Bibliographic record
Abstract
In recent years the advances in cloud computing and virtualization have created the need for autonomic resource provision. The correct provisioning of resources is a difficult task due to variations and uncertainty in workload demands. Most data center workload demands are very spiky in nature and often vary significantly during the course of a single day. Because the resource availability in a data center is generally unpredictable due to the shared feature of the cloud resources and because of the stochastic nature of the workload, severe service level agreement (SLA) violations may occur frequently. To overcome this problem, researcher's attention is diverted towards developing dynamic resource management strategies. In this paper, an autonomic resource controller is proposed that dynamically controls the resource allocation for data center's virtual containers. The controller has two parts: A resource modeler that models the non-linearity of the system by employing different Machine Learning techniques allowing the datacenter to allocate the appropriate resources and a resource fuzzy tuner that dynamically tunes the allocated resources using fuzzy logic to sustain the desired performance taking into consideration the enforcing of service differentiation among clients. Experimental results on a real data center dataset showed that the proposed resource controller can predict future resource needs while still sustaining performance goals stated in the SLA. Also, using the bagging and the boosting techniques along with model tree classifiers was demonstrated to be promising in terms of accuracy and performance.
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) | 0.000 | 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".