, C., Petoff, J. L. M., & Murphy, N. R. (2016) · Cited by 206 · see citing works
Gerhards, R. (2009) · doi:10.17487/rfc5424 · Cited by 192 · see citing works
A review of edge computing: Features and resource virtualization
Mansouri, Y., & Babar, M. A. (2021) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2020.12.015 · Cited by 190 · cited 1× · see citing works
“The most challenging aspect of edge performance observability is the emergence of new, more complex architectures ranging from containers, virtual machines, and clusters [103].”
BackgroundMulti-Access Edge Computing Architecture, Data Security and Privacy: A Review
Ali, B., Gregory, M. A., & Li, S. (2021) · IEEE Access · doi:10.1109/access.2021.3053233 · Cited by 161 · see citing works
From Monolithic Architecture to Microservices Architecture
Lauretis, L. D. (2019) · IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) · doi:10.1109/issrew.2019.00050 · Cited by 123 · cited 1× · see citing works
“A principal shift is the breakdown of large monolithic and coarse-grained applications into fine-grained deployment units named microservices, communicating predominantly by way of synchronous Representational State Transfer (REST) and asynchronous events [23].”
BackgroundArchitecting Microservices: Practical Opportunities and Challenges
Baskarada, S., Nguyen, V., & Koronios, A. (2018) · Journal of Computational Information Systems · doi:10.1080/08874417.2018.1520056 · Cited by 121 · cited 2× · see citing works
“Furthermore, considerable developments have occurred since then [2], [14], [15], and several aspects of distributed IT systems and application patterns have revamped.”
BackgroundBuilding Modern Clouds: Using Docker, Kubernetes & Google Cloud Platform
Shah, J. A., & Dubaria, D. (2019) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc.2019.8666479 · Cited by 117 · cited 2× · see citing works
“Cilium [60] is the eBPF-based open-source solution to secure network connectivity among services that are deployed with Linux container management platforms such as Docker and Kubernetes [10].”
BackgroundMethodZhang, T., Li, Y., & Chen, C. L. P. (2020) · Information Sciences · doi:10.1016/j.ins.2020.12.021 · Cited by 92 · cited 1× · see citing works
“These are all diverse application scenarios in which an observability system must timely support dynamic configurations and decision-making based on collected data [104], [105].”
BackgroundQuality of Experience Framework for Cloud Computing (QoC)
Laghari, A. A., He, H. X., Khan, A., Kumar, N., & Kharel, R. (2018) · IEEE Access · doi:10.1109/access.2018.2865967 · Cited by 89 · cited 1× · see citing works
“Laghari et al. [59] conducted a study on the QoE framework for cloud computing to monitor end-users’ video streaming services.”
BackgroundZhang, Q., Ng, K. K. W., Kazer, C. W., Shen, Y., Sedoc, J., & Liu, V. (2021) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/3452296.3472926 · Cited by 67 · cited 1× · see citing works
“Zhang. et al. [85] describe a system named MimicNet for accurate performance estimates for large datacenters.”
BackgroundLow-Cost Adaptive Monitoring Techniques for the Internet of Things
Trihinas, D., Pallis, G., & Dikaiakos, M. D. (2018) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2018.2808956 · Cited by 65 · cited 1× · see citing works
“Trihinas et al. introduced a lightweight, adaptive monitoring framework for smart IoT devices with limited processing capabilities [49].”
BackgroundOn Observability and Monitoring of Distributed Systems – An Industry Interview Study
Niedermaier, S., Koetter, F., Freymann, A., & Wagner, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33702-5_3 · Cited by 65 · see citing works
Cloud monitoring: A review, taxonomy, and open research issues
Syed, H. J., Gani, A., Ahmad, R. W., Khan, M. K., & Ahmed, A. I. A. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.08.021 · Cited by 64 · cited 2× · see citing works
“Despite the systematic literature review on monitoring technologies, only a few studies such as [7], [12], [13] survey this field.”
BackgroundMethodEfficient Network Monitoring Applications in the Kernel with eBPF and XDP
Abranches, M., Michel, O., Keller, E. R., & Schmid, S. (2021) · IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) · doi:10.1109/nfv-sdn53031.2021.9665095 · Cited by 63 · cited 1× · see citing works
“[84] developed a software-based network monitoring framework that significantly reduces network”
MethodSelf-adaptive cloud monitoring with online anomaly detection
Wang, T., Xu, J., Zhang, W., Gu, Z., & Zhong, H. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.09.067 · Cited by 60 · cited 2× · see citing works
“The key for observability tools is to identify a significant anomaly from a potential number of other anomalies and link that issue to other pertinent data from log files, traces, or metrics [42], [73], [79].”
BackgroundViperProbe: Rethinking Microservice Observability with eBPF
Levin, J., & Benson, T. (2020) · IEEE International Conference on Cloud Networking · doi:10.1109/cloudnet51028.2020.9335808 · Cited by 58 · cited 1× · see citing works
“ViperProbe [61] proposed a scalable eBPF-based dynamic and adaptive microservices metrics collection framework.”
MethodThe rise of eBPF for non-intrusive performance monitoring
Cassagnes, C., Trestioreanu, L., Joly, C., & State, R. (2020) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms47738.2020.9110434 · Cited by 54 · cited 1× · see citing works
“Cassagnes et al. [62] discussed lessons learned after using eBPF to monitor and profile performance.”
MethodA protocol-independent container network observability analysis system based on eBPF
Liu, C., Cai, Z., Wang, B., Tang, Z., & Liu, J. (2020) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads51040.2020.00099 · Cited by 54 · cited 1× · see citing works
“A protocol-independent network monitoring at the kernel level for the Alibaba Kubernetes cluster is proposed in [64].”
