Designing an Energy-Efficient Cloud Network [Invited]
Bibliographic record
Abstract
Cloud computing services are mainly hosted in remote data centers (DCs) where high performance servers and high capacity storage systems are located. Moving the services to distant servers can help handling the energy bottleneck of the information and communication technologies by leading to significant power savings at the local computing resources, which on the other hand increases the energy consumption of the transport network and the DCs. In this paper, we propose mixed-integer-linear-programming- (MILP-) based provisioning models to guarantee either minimum delayed or maximum power-saving cloud services where high performance DCs are assumed to be located at the core nodes of an IP-over-wavelength division multiplexing network. We further propose heuristics, namely, delay-minimized provisioning and power-minimized provisioning, each of which mimics the behavior of the benchmark MILP formulation. Through numerical results, we show that power savings can be attained at the expense of increased propagation delays. Hence, we finally propose the delay- and power-minimized provisioning (DePoMiP), which aims to minimize the propagation delay, maximize the power savings in the transport network and minimize the power consumption overhead introduced to the DCs. Simulation results verify that DePoMiP achieves low-delay and low-power provisioning in an environment which is dominated by the cloud services.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".