Oh-Trust: Overbooking and Hybrid Trading-Empowered Resource Scheduling With Smart Reputation Update Over Dynamic Edge Networks

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.

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Publication Details

Journal
IEEE Transactions on Emerging Topics in Computing
Published
2025-10-01
DOI
https://doi.org/10.1109/tetc.2025.3625379
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Oh-Trust: Overbooking and Hybrid Trading-Empowered Resource Scheduling With Smart Reputation Update Over Dynamic Edge Networks

Minghui Liwang, Huaiyu Dai, Liqun Fu, Houyi Qi et al.
IEEE Transactions on Emerging Topics in Computing
IoT and Edge/Fog Computing
article

Oh-Trust: Overbooking and Hybrid Trading-Empowered Resource Scheduling With Smart Reputation Update Over Dynamic Edge Networks

Minghui Liwang, Huaiyu Dai, Liqun Fu, Houyi Qi, Xianbin Wang, Xiaoyu Xia
article en

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.

IEEE Transactions on Emerging Topics in ComputingVol. 13(4)
Tongji University (CN), Western University (CA), North Carolina State University (US), Xiamen University (CN), RMIT University (AU)
National Natural Science Foundation of China, Chinese Academy of Engineering
Openalex Percentile: Top 63%
IoT and Edge/Fog Computing
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