Local Regression Based Box-Cox Transformations for Resource Management in Cloud Networks
Bibliographic record
Abstract
Understanding and implementing approaches to efficiently manage the infrastructure resources of cloud data centers has become essential. Energy consumption and disorganized resource usage can expensively produce an impressive increase in the operational cost of cloud services. This increase turns to a remarkable rise in the cloud customers' invoices. Providing an exceptional quality of service running on well-organized resources with efficient energy is a critical issue that needs to be carefully considered by both industrial and academics. Although, the cloud providers are trying to deliver sufficient quality of services to their customers with a comparatively proper cost. One of the effective techniques to address these issues in cloud data centers is a dynamic virtual machine consolidation. This technique intends to improve energy efficiency and resource utilization by reallocating multiple virtual machines including various workload among available hosts and turning the unutilized hosts to an ideal state. However, consolidating the virtual machines due to fluctuating workload in cloud application can cause a violation in service level agreement. In this paper, we propose a host overload detection algorithm based on the Box-Cox transformations and the local regression model to predict overloaded hosts. This algorithm transforms the historical data of the host workload by using the Box-Cox transformations technique, and it also applies the local regression to predict the future state of the selected host. The experiments and simulation results based on dynamic workloads show the proposed algorithm outperforms the other competitive host overload detection algorithms.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".