Energy-Efficient Cloud Services over Wavelength-Routed Optical Transport Networks
Bibliographic record
Abstract
Optical WDM networks can be employed as the transport medium technology for cloud computing services since they have high capacity and low delay, and they satisfy the service requirements by the help of the control plane. Recent research has shown that cloud services can be efficiently provisioned based on anycast or manycast paradigms. In this paper, we focus on the energy savings in the optical transport network which forms a communication infrastructure for the cloud services based on the manycast paradigm. We propose an optimization model to maximize the energy savings by putting the wavelength routing modules of the optical nodes in the power saving mode. Based on the optimization model, we propose an evolutionary algorithm, namely the Evolutionary Algorithm for Green Light-tree Establishment (EAGLE) which can provide lower runtime for large topologies and find a suboptimal solution. We evaluate the performance of our optimization model by running EAGLE under a topology lying on four different time zones, i.e., NSFNET. Simulation results verify that selecting a feasible number of nodes to put their wavelength routing modules in the power saving mode leads to significant energy savings in transportation of the cloud services over WDM networks. Furthermore, the proposed scheme does not introduce a resource consumption penalty when compared to the wavelength minimizing 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".