The right tool for the job: Switching data centre management strategies at runtime
Bibliographic record
Abstract
Applications are shifting into large scale, virtualized data centres that provide resources on a pay-per-usage basis. Data centres must minimize resource consumption while providing enough resources to meet application requirements. To meet highly variable application demands, a dynamic approach to virtual machine (VM) management is required. This involves three basic management operations: (i) placing newly arrived VMs, (ii) migrating (moving) VMs off of highly utilized machines to avoid performance degradation, and (iii) migrating VMs off of underutilized machines so that they may be shut down to save power. We define a management strategy to consist of a set of policies that guide these three operations. We consider the goals of minimizing Service Level Agreement violations and minimizing power consumption. Developing a management strategy to achieve both of these goals is challenging, as the goals are often in conflict. We propose achieving both goals through dynamically switching between two management strategies, each with a single goal, depending on current data centre state. We propose three methods of dynamically switching strategies, and evaluate these methods through simulation. Dynamic strategy switching offers improved results over a single management strategy.
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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 | 0.006 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".