4 On the Energy Cost of Robustness and Resiliency in IP Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the growing concern for the energy consumption of the Internet, green strategies for network and traffic management cannot undermine Quality of Service (QoS) and network survivability. In particular, two very important issues that may be affected by green networking techniques are resilience to node and link failures, and robustness to traffic variations.In this paper, we study how achieving different levels of resiliency and robustness impacts the network energy-aware efficiency. We propose novel optimization models to minimize the energy consumption of IP networks that explicitly guarantee network survivability to failures and robustness to traffic variations. Energy consumption is reduced by putting in sleep mode idle line cards and nodes according to traffic variations in different periods of the day. To guarantee network survivability we consider two different schemes, dedicated and shared protection, which assign a backup path to each traffic demand and some spare capacity on the links along the path. Robustness to traffic variations is provided by tuning the capacity margin on active links in order to accommodate load variations of different magnitude. Furthermore, we impose some inter-period constraints to guarantee network stability and preserve device lifetime. Both exact and heuristic methods are proposed.Experimentations carried out on realistic networks operated with flow-based routing protocols (like MPLS) allow us to quantitatively analyze the trade-off between energy cost and level of protection and robustness. Results show that significant savings, up to 30%, may be achieved even when both survivability and robustness are fully guaranteed, both with exact and heuristic approaches.
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.
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 | 0.000 |
| 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 it