A Shapley-Value Mechanism for Bandwidth On Demand between Datacenters
Why this work is in the frame
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Bibliographic record
Abstract
Recent studies in cloud resource allocation and pricing have focused on computing and storage resources but not network bandwidth. Cloud users nowadays customarily deploy services across multiple geo-distributed datacenters, with significant inter-datacenter traffic generated, paid by cloud providers to ISPs. An effective bandwidth allocation and charging mechanism is needed between the cloud provider and the cloud users. Existing volume based static charging schemes lack market efficiency. This work presents the first dynamic pricing mechanism for inter-data-center on-demand bandwidth, via a Shapley value based auction. Our auction is expressive enough to accept bids as a flat bandwidth rate plus a time duration, or a data volume with a transfer deadline. We start with an offline auction, design an optimal end-to-end traffic scheduling approach, and exploit the Shapley value in computing payments. Our auction is truthful, individual rational, budget balanced and approximately efficient in social welfare. An online version of the auction follows, where decisions are made instantly upon the arrival of each user's realtime transmission demand. We propose an efficient online traffic scheduling algorithm, and approximate the offline Shapley value based payments on the fly. We validate our mechanism design with solid theoretical analysis, as well as trace-driven simulation studies.
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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 | 0.002 | 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 | 0.001 | 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 it