NIMA (Network Impact Modeling and Analysis): A QoS Perspective
Bibliographic record
Abstract
The mammoth Internet traffic growth puts a significant load on Internet Service Provider's (ISP) network capacity which impacts a wide range of network Quality of Service (QoS) metrics such as latency, jitter, throughput, packet loss, link utilization, load balancing, and service availability. From the ISP's perspective, understanding the possible impact of the future Internet traffic on its network QoS is critical for provisioning their network capacity in a cost-effective manner while meeting Service Level Agreement (SLA) between an operator and its customers. To achieve the above goal, one needs a mechanism that is capable of taking input from the projected future traffic, assign the forecasted traffic over the end-to-end network, and then analyses the impact on the network's QoS status. In this paper, we developed a novel network planning framework, namely Network Impact Modeling and Analysis (NIMA) that uses novel methods and techniques to alert ISP's network capacity planners on the: (a) links that are subject to high-risk in terms of congestion; (b) impact on latency, jitter, packet loss, and throughput; (c) impact on load balancing; and finally suggests an optimal routing strategy that can improve the overall network health. For simulation purposes, we used Mininet in combination with a floodlight controller for implementation. The experiments are performed on different sized mesh topologies to test the effectiveness of our proposed framework.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".