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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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Cites
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×
“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×
“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
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×
“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
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
X-trace: a pervasive network tracing framework
Fonseca, R., Porter, G., Katz, R. H., Shenker, S. J., & Stoica, I. G. (2007) · Cited by 438
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×
“To address centralized cloud computing constraints, research interests are shifting towards distributed paradigms [19].”
BackgroundGannon, D. B., Barga, R., & Sundaresan, N. (2017) · IEEE Cloud Computing · doi:10.1109/mcc.2017.4250939 · Cited by 260 · cited 1×
“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.”
Hurwitz, J., Bloor, R., Kaufman, M., & Halper, F. (2009) · Cited by 241
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×
“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].”
BackgroundSite Reliability Engineering: How Google Runs Production Systems
Beyer, B., Jones, C., Petoff, J. L. M., & Murphy, N. R. (2016) · Cited by 206
Gerhards, R. (2009) · doi:10.17487/rfc5424 · Cited by 192
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×
“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
Architecting Microservices: Practical Opportunities and Challenges
Baškarada, S., Nguyen, V., & Koronios, A. (2018) · Journal of Computer Information Systems · doi:10.1080/08874417.2018.1520056 · Cited by 135 · cited 2×
“Furthermore, considerable developments have occurred since then [2], [14], [15], and several aspects of distributed IT systems and application patterns have revamped.”
BackgroundFrom 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×
“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].”
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×
“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×
“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×
“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 74 · cited 1×
“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×
“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
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×
“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×
“[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×
“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×
“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×
“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×
“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×
“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×
“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×
“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×
“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×
“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×
“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×
“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×
“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×
“…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×
“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×
“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×
“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×
“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
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×
“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×
“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×
“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×
“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
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×
“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×
“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×
“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×
“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×
“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×
“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×
“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×
“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×
“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×
“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
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
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×
“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
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×
“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×
“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
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×
“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
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×
“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×
“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
McCollam, R. (2022) · Apress eBooks · doi:10.1007/978-1-4842-8309-7_1 · Cited by 3
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×
“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
Sahay, R. (2020) · Apress eBooks · doi:10.1007/978-1-4842-6200-9_5 · Cited by 1
(2022) · Prometheus · doi:10.13169/prometheus.38.2.0001 · Cited by 1
Flagged first, then by citations
Research works printed but not linked: 31
- Biehl, 2016, RESTful API Design: APIs Your Consumers Will Love
- Ewaschuk, 2016, Monitoring Distributed Systems
- Kumar, 2020, White Box Monitoring and Black Box Monitoring explained
- Chhetri, CL-SLAM: Cross-layer SLA monitoring framework for cloud service-based applications, 30
- 2022, Monasca—OpenStack
- 2022, Instana—Enterprise Observability and APM for Cloud-Native Applications
- 2022, What is Splunk?
- 2022, Blazing-Fast Log Management and Observability for Modern Apps
- 2022, NetBeez Network Monitoring From the User Perspective
- 2022, Logstash: Collect, Parse, Transform Logs
- 2022, Introduction to Cilium & Hubble—Cilium 1.10.3 Documentation
- 2022, Apache SkyWalking
- 2022, OpenTelemetry
- 2022, Consul by HashiCorp
- 2022, Jaeger: Open Source, End-to-End Distributed Tracing
- 2022, The Leader in Cloud Monitoring
- 2022, Cloud Monitoring as a Service Bar Datadog 0400
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Cited by
Sorted by citations
Klieštik, T., Nica, E., Ďurana, P., & Popescu, G. H. (2023) · Oeconomia Copernicana · doi:10.24136/oc.2023.033 · Cited by 139
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 123
“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
“…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
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
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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