Multivariate outlier filtering for A-NFVLearn: an Advanced Deep VNF Resource Usage Forecasting Technique
Bibliographic record
Abstract
Abstract Virtual Network Function Resource Adaptation (VNF-RA) aims at adequately adapting Network Function Virtualization Infrastructure (NFVI) resources according to the geographical fluctuation of user demand by maximizing the quality of service (QoS) of the offered services and the energy consumption of the NFVI while limiting the risks of Service Level Agreement (SLA) breaches, the CAPEX and OPEX of the cloud operators and their customers. Virtual Network Function (VNF) resource usage forecasting leads therefore a key role in enabling proactive resource adaptation in dynamic Network Function Virtualization (NFV) environments whose resource demand constantly changes. In parallel, Long Short-Term Memory (LSTM)-based prediction has garnered huge interest in the research community and several research teams have therefore proposed different VNF resource usage prediction algorithms based on this machine learning technique. However, current LSTM-based VNF resource usage forecasting techniques lack the flexibility to take several resource attributes of different scales, over many time steps, in order to predict several other resource attributes over many time steps from a Service Function Chain (SFC). In this paper, we push the state of the art forward by presenting A-NFVLearn, a flexible multivariate, LSTM-based model with an attention mechanism which uses different attributes of resource load history (CPU, memory, I/O bandwidth) from various VNFs of an SFC to forecast future load of multiple resources of a VNF. Next, we propose a multivariate outlier filtering scheme at pre-processing based on Adjusted Outlyingness (AO), which improves training time performance of LSTM-based models without impacting prediction accuracy.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".