Cloud-Native Observability

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Network Virtualization and Edge/Fog Computing".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 5747

Special Issue Editors


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Department of Electrical Engineering and Computer Science, Lübeck University of Applied Sciences, 23562 Lübeck, Germany
Interests: cloud computing; service computing; microservices; serverless architectures; web-scale information systems; data science; machine learning; volunteer computing
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Special Issue Information

Dear Colleagues,

The ongoing progress in digitization and the internet of things (IoT) relies increasingly on deploying applications and services to cloud and edge infrastructures and platforms. This cloud-native focus applies equally to cloud- and edge components.

Compared to monolithic SOA-based systems, cloud-native software systems often have a much more decentralized structure and many independently deployable and (horizontally) scalable components, making it more complicated to create a shared and consolidated picture of the overall decentralized system state.

Furthermore, these systems are often developed in a DevOps-based and evolutionary design approach. Adaptations and further developments to these systems are thus carried out during the operations phase and often at the "open heart". Data-driven engineering approaches also use insights from telemetry data from the operations phase.

Nevertheless, while the current research covers the implementation of cloud-native applications and the corresponding DevOps implications, the observability of these kinds of systems is still treated somehow stepmotherly. For instance, historically, telemetry data were understood as the triad of logs, (distributed) traces, and metrics. Consequently, these kinds of data are often consolidated in specialized and isolated logging, tracing, or metric data-analysis stacks. Commonalities between these silos do exist, but they are often not used systematically and harmonized.

This Special Issue intends to address this research gap by looking for contributions in the form of systematic reviews, systematic mapping studies, product and framework reviews, benchmark performance-and-cost studies, solution proposals, case studies, and software engineering methodologies, including but not limited to:

  • Collection and analysis of telemetry data of cloud- and edge software and system components;
  • Consolidation of telemetry data of cloud- and edge components;
  • Unification of telemetry data (logs, traces, metrics) in cloud- and edge scenarios;
  • Impact of structured logging approaches on the unification and analysis of telemetry data;
  • Unification of telemetry data architectures (logs, traces, metrics) in cloud- and edge scenarios;
  • Black-box and white-box telemetry data acquisition;
  • Managed and self-hosted telemetry data consolidation stacks;
  • Telemetry data-driven SW-engineering methodologies;
  • Data-science methodologies for telemetry data;
  • Best practices for unified telemetry data collection in cloud- and edge scenarios;
  • Telemetry data collection in different industries and use cases (cloud, edge, IoT).

Prof. Dr. Nane Kratzke
Prof. Dr. Michael Sheng
Guest Editors

Manuscript Submission Information

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Keywords

  • cloud computing
  • edge computing
  • observability
  • cloud-native
  • telemetry data

Published Papers (2 papers)

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Research

21 pages, 3340 KiB  
Article
Cost-Profiling Microservice Applications Using an APM Stack
by Sjouke de Vries, Frank Blaauw and Vasilios Andrikopoulos
Future Internet 2023, 15(1), 37; https://doi.org/10.3390/fi15010037 - 13 Jan 2023
Viewed by 2141
Abstract
Understanding how the different parts of a cloud-native application contribute to its operating expenses is an important step towards optimizing this cost. However, with the adoption and rollout of microservice architectures, the gathering of the necessary data becomes much more involved and nuanced [...] Read more.
Understanding how the different parts of a cloud-native application contribute to its operating expenses is an important step towards optimizing this cost. However, with the adoption and rollout of microservice architectures, the gathering of the necessary data becomes much more involved and nuanced due to the distributed and heterogeneous nature of these architectures. Existing solutions for this purpose are either closed-source and proprietary or focus only on the infrastructural footprint of the applications. In response to that, in this work, we present a cost-profiling solution aimed at Kubernetes-based microservice applications, building on a popular open-source application performance monitoring (APM) stack. By means of a case study with a data engineering company, we demonstrate how our proposed solution can provide deeper insights into the cost profile of the various application components and drive informed decision-making in managing the deployment of the application. Full article
(This article belongs to the Special Issue Cloud-Native Observability)
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23 pages, 1368 KiB  
Article
Cloud-Native Observability: The Many-Faceted Benefits of Structured and Unified Logging—A Multi-Case Study
by Nane Kratzke
Future Internet 2022, 14(10), 274; https://doi.org/10.3390/fi14100274 - 26 Sep 2022
Cited by 4 | Viewed by 2849
Abstract
Background: Cloud-native software systems often have a much more decentralized structure and many independently deployable and (horizontally) scalable components, making it more complicated to create a shared and consolidated picture of the overall decentralized system state. Today, observability is often understood as a [...] Read more.
Background: Cloud-native software systems often have a much more decentralized structure and many independently deployable and (horizontally) scalable components, making it more complicated to create a shared and consolidated picture of the overall decentralized system state. Today, observability is often understood as a triad of collecting and processing metrics, distributed tracing data, and logging. The result is often a complex observability system composed of three stovepipes whose data are difficult to correlate. Objective: This study analyzes whether these three historically emerged observability stovepipes of logs, metrics and distributed traces could be handled in a more integrated way and with a more straightforward instrumentation approach. Method: This study applied an action research methodology used mainly in industry–academia collaboration and common in software engineering. The research design utilized iterative action research cycles, including one long-term use case. Results: This study presents a unified logging library for Python and a unified logging architecture that uses the structured logging approach. The evaluation shows that several thousand events per minute are easily processable. Conclusions: The results indicate that a unification of the current observability triad is possible without the necessity to develop utterly new toolchains. Full article
(This article belongs to the Special Issue Cloud-Native Observability)
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