MethodNoor, A., Jha, D. N., Mitra, K., Jayaraman, P. P., Souza, A., Ranjan, R. K., & Dustdar, S. (2019) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2019.00035 · Cited by 45 · cited 1× · see citing works
“A generic monitoring framework Multi-microservices Multi-virtualization Multi-cloud (M3) [57] is introduced to monitor the performance of microservices deployed over heterogeneous virtualization platforms in a multi-cloud environment. milliScope (mScope) [58] is a millisecond granularity…”
Cloud applications monitoring: An industrial study
Tamburri, D. A., Miglierina, M., & Nitto, E. D. (2020) · Information and Software Technology · doi:10.1016/j.infsof.2020.106376 · Cited by 44 · cited 2× · see citing works
“Despite the systematic literature review on monitoring technologies, only a few studies such as [7], [12], [13] survey this field.”
BackgroundA Black-box Monitoring Approach to Measure Microservices Runtime Performance
Brondolin, R., & Santambrogio, M. D. (2020) · ACM Transactions on Architecture and Code Optimization · doi:10.1145/3418899 · Cited by 43 · cited 1× · see citing works
“A black-box monitoring approach to track microservices at scale, concentrating on architectural metrics, power consumption, application, and network performance is discussed in [55].”
BackgroundA survey on quality of service in cloud computing
Jelassi, M., Ghazel, C., & Saidane, L. A. (2017) · International Conference on Frontiers of Signal Processing (ICFSP) · doi:10.1109/icfsp.2017.8097142 · Cited by 38 · cited 1× · see citing works
“To the best of our knowledge, there is no other survey covering the same scope as ours, however, there are a number of monitoring surveys available [7], [26]–[29].”
BackgroundCross-Layer Multi-Cloud Real-Time Application QoS Monitoring and Benchmarking As-a-Service Framework
Alhamazani, K., Ranjan, R., Jayaraman, P. P., Mitra, K., Liu, C., Rabhi, F., Georgakopoulos, D., & Wang, L. (2015) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2015.2441715 · Cited by 34 · cited 1× · see citing works
“Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service (CLAMBS) [37] introduced a solution for QoS monitoring and benchmarking of cloud applications deployed in multi-cloud environments.”
BackgroundRighi, R. D. R., Lehmann, M. B., Gomes, M. M., Nobre, J. C., da Costa, C. A., Rigo, S. J., Lena, M., Mohr, R. F., & de Oliveira, L. R. B. (2019) · Journal of Grid Computing · doi:10.1007/s10723-018-09471-x · Cited by 34 · cited 1× · see citing works
“Rodrigo et al. discussed enterprise system resource and application monitoring from performance and architectural perspectives [30].”
BackgroundNetwork's server monitoring and analysis using Nagios
Renita, J., & Elizabeth, N. E. (2017) · International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) · doi:10.1109/wispnet.2017.8300092 · Cited by 30 · cited 1× · see citing works
“For instance, Ops teams managing monolithic applications, often, set up Nagios [25] checks to acquire insights into the application performance.”
BackgroundFMonE: A Flexible Monitoring Solution at the Edge
Brandón, Á., Pérez, M. S., Montes, J., & Sánchez, A. (2018) · Wireless Communications and Mobile Computing · doi:10.1155/2018/2068278 · Cited by 30 · cited 2× · see citing works
“Flexible monitoring solution at the edge (FMonE) [35] is proposed to allow monitoring workflows that comply with the demands of edge computing for gathering system-level performance metrics.”
BackgroundMethodmilliScope: A Fine-Grained Monitoring Framework for Performance Debugging of n-Tier Web Services
Lai, C.-A., Kimball, J., Zhu, Τ., Wang, Q., & Pu, C. (2017) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2017.228 · Cited by 29 · cited 1× · see citing works
“…to monitor the performance of microservices deployed over heterogeneous virtualization platforms in a multi-cloud environment. milliScope (mScope) [58] is a millisecond granularity software-based resource and event monitoring framework offering both low overhead at high frequency and high…”
BackgroundA Holistic Monitoring Service for Fog/Edge Infrastructures: A Foresight Study
Abderrahim, M., Ouzzif, M., Guillouard, K., François, J., & Lèbre, A. (2017) · International Conference on Future Internet of Things and Cloud (FiCloud) · doi:10.1109/ficloud.2017.30 · Cited by 29 · cited 1× · see citing works
“To the best of our knowledge, there is no other survey covering the same scope as ours, however, there are a number of monitoring surveys available [7], [26]–[29].”
BackgroundTowards AIOps in Edge Computing Environments
Becker, S., Schmidt, F., Gulenko, A., Acker, A., & Kao, O. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378038 · Cited by 25 · cited 2× · see citing works
“F3) Incident response: Finally, the observability system should handle issues without manual intervention through automatic remediation using machine learning [34], [101].”
BackgroundMethodA Monitoring Framework for Multi-Site 5G Platforms
Pérez, R., García-Reinoso, J., Zabala, A., Serrano, P., & Banchs, A. (2020) · European Conference on Networks and Communications · doi:10.1109/eucnc48522.2020.9200914 · Cited by 24 · cited 1× · see citing works
“Perez et al. [52] presented a monitoring architecture for the distribution and ingestion of metrics and KPIs for 5G multi-site platforms, where disparate verticals from different stakeholders are applied over a shared infrastructure.”
BackgroundCloud Monitoring System: Basics, Phases and Challenges
Birje, M. N., & Bulla, C. M. (2019) · International Journal of Recent Technology and Engineering (IJRTE) · doi:10.35940/ijrte.c6857.098319 · Cited by 23 · cited 1× · see citing works
“Bula et al. [31] characterize various phases of cloud monitoring activities and present a comparative analysis of the state-of-the-art works of these phases.”
