Energy-Efficient and Delay-Fair Mobile Computation Offloading
Bibliographic record
Abstract
Mobile edge computing has been regarded as a new paradigm to achieve mobile computation offloading (MCO), which enables resource-limited mobile devices (MDs) to offload part or all of computation-intensive applications to more powerful computing entities. In this work we study MCO for data partitioned oriented applications. This type of services are usually delay-elastic, but shorter processing delay helps improve the user experience. We consider energy consumption of MDs and latency fairness of the applications. The latency fairness is achieved by allowing multiple offloading MDs to share one channel and allocating a fraction of the channel time for each application, and minimizing total energy consumption of the MDs is achieved by jointly optimizing the offloading ratio, channel assignments, and channel time allocations. For a special case that assigns at most one MD to each channel, a closed-form offloading ratio is derived and optimal channel assignment is obtained through transforming the problem into a weighted bipartite matching. The general problem is then solved by embedding the branch-and-bound iterations with successive convex approximation that solves a series of geometric programming problems. To reduce the complexity, a two-layer recursive method is then proposed that finds the channel assignments, offloading ratio and channel time allocation for each MD. Simulation results demonstrate that the proposed method can achieve low energy consumption and high latency fairness; and much lower energy consumption can be achieved with a slight decrease in latency fairness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".