Fair multi-resource allocation with external resource for mobile edge computing
Bibliographic record
Abstract
We consider the problem of fair multi-resource allocation for mobile edge computing (MEC). In MEC, to execute tasks with demands on multiple types of computing resources in the edge servers, the users must upload their tasks over a single dedicated wireless communication link that exists outside the servers. For this environment, we design a multi-resource allocation mechanism that extends the notion of dominant resource fairness (DRF) to accommodate an external resource, called DRF-ER. It provides several highly desirable properties. First, DRF-ER is envy-free, as no user prefers the allocation of another user. Second, DRF-ER allocations are Pareto optimal, as no one can improve its allocation without decreasing that of the others. Finally, DRF-ER is strategy-proof, as no user has an incentive to lie about its resource demand. Large-scale simulation driven by Google cluster traces further shows that DRF-ER significantly outperforms a naive extension of DRFH, which is a well-known variant of DRF for multiple servers, leading to higher resource utilization.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".