A Black-box Monitoring Approach to Measure Microservices Runtime Performance
Abstract
Domain fit: Niche / domain-specific · No strong AI-core implementation/artifact signals were detected from current providers.
Microservices changed cloud computing by moving the applications’ complexity from one monolithic executable to thousands of network interactions between small components. Given the increasing deployment sizes, the architectural exploitation challenges, and the impact on data-centers’ power consumption, we need to efficiently track this complexity. Within this article, we propose a black-box monitoring approach to track microservices at scale, focusing on architectural metrics, power consumption, application performance, and network performance. The proposed approach is transparent w.r.t. the monitored applications, generates less overhead w.r.t. black-box approaches available in the state-of-the-art, and provides fine-grain accurate metrics.
Results and benchmarks
Microservices changed cloud computing by moving the applications’ complexity from one monolithic executable to thousands of network interactions between small components.
Benchmark evidence is limited
Evidence graph: 2 refs, 1 links.
Utility signals: depth 65/100, grounding 58/100, status medium.
Implementation
No direct implementation yet
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- Start from related paper: MicroCM: A cloud monitoring architecture for microservice invocation.
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Time to first repro: a few days
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Reproduction readiness
No repo
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Hardware requirements
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Validation caveat
Hugging Face artifacts
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Research context
43
Citations
21
References
Tasks
Microservices, Computer science, Executable, Black box, Overhead (engineering), Software deployment, Cloud computing, Distributed computing
Methods
None detected
Domains
Power (physics)
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