Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159752
Title: Hybrid market-based resources allocation in Mobile Edge Computing systems under stochastic information
Authors: Huang, Xiaowen
Gong, Shimin
Yang, Jingmin
Zhang, Wenjie
Yang, Liwei
Yeo, Chai Kiat
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Huang, X., Gong, S., Yang, J., Zhang, W., Yang, L. & Yeo, C. K. (2022). Hybrid market-based resources allocation in Mobile Edge Computing systems under stochastic information. Future Generation Computer Systems, 127, 80-91. https://dx.doi.org/10.1016/j.future.2021.08.029
Journal: Future Generation Computer Systems
Abstract: In order to deal with the problem of user diversity in Mobile Edge Computing (MEC) resource trading market, in this paper, we propose a hybrid market-based resource transaction mechanism consisting of futures market and spot market. Two different types of users have been taken into consideration. One is registered users and another is unregistered users. In futures market, registered users pay a registration fee to the agent and use the reserved resources according to the contract signed exclusively. We design optimal contracts for the registered users by adjusting the registration fee in order to maximize the servers’ utility. In spot market, unregistered users compete with one another to purchase the resources on demand. We model the trading process as a multi-seller and multi-buyer market, and propose auction algorithms to match the asking price from servers and the bidding price from unregistered users by assigning computation resources to the users. The agent acts as the auctioneer to host the auction, and the unregistered users bid on computation resources based on the estimated valuation. We study the optimal solution under both complete and incomplete information scenarios, depending on whether the agent can observe the users’ private information. Simulation results demonstrate the existences of the asking price and registration fee for the servers to maximize utility.
URI: https://hdl.handle.net/10356/159752
ISSN: 0167-739X
DOI: 10.1016/j.future.2021.08.029
Schools: School of Computer Science and Engineering 
Rights: © 2021 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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