A MapReduce Approach for Traffic Matrix Estimation in SDN
Bibliographic record
Abstract
A traffic matrix (TM) is a source of critical traffic throughput information for traffic engineering activities and network management tasks such as traffic prediction, capacity planning, network provisioning, and anomaly detection. However, estimating TM poses several challenges for network engineers. One of the challenges is that traffic data statistics are constantly changing, and their aggregation for real-time monitoring becomes a difficult task. This paper presents a near real-time TM estimation approach for OpenFlow (OF) networks. It makes use of Big Data techniques based on MapReduce operations to tackle the aggregation problem. The proposed method uses traffic data statistics collected from OF switches through an SDN controller as input and aggregates these data in a Big Data streaming processing environment. This paper explores the benefits of the distributed MapReduce computing model to provide an estimate of the TM for all origin-destination (OD) pairs of hosts in the network in two ways: 1) the accumulated throughput and 2) the throughput between two sequential TM estimates. This procedure enables network engineers to monitor the behavior and evolution of the throughput on each OD pair in the network and on each link in the path between each OD pair. The generated TM is persisted in a NoSQL database and can be made available for a variety of network traffic monitoring applications. The results of the simulations show the potential of the proposed MapReduce approach for TM estimation.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".