QoS-aware Energy Saving Scheme and Traffic Management in Mobile Edge Computing Networks
Bibliographic record
Abstract
In this paper, an energy saving scheme is presented whereby unused virtual machines (VMs) in distributed edge devices enter a sleep mode taking into account the quality of service (QoS) experienced by mobile users. The virtual machines in the system under consideration form a shared VM pool assisted by the software-defined networking (SDN) technology. The energy saving optimization problem aims to minimize the number of active VMs without violating the QoS requirements, and is solved using the square-root staffing rule and the Halfin-Whitt function. In addition, a traffic management strategy in overloaded edge devices is carried out to maximize the number of accommodated users taking into account the fronthaul capacity which impacts task migration among edge devices. Results show the effectiveness of the proposed scheme in saving remarkable amount of energy while satisfying the QoS requirements. It is also shown that the VM sleeping scheme exhibits higher energy saving and less task migration compared to the entire edge device sleeping scheme due to its enhanced flexibility.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".