Monitoring Probe Deployment Patterns for Cloud-Native Applications: Definition and Empirical Assessment

Abstract

Monitoring is a key feature to enhance systems with the capability to anticipate, detect, predict, and mitigate failures, while providing Quality of Service (QoS) monitoring and Service Level Agreements (SLAs) guarantee. Monitoring frameworks can serve these purposes by deploying probes according to many possible patterns that have different features, for instance in terms of efficiency and privacy. So far, these probe deployment patterns have not been systematically defined, analyzed and assessed. Thus, engineers who design and configure their monitoring systems have to take decisions only based on partial knowledge and personal experience. This article addresses this knowledge gap, by presenting a systematic analysis of 11 probe deployment patterns, their known uses, and implementations. We assess these patterns qualitatively, and quantitatively using both VMs and containers. Results show the targets have negligible resource consumption (e.g., less than 1% CPU usage), while the probe holder consumption is mainly significant in relation to memory consumption, reaching up to 10 GiB in our experiments. Our findings suggest that reusing probes and holders among users can generally enhance efficiency and scalability when direct access to the monitored target is not an option. We generate a set of best practices that can assist engineers in configuring their monitoring systems. Finally, we showcase the application of certain patterns through three practical usage scenarios, which feature diverse technologies and requirements.