Dynamic Pricing in Multi-Tenant MANO With Resource Sharing: A Stackelberg Game Approach
Bibliographic record
Abstract
Network slicing is used to support the stringent requirements of sixth generation (6G) services by dividing an infrastructure network into multiple logical networks that can enable service-oriented resource allocation. However, there are several orchestration issues when considering multiple infrastructure providers (InPs) and multiple tenants in a recursive architecture. There are also challenging issues in designing efficient auction mechanisms for such multi-domain and multi-tenant network slicing. To address these challenges, we consider multi-tenant management and orchestration as a multi-buyer, multi-seller scenario, and propose a novel two-stage auction mechanism that aims to increase the overall utility of all participants while mitigating the overall cost of the network. We formulate this two-stage auction mechanism as a multi-leader multi-follower (MLMF) Stackelberg game approach that converges to a Stackelberg equilibrium. In this game, there are multiple InPs that lease network, computing, and storage infrastructure resources to multiple Tier1 tenants in the first stage of the auction mechanism. Next, Tier1 tenants instantiate triple 6G slices as extremely reliable and low-latency communications (eURLLC), ultra-massive machine-type communications (umMTC), and further enhanced mobile broadband (FeMBB) slices, and lease smaller slices to Tier2 tenants through the second step of the auction mechanism. Tier2 tenants then serve different eURLLC, umMTC, and FeMBB users who have specific and mostly different requirements and constraints, while Tier2 tenants manage their own resources to maximize their utility. Due to the distributed nature of the proposed problem, we consider distributed reinforcement learning (DRL) as a solution. Simulation results show that our DRL-based solution increases the average profit of the network by 19% compared to the existing state-of-the-art benchmark.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".