A Dynamic Bandwidth Prediction and Provisioning Scheme in Cloud Networks
Bibliographic record
Abstract
Effective resource provisioning for collocated cloud applications each with unique traffic patterns is a challenging task. Furthermore, resource requirements cannot be adequately determined a priori to deployment while traffic generated and received during application communication lifecycles is subject to fluctuations that exacerbate provisioning. Given this volatility, we analyzed traces from production cloud infrastructure mindful of the nonstationarity that presents challenges to Quality of Service (QoS) provisioning. Henceforth we developed a forecasting model that enables current predictive solutions widely applied in the cloud to adapt to a range of diverse operating conditions often observable as time-of-day load surges, traffic bursts and flash crowds resulting from sudden website popularity. We foster robustness in current methods by augmentation with a class of estimators adaptable to traffic volatility while providing a tradeoff between complexity and performance. The developed forecasting model avails itself of the Auto-Regressive Integrated Moving Average (ARIMA) model enhanced by a general class of Adaptive Conditional Score Models (ACS). The latter is able to adapt efficiently to load fluctuations observed variously as time-of-day traffic surges, flash-crowds and DoS episodes. The model offers between 10 & 15 % improved accuracy over existing models. We have realized a novel algorithm which has been adapted as a rate based solution applicable at integral points in the cloud network. The analysis and experimentation with our methods suggest a 25% reduction in Average Flow Completion Time (AFCT) when compared with current rate based methods of XCP & RCP while offering a 60% reduction in comparison to TCP.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".