A Distributed Coalition Game Approach to Femto-Cloud Formation
Bibliographic record
Abstract
This paper studies distributed formation of femto-clouds in a UMTS LTE network. Femtocell access points (FAPs) are equipped with computational resources. They share their resources with neighboring FAPs and form local clouds with the aim to avoid the remote cloud costs while improving the user quality of experience (QoE) in terms of handling latency. In exchange for sharing their excess resources, FAPs receive monetary incentives proportional to their contribution in performing computational tasks in the femto-cloud. The resource sharing problem is formulated as an optimization problem and a myopic procedure is presented that enables FAPs to collaboratively find its solution in a distributed fashion. In such an optimal femto-cloud structure, the local computational resources of FAPs are maximally exploited, yet the incentive earned by each femto-cloud is divided among the FAPs in a fair fashion. Numerical simulations using NS-3 verify superior QoE of users as well as higher incentives provided to FAP owners as compared with alternative heuristic schemes. Numerical results also show that the grand femto-cloud-the largest collaborative cloud comprising of all FAPs-is not always the optimal structure.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".