Energy-Efficient Cloud Services over Wavelength-Routed Optical Transport 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
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.
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