On Base Station Sleeping for Heterogeneous Cloud-Fog Computing Networks
Bibliographic record
Abstract
In this paper, a base station sleeping mechanism is proposed for cloud-fog computing networks. Motivated by the capability of heterogeneous cloud radio access networks (H-CRANs) to efficiently control all network nodes, we aim to minimize the power consumption of small base stations (SBSs) taking into account the delay incurred by tasks offloaded from sleeping SBSs to the central cloud. In the proposed model, computing tasks can be processed either at the network edge (i.e., SBS) or at the central cloud. All computing and non-computing tasks of sleeping SBSs are offloaded to the macro base station (MBS), while computing tasks are further offloaded to the cloud. The probabilities of queueing in the MBS and the cloud given that a particular SBS is sleeping are calculated prior to making the decision of SBS sleeping. Therefore, according to whether more power saving or less delay is preferred, SBSs that impose less queueing probability on the MBS or cloud are forced to enter the sleep mode, respectively. Results show that taking computing tasks into consideration in SBS sleeping can reduce the computing response time at the cloud.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".