Optimizing application downtime through intelligent VM placement and migration in cloud data centers
Bibliographic record
Abstract
As cloud data centres grow in size and complexity, hosted applications become increasingly vulnerable to dynamically occurring infrastructure downtime periods caused by partial infrastructure failures. Downtimes within cloud data centres can be diverse, ranging from unplanned server/rack unit failures to compulsory server power-offs when addressing arbitrary environment conditions, e.g., thermal issues. For instance, in these environments, server racks are often a unit of failure due to either faulty rack switches or rack power units. We observe that the degree of application disruption depends on i) the application's fault tolerance, reconfiguration capabilities, and redundancy of VM components affected by the respective emergency shutdowns and ii) the support for VM migration of vulnerable application components within the constrained time window of impending shutdown of a failure unit. In this context, in this paper, we develop and evaluate techniques which aim to optimize the downtime of hosted applications during emergency shutdowns due to partial failures through two orthogonal approaches: i) designing VM placement techniques that are aware of application fail-over semantics and ii) prototyping intelligent schemes for live VM migration prioritization based on prediction models for both VM migration times and expected downtimes for applications.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".