On Energy-Efficient Offloading in Mobile Cloud for Real-Time Video Applications
Bibliographic record
Abstract
Batteries of modern mobile devices remain severely limited in capacity, which makes energy consumption a key concern for mobile applications, particularly for the computation-intensive video applications. Mobile devices can save energy by offloading computation tasks to the cloud, yet the energy gain must exceed the additional communication cost for cloud migration to be beneficial. The situation is further complicated by real-time video applications that have stringent delay and bandwidth constraints. In this paper, we closely examine the performance and energy efficiency of representative mobile cloud applications under dynamic wireless network channels and state-of-the-art mobile platforms. We identify the unique challenges of and opportunities for offloading real-time video applications and develop a generic model for energy-efficient computation offloading accordingly in this context. We propose a scheduling algorithm that makes adaptive offloading decisions in fine granularity in dynamic wireless network conditions and verify its effectiveness through trace-driven simulations. We further present case studies with advanced mobile platforms and practical applications to demonstrate the superiority of our solution and the substantial gain of our approach over baseline approaches.
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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) | 0.001 | 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) | 0.001 | 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".