Green networking strategies for distributed computing
Bibliographic record
Abstract
The high bandwidth and speed of wavelength division multiplexing (WDM) optical networks are allowing efficient use of distributed computing and storage resources that would not have been feasible earlier. There has been much focus, in recent years, on the development of “green” techniques that reduce the energy consumption of the computing and storage facilities at the network nodes. However, a significant amount of energy overhead is also incurred in the process of transmitting large amounts of data over the network. This includes energy consumption of various network components such as routers, switches, in-line amplifiers and transponders. In this paper, we focus on energy minimization techniques for high-speed communication over an optical network. Given a set of communication requests, along with the bandwidth needs, and specified start and end times for each request, we propose a new approach that exploits knowledge of demand holding times to intelligently share resources among non-overlapping demands and reduce the overall power consumption of the network. We first present a simple shortest path heuristic that significantly outperforms traditional holding time unaware (HTU) approaches. We then present a novel Genetic Algorithm (GA) based strategy that jointly minimizes both power consumption and transceiver cost for the network and achieves additional energy savings, even compared to our first approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".