Maximization of Value of Service for Mobile Collaborative Computing Through Situation-Aware Task Offloading
Bibliographic record
Abstract
Mobile collaborative computing (MCC) is an emerging platform for effectively improving the quality of mobile service by exploiting the idling computational resources in distributed mobile devices (MDs) through peer-to-peer task offloading. Recently, diverse MCC applications have been developed to provide multiple functional benefits and individualized value to users. In this paper, we propose to use a new concept of value of service (VoS) to represent the total value of all tasks and devices with respect to their performance including latency and energy consumption. To improve service provisioning under fast-varying conditions, a situation-aware offloading scheme is proposed to maximize VoS by opportunistically leveraging the changing resource availability conditions. Specifically, we consider a collaborative computing system where a user can offload input data of computation to other available MDs. VoS maximization for two popular offloading scenarios, i.e., binary and partial offloading, are formulated separately. Decision making of binary offloading is an NP-hard problem and solved by a novel heuristic algorithm which achieves suboptimal solution in polynomial time. Partial offloading is formulated as a non-convex problem involving task partition decision. By exploiting the unique characteristics of the problem, we propose an adapted barrier method (ABM) which achieves significant improvements in convergence efficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".