BackgroundCombining System Visibility and Security Using eBPF.
Deri, L., Sabella, S., & Mainardi, S. (2019) · Cited by 23 · see citing works
CloudProcMon: A Non-Intrusive Cloud Monitoring Framework
Syed, H. J., Gani, A., Nasaruddin, F. H., Naveed, A., Ahmed, A. I. A., & Khan, M. K. (2018) · IEEE Access · doi:10.1109/access.2018.2864573 · Cited by 20 · cited 1× · see citing works
“Syed et al. [41] focused on collecting data from the host Operating System (OS) non-intrusively and linked data with the cloud controller via the proc filesystem (procfs) of the host OS.”
BackgroundCelesti, A., & Fazio, M. C. (2018) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2018.10.015 · Cited by 17 · cited 1× · see citing works
“and Storm (real-time data analytics) is proposed in [50].”
BackgroundA Framework for Detecting System Performance Anomalies Using Tracing Data Analysis
Kohyarnejadfard, I., Aloise, D., Dagenais, M., & Shakeri, M. (2021) · Entropy · doi:10.3390/e23081011 · Cited by 17 · cited 1× · see citing works
“Traditional monitoring tools, however, do fall short of meeting the challenges posed by operation monitoring demands of containerized microservices deployed over distributed IT infrastructures [8].”
BackgroundCyber-physical application monitoring across multiple clouds
Noor, A., Mitra, K., Solaiman, E., Souza, A., Jha, D. N., Demirbaga, Ü., Jayaraman, P. P., Cacho, N., & Ranjan, R. K. (2019) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2019.06.007 · Cited by 16 · cited 1× · see citing works
“Multi-virtualization and Multi-cloud monitoring in cloud-based Cyber-PhysicAl systems (M2CPA) [39] keeps track of the performance of application components that are operating over diverse virtualization platforms and deployed across multiple clouds.”
BackgroundMonitoring of distributed systems
Shim, Y.-C., & Ramamoorthy, C. V. (2002) · Symposium on Applied Computing · doi:10.1109/soac.1991.143885 · Cited by 15 · see citing works
A monitoring framework for 5G service deployments
Angelopoulos, Y., Trouva, E., & Xilouris, G. K. (2017) · IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) · doi:10.1109/camad.2017.8031617 · Cited by 13 · cited 1× · see citing works
“CHARISMA [51] discusses the key features of monitoring as well as the main requirements to resource monitoring systems for future 5G deployments and services.”
BackgroundUsman, M., Risdianto, A. C., Han, J., & Kim, J. (2018) · The Computer Journal · doi:10.1093/comjnl/bxy103 · Cited by 13 · cited 1× · see citing works
“We agree that topology information is vital to understand the relationships between components in dynamic, distributed cloud-native environments [81], [100].”
BackgroundSelf-citationWurster, M., Breitenbücher, U., Antonio Brogi, A., Leymann, F., & Soldani, J. (2020) · International Conference on Cloud Computing and Services Science · doi:10.5220/0009571001710180 · Cited by 12 · cited 2× · see citing works
“Cloud-native technologies [1] empower organizations to build and run applications in modern, scalable and dynamic distributed Information Technology (IT) infrastructures.”
BackgroundAlimi, I. A., Patel, R. K., Zaouga, A., Muga, N. J., Xin, Q., Pinto, A. N., & Monteiro, P. P. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.98315 · Cited by 12 · cited 2× · see citing works
“The need to achieve excellent Quality of Service (QoS) to facilitate good Quality of Experience (QoE) is one of the notable factors VOLUME 10, 2022 that has brought substantial evolution in the distributed infrastructures [14].”
BackgroundSmartX Box: Virtualized Hyper-Converged Resources for Building an Affordable Playground
Risdianto, A. C., Usman, M., & Kim, J. (2019) · Electronics · doi:10.3390/electronics8111242 · Cited by 10 · cited 1× · see citing works
“Cloud computing [17], [18] is one such big paradigm that gained traction in the mid-2000s with the introduction of the Amazon Web Services (AWS).”
BackgroundSelf-citationMaintaining SmartX multi‐view visibility for OF@TEIN+ distributed cloud‐native edge boxes
Rathore, M. A., Usman, M., & Kim, J. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4101 · Cited by 10 · cited 1× · see citing works
“In addition, Ahmad et al. [82] presented a setup of distributed resources with cloud-native edge capabilities and a solution for persistently maintaining monitoring data collections in a variety of environments.”
BackgroundSelf-citationPubic Cloud Computing: Big Three Vendors
Alkhatib, A. A., Sabbagh, A. A., & Maraqa, R. (2021) · doi:10.1109/icit52682.2021.9491680 · Cited by 10 · cited 1× · see citing works
“As IT infrastructures continue to grow and become more distributed [5], enticed by a prospect of faster innovation, cost reduction, and boost agility, many businesses are migrating their traditional monolithic applications to microservices.”
BackgroundSmartX Multi‐View Visibility Framework for unified monitoring of SDN‐enabled multisite clouds
Usman, M., & Kim, J. (2019) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.3819 · Cited by 9 · cited 1× · see citing works
“In [36], an extension of SmartX MVF with flow-centric visibility is presented for the simultaneous monitoring of physical-virtual resources and flows classification for secure operation of private clouds.”
Self-citationAdvancing Monitoring in Microservices Systems
Cinque, M., Corte, R. D., & Pecchia, A. (2019) · IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) · doi:10.1109/issrew.2019.00060 · Cited by 9 · cited 2× · see citing works
“Logs-based monitoring is focused in [56].”
