Cloud-Native Observability

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

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