Rolling Upgrade with Dynamic Batch Size for IaaS Cloud
Bibliographic record
Abstract
Cloud systems are upgraded regularly to improve their performance, fix bugs, deploy new software versions, etc. Their hosted services, for example, telecommunication services, may have stringent non-functional requirements such that they do not tolerate more than five minutes of downtime in a year regardless whether they are provided by a cloud system or the outage is due to an upgrade. Such services must remain highly available under any circumstance, which imposes availability requirements on the provider cloud system. The dynamicity of the environment is one of the main challenges for maintaining High Availability (HA) in cloud deployments during upgrades. To maintain availability during upgrades, most cloud providers use rolling upgrade, and to avoid any unexpected interference between the upgrade process and the cloud's scaling mechanism, scaling is disabled for the time of the upgrade. In this paper, we propose a novel approach for rolling upgrades applicable to - among others - IaaS cloud systems to address HA. This approach mitigates the interference between the upgrade process, any failure handling and scaling by dynamically adjusting the upgrade process to the changes in the cloud environment. Accordingly, the upgrade process can start/resume only when the system has sufficient resources to perform an upgrade iteration and suspends the process when this is not the case. As a result, scaling does not need to be disabled during upgrades, rather the scaling operations regulate the pace of the upgrade.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".