BackgroundSmartX multiview visibility framework leveraging open-source software for SDN-cloud playground
Usman, M., Risdianto, A. C., Han, J., Kang, M., & Kim, J. (2017) · IEEE International Conference on Network Softwarization (NetSoft) · doi:10.1109/netsoft.2017.8004242 · Cited by 8 · cited 1× · see citing works
“For private clouds, SmartX Multi-View Visibility Framework (MVF) [77]–[81] proposed to unify the multi-layer visibility of physical and virtual resources for timely detection and mitigation of various operational issues.”
Self-citationTaylor, J. T., & Taylor, W. T. (2021) · Apress eBooks · doi:10.1007/978-1-4842-6440-9_5 · Cited by 8 · see citing works
Resource monitoring and visualization for OF@TEIN SDN-enabled multi-site cloud
Usman, M., Risdianto, A. C., & Kim, J. (2016) · International Conference on Information Networking · doi:10.1109/icoin.2016.7427150 · Cited by 7 · see citing works
Self-citationA scalable network-aware framework for cloud monitoring orchestration
Jabbarifar, M., Shameli‐Sendi, A., & Kemme, B. (2019) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.02.006 · Cited by 7 · cited 1× · see citing works
“A couple of frameworks also focused on self-adaptive monitoring [42] and network-aware monitoring [43] for cloud environments.”
BackgroundHybrid Software-Defined Network Monitoring
Abushagur, A. A. G., Chin, T. S., Kaspin, R., Omar, N., & Samsudin, A. T. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34914-1_23 · Cited by 7 · see citing works
A Multi-Objective Optimized Service Level Agreement Approach Applied on a Cloud Computing Ecosystem
de Azevedo, L. J. D. M., Estrella, J. C., Toledo, C. F. M., & Reiff‐Marganiec, S. (2020) · IEEE Access · doi:10.1109/access.2020.3006171 · Cited by 7 · cited 1× · see citing works
“Establishing end-to-end runtime performance insights for today’s dynamic, complex distributed IT environments is critical for guaranteeing containerized microservices agreed-upon Service Level Agreements (SLAs) [16].”
BackgroundUsman, M., Risdianto, A. C., Han, J., & Kim, J. (2017) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-60717-7_48 · Cited by 5 · cited 1× · see citing works
“The key for observability tools is to identify a significant anomaly from a potential number of other anomalies and link that issue to other pertinent data from log files, traces, or metrics [42], [73], [79].”
BackgroundSelf-citationA Survey on QoS in Cloud Computing Environment
Deepshikha, D., & Prakash, S. (2019) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc.2019.8819696 · Cited by 5 · see citing works
NetGraf: An End-to-End Learning Network Monitoring Service
Mohammed, B., Kiran, M., & Enders, B. (2021) · doi:10.1109/indis54524.2021.00007 · Cited by 5 · cited 1× · see citing works
“F3) Incident response: Finally, the observability system should handle issues without manual intervention through automatic remediation using machine learning [34], [101].”
MethodRathore, M. A., Usman, M., & Kim, J. (2018) · International Conference on Future Internet Technologies · doi:10.1145/3226052.3226053 · Cited by 3 · see citing works
Self-citationEMMCS: An Edge Monitoring Framework for Multi-Cloud Environments using SNMP
Khoudali, S., Benzidane, K., & Sekkaki, A. (2019) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2019.0100178 · Cited by 3 · cited 1× · see citing works
“Edge Monitoring Framework for Multi-Cloud Environments (EMMCS) [33] monitors edge computing infrastructure and employs RESTful microservices.”
MethodAn Effective Multivariate Control Framework for Monitoring Cloud Systems Performance
Hababeh, I. O., Thabain, A., & Alouneh, S. (2019) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2019.01.006 · Cited by 3 · cited 1× · see citing works
“Hababeh et al. proposed a practical multivariate control framework for monitoring cloud systems performance in [38].”
BackgroundReport from the IAB COVID-19 Network Impacts Workshop 2020
Arkko, J., Farrell, S., Kühlewind, M., & Perkins, C. S. (2021) · doi:10.17487/rfc9075 · Cited by 3 · see citing works
McCollam, R. (2022) · Apress eBooks · doi:10.1007/978-1-4842-8309-7_1 · Cited by 3 · see citing works
Conceptual Verification of Multi-Level Visibility Points for SmartX MultiView Security
Shin, J.-S., Usman, M., & Kim, J. (2018) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc.2018.8539523 · Cited by 2 · cited 2× · see citing works
“We agree that topology information is vital to understand the relationships between components in dynamic, distributed cloud-native environments [81], [100].”
BackgroundMethodSelf-citation(2020) · doi:10.1002/9781119561569.ch2 · Cited by 2 · see citing works
Sahay, R. (2020) · Apress eBooks · doi:10.1007/978-1-4842-6200-9_5 · Cited by 1 · see citing works
(2022) · Prometheus · doi:10.13169/prometheus.38.2.0001 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- C. Ebert, G. Gallardo, J. Hernantes, and N. Serrano, "DevOps," IEEE Software, vol. 33, no. 3, pp. 94-100, May 2016, conference Name: IEEE Software.
- M. B. Chhetri, Q. B. Vo, and R. Kowalczyk, "CL-SLAM: Cross-Layer SLA Monitoring Framework for Cloud Service-Based Applications," in 2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC), Dec. 2016, pp. 30-36.
- G. Fournier, S. Afchain, and S. Baubeau, "Runtime security monitoring with ebpf," in 17th SSTIC Symposium sur la Sécurité des Technologies de l'Information et de la Communication, 2021.
