Microservices Upgrade in Clouds: Dynamic Management of Version Dependencies and User Load
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
In today's cloud computing environments, where scalability, agility, and resiliency are paramount, microservices architecture stands out as a fundamental keystone of modern software development. While microservices are designed as independent components communicating through well-defined APIs, maintaining and upgrading them pose unique challenges, including version compatibility, dependency management, and service continuity. These challenges become intricate when multiple instances of a specific microservice are deployed, utilizing load balancing to distribute users and offering different functionalities simultaneously. This paper proposes a heuristic algorithm to address the microservices upgrading problem. The proposed algorithm migrates users gradually and effectively by managing version dependencies, user load, considering propagation impact, multiple instances, and resource constraints. The simulation results demonstrate the superiority of our algorithm over existing benchmarks in terms of resource usage cost and the number of new version instances.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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