A Black-box Monitoring Approach to Measure Microservices Runtime Performance

Q: How reproducible is "A Black-box Monitoring Approach to Measure Microservices Runtime Performance"?

Estimated time to first reproduction: a few days. Risk flags: No repository-level reproducibility signals are currently available, Estimate is based on paper-only reproduction flow. No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.

Rolando Brondolin, Marco D. Santambrogio

Published: Nov 10, 2020

No direct implementation yet

Evidence: Inferred

Domain fit: Niche / domain-specific

Verified repos: 0

No strong AI-core implementation/artifact signals were detected from current providers.

Time to first repro: a few days

2 risk flags

DOI Publisher

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 tra ...

Read full abstract

ck 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.

Technical details

Canonical key: doi-10.1145_3418899

Cache status: Fresh

Generated at: Jun 20, 2026, 10:42 AM

Artifact coverage: sparse

HF provider: ok (mixed)

PWC source used: No

LLM status: not_generated

LLM model: n/a

LLM generated: Unknown

LLM content type: n/a

HF policy: hf-relevance-v27

context only

Benchmarks: missing

Time to repro: a few days

2 risk flags

Results & Benchmarks

Freshness tier: cold

Direct + Inferred Evidence

No concrete benchmark grounding is available yet. Treat the page as context or an implementation starting point only.

Microservices changed cloud computing by moving the applications’ complexity from one monolithic executable to thousands of network interactions between small components.

Implementation Evidence Summary

Confidence: low

Recommendation evidence is currently too limited for a maintained-repo choice. Use Implementation Status and Reproduction Path for a practical baseline plan.

Reproduction Risks

Estimate is based on paper-only reproduction flow

Hardware Notes

Expect multi-day setup/compute for meaningful reproduction based on current guidance.

Evidence disclosure

Evidence graph: 2 refs, 1 links.

Utility signals: depth 65/100, grounding 58/100, status medium.

Implementation Status

No verified maintained repo

There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.

No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.
Start from related paper: Research of Microservices Features in Information Systems Using Spring Boot.
Track assumptions and missing details in an experiment log before coding.

Time to first repro: a few days

Reproduction readiness

No Repo

Time to first repro: days

Last checked: Jun 20, 2026

Hardware requirements

Expect multi-day setup/compute for meaningful reproduction based on current guidance.

No verified implementation available

· No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.

No benchmark numbers could be verified. You will not be able to validate reproduction correctness against published numbers.

Hugging Face artifacts

No trustworthy direct or curated related Hugging Face artifacts were found yet.

Continue with targeted Hugging Face searches derived from the paper title and method context:

Models

Microservices Power (physics) Microservices model

Datasets

Microservices dataset

Spaces

Microservices demo

Tip: start with models, then check datasets/spaces if you need evaluation data or demos.

Direct artifact matches are currently sparse. Use targeted Hugging Face searches to quickly locate candidate models, datasets, and demos.

Search models Search datasets Search spaces

Research context

Citations

References

Tasks

Microservices, Computer science, Executable, Black box, Overhead (engineering), Software deployment, Cloud computing, Distributed computing

Methods

None detected

Domains

Power (physics)

Evaluation & Human Feedback Data

Open this paper in HFEPX to review benchmark signals, evaluation modes, and human-feedback protocol context.

Open in HFEPX

Explore Similar Papers

Jump to Paper2Code search queries derived from this paper's research context.

Microservices Computer science Executable Black box Overhead (engineering) Software deployment