- Louis, MO, USA: IEEE, Nov. 2021, pp. 12-22. [Online]. Available: https://ieeexplore.ieee.org/document/9652600/
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Sorted by citations
Klieštik, T., Nica, E., Ďurana, P., & Popescu, G. H. (2023) · Oeconomia Copernicana · doi:10.24136/oc.2023.033 · Cited by 139 · see citing works
A Joint Study of the Challenges, Opportunities, and Roadmap of MLOps and AIOps: A Systematic Survey
Díaz-de-Arcaya, J., Torre-Bastida, A. I., Zárate, G., Miñón, R., & Almeida, A. (2023) · ACM Computing Surveys · doi:10.1145/3625289 · Cited by 105 · see citing works
“In this regard, observability is crucial for the adoption of AI-based solutions, and is being conducted in various areas, including distributed cloud applications [114].”
BackgroundMulti-UAV Collaborative Absolute Vision Positioning and Navigation: A Survey and Discussion
Tong, P., Yang, X., Yang, Y., Liu, W., & Wu, P. (2023) · Drones · doi:10.3390/drones7040261 · Cited by 79 · see citing works
“…based on the feature using the connectivity of the image data and the module connection segmentation method, and the feature of each module is reconstructed using the segmentation contour extraction technology and the topological data editing function, or directly using the simple model function.”
MethodToward the Observability of Cloud-Native Applications: The Overview of the State-of-the-Art
Kosińska, J., Baliś, B., Konieczny, M., Malawski, M., & Zieliński, S. (2023) · IEEE Access · doi:10.1109/access.2023.3281860 · Cited by 63 · see citing works
Hossain, M. D., Sultana, T., Akhter, S., Hossain, M. I., Thu, N. T., Huynh, L. N. T., Lee, G., & Huh, E. (2023) · ICT Express · doi:10.1016/j.icte.2023.06.006 · Cited by 70 · see citing works
Faseeha, U., Syed, H. J., Samad, F., Zehra, S., & Ahmed, H. M. (2025) · IEEE Access · doi:10.1109/access.2025.3562125 · Cited by 41 · see citing works
AIDA—A holistic AI-driven networking and processing framework for industrial IoT applications
Chahed, H., Usman, M., Chatterjee, A., Bayram, F., Chaudhary, R., Brunström, A., Taheri, J., Ahmed, B. S., & Kassler, A. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100805 · Cited by 30 · see citing works
Self-citationKratzke, N. (2022) · Future Internet · doi:10.3390/fi14100274 · Cited by 34 · see citing works
“An interesting and recent overview on observability of distributed edge and container-based microservices is provided by [42].”
BackgroundIntelligentMonitor: Empowering DevOps Environments with Advanced Monitoring and Observability
Thantharate, P. (2023) · doi:10.1109/icit58056.2023.10226123 · Cited by 23 · see citing works
“It offers rich, detailed insights into the workings of a system, helping teams understand not just when and where issues occur but why [5].”
BackgroundInfluentialXie, Z., Zhang, S., Geng, Y., Zhang, Y., Ma, M., Nie, X., Yao, Z., Xu, L., Sun, Y., Li, W., et al. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671530 · Cited by 23 · see citing works
“Observabilityiscrucialformicroser-vicessystems[ 8, 19], and RCA algorithms heavily rely on them to obtain the system status.”
MicroIRC: Instance-level Root Cause Localization for Microservice Systems
Zhu, Y., Wang, J., Li, B., Zhao, Y., Zhang, Z., Xiong, Y., & Chen, S. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112145 · Cited by 22 · see citing works
Azizi, S., Shojafar, M., Farzin, P., & Dogani, J. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.12.016 · Cited by 19 · see citing works
Mapping DevOps capabilities to the software life cycle: A systematic literature review
Amaro, R., Pereira, R., & da Silva, M. M. (2024) · Information and Software Technology · doi:10.1016/j.infsof.2024.107583 · Cited by 28 · see citing works
“Keeping the project on schedule and within scope prevents costly changes and rework later in the development cycle [11,21,51,57].”
InfluentialCdascaler: a cost-effective dynamic autoscaling approach for containerized microservices
Shafi, N., Abdullah, M., Iqbal, W., Erradi, A., & Bukhari, F. (2024) · Cluster Computing · doi:10.1007/s10586-023-04228-y · Cited by 11 · see citing works
“Cloud computing provides distributed microservices architecture because microservices are cohesive, self-contained, and independent [21] [22].”
AIOps in DevOps: Leveraging Artificial Intelligence for Operations and Monitoring
Abbas, S. I., & Garg, A. (2024) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl61749.2024.00016 · Cited by 11 · see citing works
Intelligent Root Cause Localization in MicroService Systems: A Survey and New Perspectives
Fu, N., Cheng, G., Teng, Y., Dai, G., Yu, S., & Chen, Z. (2025) · ACM Computing Surveys · doi:10.1145/3736755 · Cited by 11 · see citing works
“The core of the observability in microser-vice systems is to learn the state of infrastructure quickly and of services hosted in it, avoiding extended outages, and during an outage, quickly enable root cause localization of the problem [127].”
AI-Driven DevOps Transformation: A Paradigm Shift in Software Development
Bali, M. K., Mehdi, A., & Hariharan, H. (2024) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl61749.2024.00026 · Cited by 9 · see citing works
“The [13] software release lifecycle is therefore likewise extremely lengthy.”
MethodAkbari, M., Bolla, R., Bruschi, R., Davoli, F., Lombardo, C., & Siccardi, B. (2024) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops59551.2024.10615948 · Cited by 9 · see citing works
“A monitoring approach tailored to a specific component would offer only local information, which might not be adequate for an operations team to identify the cause of a problem, let alone predict its recurrence in the future [20].”
