Energy aware anycast routing in optical networks for cloud computing applications
Bibliographic record
Abstract
Optical networks are emerging as attractive candidates capable of meeting the computing, storage and highspeed data transfer needs of current and future cloud based applications. 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, it is becoming increasingly important to consider the energy overhead incurred in the process of transmitting large amounts of data over the network. Energy aware design techniques for optical networks, which is expected to be the fundamental infrastructure for cloud computing, should be developed to reduce the power requirement for these core networks. In cloud based systems, a request can often be serviced at one of several possible destination nodes. This is known as anycasting, and in this paper we propose a new approach for energy aware resource allocation in optical networks that exploits the inherent flexibility of anycasting. We consider dynamic lightpath allocation and present a new integer linear program (ILP) formulation that selects the destination node and performs routing and wavelength assignment (RWA) in an integrated manner to minimize the overall energy consumption. Simulation results clearly demonstrate that properly exploiting the anycast principle can lead to significant energy savings, not only compared to traditional energy-unaware RWA techniques but also over energy-aware unicast methods.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".