MétaCan
Menu
Back to cohort
Record W4362490011 · doi:10.1145/3578245.3585030

Transparent Trace Annotation for Performance Debugging in Microservice-oriented Systems (Work In Progress Paper)

2023· article· en· W4362490011 on OpenAlex
Adel Belkhiri, Ahmad Shahnejat Bushehri, Felipe Göhring de Magalhães, Gabriela Nicolescu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware System Performance and Reliability
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsDebuggingTRACE (psycholinguistics)Computer scienceAnnotationWork (physics)Software engineeringProgramming languageArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Microservices is a cloud-native architecture in which a single application is implemented as a collection of small, independent, and loosely-coupled services. This architecture is gaining popularity in the industry as it promises to make applications more scalable and easier to develop and deploy. Nonetheless, adopting this architecture in practice has raised many concerns, particularly regarding the difficulty of diagnosing performance bugs and explaining abnormal software behaviour. Fortunately, many tools based on distributed tracing were proposed to achieve observability in microservice-oriented systems and address these concerns (e.g., Jaeger). Distributed tracing is a method for tracking user requests as they flow between services. While these tools can identify slow services and detect latency-related problems, they mostly fail to pinpoint the root causes of these issues.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.271
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it