Oh-Trust: Overbooking and Hybrid Trading-empowered Resource Scheduling with Smart Reputation Update over Dynamic Edge Networks

2025-09-29

AI Summary

In dynamic edge networks, mobile users—including both permanent and transient ones—exhibit highly volatile computational resource demands, rendering conventional spot or futures trading mechanisms inadequate in simultaneously ensuring real-time responsiveness and service stability. To address this, we propose Oh-Trust: a framework integrating overbooking with a hybrid futures–spot trading model, coupled with a reputation-based dynamic long-term contract update algorithm that incentivizes permanent users to commit to contracts while steering transient users toward immediate spot transactions. Our key innovation lies in the joint modeling of overbooking, reputation-driven adaptive contract adjustment, and real-time market responsiveness. Extensive simulations on real-world datasets demonstrate that Oh-Trust significantly outperforms baseline approaches across three core metrics: resource utilization, task completion timeliness, and platform revenue—thereby achieving a balanced trade-off among system efficiency, operational stability, and economic viability.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Incentive-driven computing resource sharing is crucial for meeting the ever-growing demands of emerging mobile applications. Although conventional spot trading offers a solution, it frequently leads to excessive overhead due to the need for real-time trading related interactions. Likewise, traditional futures trading, which depends on historical data, is susceptible to risks from network dynamics. This paper explores a dynamic and uncertain edge network comprising a computing platform, e.g., an edge server, that offers computing services as resource seller, and various types of mobile users with diverse resource demands as buyers, including fixed buyers (FBs) and uncertain occasional buyers (OBs) with fluctuating needs. To facilitate efficient and timely computing services, we propose an overbooking- and hybrid trading-empowered resource scheduling mechanism with reputation update, termed Oh-Trust. Particularly, our Oh-Trust incentivizes FBs to enter futures trading by signing long-term contracts with the seller, while simultaneously attracting OBs to spot trading, enhancing resource utilization and profitability for both parties. Crucially, to adapt to market fluctuations, a smart reputation updating mechanism is integrated, allowing for the timely renewal of long-term contracts to optimize trading performance. Extensive simulations using real-world datasets demonstrate the effectiveness of Oh-Trust across multiple evaluation metrics.

Problem

• Optimizing resource scheduling in dynamic edge networks
• Reducing trading overhead from real-time spot interactions
• Mitigating risks from network dynamics in futures trading

Innovation

• Hybrid trading combines futures and spot trading
• Overbooking mechanism enhances resource utilization efficiency
• Smart reputation update adapts to network dynamics