Bibliographic record
Abstract
An adaptive traffic estimation algorithm for an Autonomic Future Internet which can provide improved throughput, energy-efficiency and QoS guarantees is proposed. The theory for a Future Internet which supports multiple service classes, i.e., the traditional Best-Effort (BE) class and a new Essentially-Perfect-QoS (QoS) class, has recently been proposed. In this Future Internet, the routers give preference to the QoS traffic class. All smoothened end-to-end traffic flows in the QoS class will never experience congestion and can achieve Essentially Perfect link-utilizations and end-to-end QoS guarantees, with significantly improved energy-efficiencies. Each Future Internet router must provision bandwidth for the QoS traffic class, and schedule this class with 100% throughput efficiency and strict QoS guarantees, using a recently-proposed mathematical scheduling algorithm. In this paper, adaptive traffic estimation algorithms which allow each Future Internet router to estimate its future QoS traffic demands and provision bandwidth for the QoS demands in anticipation of their arrival are proposed. An Autonomic Controller (AC) in each router maintains a history of its QoS and Best-Effort traffic demands over a long time horizon. Several variations of Autoregressive Integrated Moving Average (ARIMA) filters are used to estimate the future traffic demands. The AC can then provision resources for the QoS demands in anticipation of their arrival. To test the algorithms, real traffic measurements taken every 15 minutes over 4 months for a European backbone network are used. The estimates are shown to be very accurate, provisioning bandwidth for the QoS class with success rates between 93% ... 99%. An Autonomic Controller (AC) in each router can also be used to automate the bandwidth provisioning process for existing Differentiated-Services traffic classes in existing Best-Effort Internet routers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".