Profitable and Scalable MEC: Reputation-Based Service Replication via Stackelberg Game
Bibliographic record
Abstract
Mobile edge computing (MEC) is a promising paradigm for Internet of Things applications requiring synchronized user experiences. However, sustaining scalable and reliable MEC services is challenging when computational resources are overloaded, especially as MEC service providers (SPs) must minimize operational costs to maximize profits while offering competitively priced services. This article proposes the cooperative multiprovider market (CMPM) scheme, the first to cooperatively enhance service scalability and reliability while addressing the profit-pricing dilemma in a multiprovider market. CMPM enables overloaded home SPs (HSPs) to leverage underutilized computational resources from reliable foreign SPs (FSPs) via reputation-based service replication, meeting the stringent Quality of Service (QoS) requirements for real-time applications involving user groups. CMPM resolves the pricing dilemma by applying a game-theoretic approach, allowing FSPs to dynamically optimize revenue and adjust prices when HSPs cannot meet user demand. We formulate the resource allocation and pricing problem as a Stackelberg game, establish the existence of the equilibrium, and develop a distributed algorithm to reach it. Extensive evaluations show that CMPM significantly reduces unit prices, attracts more HSPs, and better manages high-density user loads compared to state-of-the-art schemes that overlook SP reputation and social welfare. CMPM also achieves up to 84% higher FSP revenue, a 67% improvement in scalability, and a 70% higher task success rate compared to baseline schemes.
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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 | 0.003 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".