Energy/Quality-of-Experience TradeOff of Power Saving Modes for Voice Over IP Services
Bibliographic record
Abstract
Deploying a power saving mode (PSM) is considered an effective solution for saving energy; however, a key challenge is how to simultaneously save energy and support a required level of quality of experience (QoE) using PSM for voice over IP (VoIP) services. This paper aims at developing a novel Markovian-based evaluation framework that assesses the energy consumption and QoE, achieved by a fixed sleeping time-based PSM. Based on this framework, a novel QoE-aware PSM is developed, which exploits the ON-OFF characteristics of a VoIP session to further improve the energy saving, while accounting for the effect of bursty losses on the QoE. The QoE and energy consumption achieved by the proposed PSM are evaluated via solving a system of linear equations. This facilitates the analytical formulation of the QoE/energy tradeoff that is needed for the successful integration of this QoE-aware PSM within the traffic offloading mechanisms. Numerical results validate the accuracy of the performance evaluation framework and verify the efficacy of the proposed QoE-aware PSM in saving energy while assuring a desired level of QoE. The Pareto-optimal operational regime show that the proposed QoE-aware PSM significantly reduces power consumption at a desired level of QoE compared with its counterparts.
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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 | 0.003 |
| 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 | 0.001 |
| 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".