BackgroundAdame, T., Amri, E., Antonopoulos, G., Azaiez, S., Berne, A., Camargo, J. S., Kakoulidis, H., Kleisarchaki, S., Llamedo, A., Prasinos, M., et al. (2024) · Sensors · doi:10.3390/s24165283 · Cited by 9 · see citing works
“This complexity underscores the need for advanced monitoring strategies that can effectively navigate and interpret the multifaceted interactions within these systems [35].”
BackgroundHammad, Y., Ahmad, A. A.-S., & Andras, P. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112573 · Cited by 9 · see citing works
Towards Future Vehicle Diagnostics in Software-Defined Vehicles
Bickelhaupt, S., Hahn, M., Morozov, A. S., & Weyrich, M. (2024) · SAE International Journal of Advances and Current Practices in Mobility · doi:10.4271/2024-01-2981 · Cited by 8 · see citing works
Open source container orchestration for Industry 4.0 – requirements and systematic feature analysis
Alamoush, A., & Eichelberger, H. (2024) · International Journal on Software Tools for Technology Transfer · doi:10.1007/s10009-024-00767-w · Cited by 8 · see citing works
“In addition, Usman et al. [35] mention system level monitoring [35] of virtualized, distributed systems (SysR-13) as a requirement, e.g., to detect critical situations.”
Gomes, F., Rêgo, P. A. L., & Trinta, F. A. M. (2025) · Computing · doi:10.1007/s00607-025-01540-w · Cited by 8 · see citing works
“According to Usman et al. [68], observability is an expanding set of practices combined with tools that go beyond mere monitoring, providing insights about the internal state of a system through the analysis of its external outputs, predominantly known as telemetry data, such as metrics, logs, and…”
InfluentialDESK: Distributed Observability Framework for Edge-Based Containerized Microservices
Usman, M., Ferlin, S., Brunström, A., & Taheri, J. (2023) · Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) · doi:10.1109/eucnc/6gsummit58263.2023.10188344 · Cited by 8 · see citing works
“To this end, observability is an emerging set of practices and tools that goes beyond monitoring with the goal to show why a system is not operative, thus providing insights of internal state of a system by analyzing its external outputs [3].”
BackgroundSelf-citationCorreia, M. P., Oliveira, W., & Cecílio, J. (2023) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2023.103035 · Cited by 10 · see citing works
Monitoring performance metrics in low-power wireless systems
Graf, F., Watteyne, T., & Villnow, M. (2024) · ICT Express · doi:10.1016/j.icte.2024.08.004 · Cited by 7 · see citing works
Cost-Profiling Microservice Applications Using an APM Stack
de Vries, S., Blaauw, F. J., & Andrikopoulos, V. (2023) · Future Internet · doi:10.3390/fi15010037 · Cited by 7 · see citing works
Toward Context-Aware Anomaly Detection for AIOps in Microservices Using Dynamic Knowledge Graphs
Moens, P., Steenwinckel, B., Ongenae, F., Volckaert, B., & Hoecke, S. V. (2026) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2026.3652304 · Cited by 6 · see citing works
5GC-Tracer: A Novel Non-Intrusive Distributed Tracing for Enhanced 5G Core Network Observability
Zhao, A., Desnoes, F., Fajjari, I., & Aitsaadi, N. (2025) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms57970.2025.11073675 · Cited by 6 · see citing works
“We recall that observability refers to the ability to understand the internal state of a system based on its external outputs [2].”
Navigating the Complexities of the Cloud-Native World: A Study of Developer Perspectives
Bhardwaj, A. (2023) · International Conference on Advanced Communication Technologies and Networking (CommNet) · doi:10.1109/commnet60167.2023.10365297 · Cited by 6 · see citing works
“This complexity arises from various factors [50], [51], such as the need to handle high volumes of diverse data types, necessitating a robust and adaptable data infrastructure.”
BackgroundSelf Adjusting Log Observability for Cloud Native Applications
Pathak, D., Verma, M., Chakraborty, A., & Kumar, H. (2024) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud62652.2024.00061 · Cited by 8 · see citing works
“In microservices-based applications, the growing number of components further accentuate the importance of observability [18].”
BackgroundNetwork-Aware Reliability Modeling and Optimization for Microservice Placement
Zhang, F., Chen, Y., Lu, H., & Huang, Y. (2025) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2025.3562913 · Cited by 5 · see citing works
“Cloud-native technologies [1] empower the creation and operation of applications in massively scalable distributed infrastructures, leveraging microservices architecture (MSA) [2] alongside platform technologies [3] like containers and virtual machines.”
Enabling Cloud-Native Observability for White-Box Performance Analysis of the 5G Core
Menin, C. E., Silva, I. M., Alves, V. B., Lando, G., Both, C. B., Nogueira, J. M. S., & Wickboldt, J. A. (2025) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms57970.2025.11073613 · Cited by 5 · see citing works
“Observability is defined as “ the ability to infer a system’s internal state solely from its external outputs ” [16].”
DevSecLogs: AI-Powered, Tamper-Evident Log Intelligence for Secure CI/CD Pipelines
Saleh, S. M. (2025) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme64153.2025.00101 · Cited by 5 · see citing works
“Logs are the primary source of observability and accountability within these pipelines [5].”
Palomares, J., Coronado, E., Cardenas, A. J. A., Cebrián-Márquez, G., Carmona-Cejudo, E., & Fernández–Fernández, A. (2025) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2025.3546866 · Cited by 5 · see citing works
“…with MEC and the extension of these to field-level devices at the so-called extreme edge of the network provides substantial advantages in terms of latency, connectivity, and resource management, as well as intelligent decision making based on collected data and observability mechanisms [2].”
