Reviving Software Diversity in Microservices to Optimize the Performance of Software Systems
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.
Bibliographic record
Abstract
With the increasing popularity and complexity of microservice software systems, satisfying the performance requirements of these systems becomes a non-trivial task. While horizontal auto-scaling is a common remedy to this problem, it is not necessarily cost-effective and the proper solution for all scenarios. Also, regardless of the number of replicas, they are all prone to common bug failure. In this poster/demo, we present an agile and cost-effective approach for satisfying the performance requirements of microservice software systems without incurring extra costs on the service provider. We research how performance, i.e., response time, can be tamed by applying software diversity, aka multi-versioning, to the system's resource-heavy critical services. We use our open-source extension of the Docker framework, called DockerMV, to deploy microservice systems with multi-versioning embedded underneath. We also propose a dynamic load-balancing service that proactively adapts to the various versions depending on current and near-future performance needs. We demonstrate the efficacy of multi-versioning for satisfying the performance requirements of microservice software systems through extensive experiments on TeaStore, a microservice reference test application, and Znn, a containerized news portal. We will present our results through the poster and a live demonstration throughout the conference. A preliminary demo of our work can be accessed here<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>https://www.youtube.com/watch?v=oeMCxlDtU64. The GitHub repository of our work can be accessed here<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>https://github.com/prabjot09/nginx-dynamic-load-balancing.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it