ESSO: An Energy Smart Service Function Chain Orchestrator
Bibliographic record
Abstract
The rapid development of technologies such as photo-intensive social networks, on-demand video streaming, online gaming, and the Internet of Things (IoT) is causing a tremendous growth of traffic volume. Such large-scale expansion is leading to higher energy consumption and carbon footprint for the telecommunication industry. Governments are trying to minimize the environmental impact by introducing regulations and taxes; driving companies to use renewable energy. However, renewable energy is still not as cost-effective compared to traditional sources of energy (i.e., brown energy), and their availability varies significantly across time and geographic locations. Therefore, it is a challenge for telecommunication companies to comply with regulations and minimize carbon footprint without significantly increasing their operational cost. In this context, we propose an Energy Smart Service Function Chain Orchestrator called ESSO. ESSO reduces the overall carbon footprint of a telecommunication network by opportunistically adapting Service Function Chain (SFC) locations to utilize more energy at locations with surplus renewable energy. ESSO minimizes brown energy consumption by migrating SFCs across different locations. In addition, ESSO provisions SFC components in a manner that allows switches, switch ports, and servers to be put into low-power consumption state. Our trace-driven simulations on real ISP topologies show that considering the availability of renewable energy sources during SFC embedding even for a small-scale network can result in 2-3× reduction in carbon footprint.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".