Large Language Models for Service-Oriented Computing (LLM4SOC): Review and Research Directions
Kumara, I., Kaplan, H., Owotogbe, J., Tamburri, D. A., & van den Heuvel, W. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-07313-6_6 · Cited by 4 · see citing works
Usman, M., Ferlin, S., & Brunström, A. (2024) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec62691.2024.00032 · Cited by 4 · see citing works
“At the same time, containerization technologies have revolutionized the deployment and management of real-world applications by facilitating the transition to microservices architectures [5].”
Self-citationA Cloud-Native Approach for Orchestrating 6G-Enabled Services at the Computing Continuum
Tzanettis, I., Kakkavas, G., Stylos, A., Zafeiropoulos, A., & Papavassiliou, S. (2024) · IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) · doi:10.1109/camad62243.2024.10942907 · Cited by 4 · see citing works
Cognitive Computing Continuum: State-of-the-Art Review and ENACT Vision & Approach
Kapetanidou, I. A., Nizamis, A., Karanastasis, E., Danciu, G. M., López, C. I. V., Kotsiopoulos, T., Gascón, J., Flor, J., Campbell, R., Liatifis, A., et al. (2025) · Journal of Grid Computing · doi:10.1007/s10723-025-09810-9 · Cited by 4 · see citing works
“Related Scientific Works Highly virtualised environments introduce several challenges in terms of resource utilisation and energy consumption [195], tracing of communication [77] and enforcement of security policies [78].”
SHEAB: A Novel Automated Benchmarking Framework for Edge AI
Abdulkadhim, M., & Répás, S. R. (2025) · Technologies · doi:10.3390/technologies13110515 · Cited by 3 · see citing works
An Empirical Study on Challenges of Event Management in Microservice Architectures
Laigner, R., Almeida, A. C., Assunção, W. K. G., & Zhou, Y. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3776581 · Cited by 3 · see citing works
“In distributed systems, such as microservice architectures, observability becomes more critical since problems can possibly span across multiple components that execute independently in a decentralized manner [11, 113].”
SeQaM: A Service Quality Manager for Edge Computing
Burbano, J. S., Pigovskyi, Y., Zhantileuov, E., Dokuchaev, I., Liyanage, M., Idrees, A. K., & Schuster, R. (2025) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc65282.2025.11059713 · Cited by 3 · see citing works
“…platforms offer a more holistic approach by integrating metrics, logs, and traces, their design remains specifically tailored to cloud environments and lacks the capability for fine-grained and adaptive data collection, limiting their usage as a stand-alone solution (e.g., Datadog, Signoz) [10].”
Explainability With Observation Sharing in Long Collaboration Chains of Automated Systems of Systems
Daubaris, P., Linkola, S., Inglés-Romero, J. F., Berrocal, J., Vicente-Chicote, C., Murillo, J. M., Mikkonen, T., & Mäkitalo, N. (2023) · IEEE Software · doi:10.1109/ms.2023.3320742 · Cited by 3 · see citing works
Application Deployment and Performance Measurement in Serverless Cloud for Microservices
Jhingran, S., & Rakesh, N. (2023) · International Conference on Sustainable Emerging Innovations in Engineering and Technology (ICSEIET) · doi:10.1109/icseiet58677.2023.10303332 · Cited by 3 · see citing works
A Cost-effective High-throughput Testbed for Supporting AI-enabled DevSecOps Services
Khamdamov, U., Usman, M., & Kim, J. (2023) · International Conference on Future Internet of Things and Cloud (FiCloud) · doi:10.1109/ficloud58648.2023.00022 · Cited by 3 · see citing works
“…technological advancements have occurred in both hardware and software domains as a result of paradigm shifts, such as switching from robust centralized cloud to latency-aware edge cloud and transitioning from static monolithic applications to elastic microservices architectural patterns [3].”
BackgroundSelf-citationAn approach for automatic self-recovery for a Node.js microservice
Chaplia, O., & Klym, H. (2023) · International Conference on Dependable Systems, Services and Technologies (DESSERT) · doi:10.1109/dessert61349.2023.10416461 · Cited by 3 · see citing works
“Stability and self-recovery within one service are particularly valuable when aiming for a high uptime percentage, such as the target of 99%, as suggested in the references [10, 11].”
BackgroundCloud, fog, edge computing and 5G technologies for industrial automation
Kang, K., Yu, C., & Zhong, R. Y. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13924-6.00013-2 · Cited by 3 · see citing works
Achieving Observability on Fog Computing with the Use of Open-Source Tools
Costa, B., Banerjee, A., Jayaraman, P. P., de Carvalho, L. R., Bachiega, J., & Araújo, A. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-63992-0_21 · Cited by 3 · see citing works
“This is a critical issue that requires additional research [49].”
BackgroundOptimizing Site Reliability Engineering with Cloud Infrastructure
John, Ł. (2025) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.1983 · Cited by 3 · see citing works
“It is predominantly convenient in cloud-native and micro services environments, where it has assisted the centralized logs from multiple sources and homogenise data collection across distributed systems [39].”
Beyond NWDAF Services for Comprehensive 5G Network Analytics and Orchestration
Hernández, I., Mejías, D., Fernández, Z., & Herranz, V. (2025) · International Conference on Computer, Information and Telecommunication Systems · doi:10.1109/cits65975.2025.11099169 · Cited by 2 · see citing works
“The adoption of virtualization, edge computing, and network slicing has made traditional monitoring approaches insufficient for capture the dynamic and distributed nature of modern mobile networks [1].”
