Optimal Reconfiguration of the Cloud Network for Maximum Energy Savings
Bibliographic record
Abstract
With the advent of cloud computing, storage and computing functions are migrating to remote resources such as virtual servers and storage systems which are mostly hosted in the data centers. This migration can ensure significant energy savings as utilization of local resources contribute to 40% of the Greenhouse Gas emissions of the Information and Communication Technologies (ICTs). On the other hand, provisioning of the cloud services needs to be handled carefully since energy consumption of the transport network, as well as the energy consumed by the data centers, is expected to increase. We revisit our previously proposed Mixed Integer Linear Programming (MILP) models that are used to reconfigure the cloud network design with look-ahead demand profile. Due to long runtimes of the MILP models in large-scale scenarios, in this paper, we propose two heuristics to reconfigure the cloud network for provisioning the cloud and Internet computing demands. The first heuristic aims to minimize the propagation delay while the second one targets minimizing the power consumption of the data centers and the transport network. We verify the heuristics through simulations where MILP models are used as the benchmarks. Numerical results show that power minimized provisioning can guarantee significant energy savings in the cloud network with less resource consumption. We also present the energy versus delay trade-off and point out possible solutions.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".