Cloud Native Observability for an Enhanced Orchestration at the Telco Edge
Matysiak, P., Fajjari, I., Tarasiuk, H., & Ziółkowski, M. (2025) · International Conference on Communications · doi:10.1109/icc52391.2025.11162045 · Cited by 2 · see citing works
“Observability [7], [8] has recently gained momentum, resulting in a wealth of studies.”
Microsegmentation for containerized microservices in edge computing
Olotu, S. I., Oronti, A. O., & Alese, B. K. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-34109-0.00011-5 · Cited by 2 · see citing works
Kair: A Statistical and Causal Approach to Pinpointing Stragglers in Distributed Model Training
Yang, Y.-T., Liu, J., Chen, J.-P., Sun, X.-Y., Wo, T.-Y., Hu, C.-M., Song, C., Ouyang, J., & Yang, R.-Y. (2025) · International Conference on Automated Software Engineering · doi:10.1109/ase63991.2025.00311 · Cited by 2 · see citing works
“†Corresponding author: Xiaoyang Sun (X.Sun4@leeds.ac.uk) The observability tool [9] represents an emerging methodology for comprehending system states and diagnosing failures through the utilization of logs, metrics, and trace data within microservices.”
Warrens: Decentralized Connectionless Tunnels for Edge Container Networks
Goethals, T., Al-Naday, M., Volckaert, B., & Turck, F. D. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3417703 · Cited by 2 · see citing works
“Areview on the observability of distributed, container-based edge systems [18] illustrates the difficulty of using classical tools to analyze issues in the network edge, naming issues such as a decentralized nature and heterogeneity.”
BackgroundObservabilidade de Desempenho de Arquiteturas Monolíticas e Microsserviços com OpenTelemetry
Gomes, F., Gabriel, V., Rocha, L. S., Rêgo, P. A. L., & Trinta, F. A. M. (2024) · Anais do Seminário Integrado de Software e Hardware (SEMISH) · doi:10.5753/semish.2024.2313 · Cited by 2 · see citing works
“Em resumo, [Li et al. 2022] e [Usman et al. 2022] abordaram teoricamente a observabilidade como um todo, discutindo os benef ´ ıcios e desafios para adoc¸ ˜ ao, fo-cando exclusivamente em microsservic¸os.”
BackgroundExperiences and advances in reliability in retail projects
Koller, M. J. D., Carranza, M. E., Martinez, C., & Guzman, M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13242-1.00027-8 · Cited by 2 · see citing works
Rumo a uma Taxonomia de Observabilidade para Aplicações Baseadas em Microsserviços
Gomes, F. A. A., Rêgo, P. A. L., & Trinta, F. A. M. (2024) · Simpósio Brasileiro de Engenharia de Software (SBES) · doi:10.5753/sbes.2024.3386 · Cited by 2 · see citing works
BeeGuard: Explainability-based Policy Enforcement of eBPF Codes for Cloud-native Environments
Chowdhary, N., Satapathy, U., Benson, T., Chattopadhyay, S., Kodeswaran, P., Sen, S., & Chakraborty, S. (2025) · doi:10.1109/comsnets63942.2025.10885589 · Cited by 2 · see citing works
“Observability and efficiency improvement of large-scale distributed systems (such as Serverless platforms [1], [2], hyperscalers [3], content delivery networks (CDNs) [4], etc.) has gained a lot of attention in recent literature [5]–[7].”
A Unified Framework for Anomaly Detection and Root Cause Analysis in Microservice Systems
Meyer, O. G. J., Johnson, E., & Brown, J. (2025) · Jisuanji shenghuojia. · doi:10.54097/1gw77589 · Cited by 2 · see citing works
“However, the very features that make microservices attractive— distribution, independence, and high interaction—also introduce substantial challenges in system monitoring, fault diagnosis, and operational observability [4].”
Efficient, Scalable, and Secure Network Monitoring Platform: Self-Contained Solution for Future SMEs
Tonge, A. S., Baniya, B. K., & Deepak, G. C. (2025) · Network · doi:10.3390/network5030036 · Cited by 2 · see citing works
“This has created new challenges in network monitoring where existing tools are unable to handle the rate of data ingress or polling, and either fail or run behind, resulting in diminished network state monitoring [8].”
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 · see citing works
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 · see citing works
“, 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 · see citing works
“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 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
“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 · see citing works
“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 · see citing works
“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 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
Analysis of Revolutionizing IoT Solutions through Cloud Computing Integration
Abbas, S. I., & Walia, R. (2024) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl61749.2024.00012 · Cited by 1 · see citing works
Fog-Based IoP Application Monitoring and Failure Detection
Boukhali, Y., Kabbaj, M. N., & Benbrahim, M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-68660-3_15 · Cited by 1 · see citing works
COMPREHENSIVE APPROACH TO PROTECTING DATA AND THE INFORMATION SYSTEM INTEGRITY
Dzelendzyak, U., & Mashtaler, N. (2024) · Measuring Equipment and Metrology · doi:10.23939/istcmtm2024.03.047 · Cited by 1 · see citing works
Bryzgalov, A. A. (2024) · Open Education · doi:10.21686/10.21686/1818-4243-2024-6-53-66 · Cited by 1 · see citing works
Evaluating the Performance of Sidecar-based and Sidecarless Cloud-native Service Mesh Solutions
Hasanth, K. M., & Nadig, D. (2024) · IEEE International Conference on Advanced Networks and Telecommunications Systems · doi:10.1109/ants63515.2024.10897788 · Cited by 1 · see citing works
“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 · see citing works
“[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].”
- 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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