Title: Multi-version and energy-efficient role-based transaction processing for AI services

Authors: Tomoya Enokido; Dilawaer Duolikun; Shigenari Nakamura; Makoto Takizawa

Addresses: Faculty of Business Administration, Rissho University, 4-2-16, Osaki, Shinagawa-ku, Tokyo, 141-8602, Japan ' BP&IT, Contemporary Amperex Technology Hungary, Debrecen, 0495/267 hrsz., 4002, Hungary ' Department of Information System Engineering, School of System Design and Technology, Tokyo Denki University, 5 Senju-Asahi-cho, Adachi-ku, Tokyo, 120-8551, Japan ' Research Center for Computing and Multimedia Studies, Hosei University, 3-7-2, Kajino-cho, Koganei-shi, Tokyo, 184-8584, Japan

Abstract: In artificial intelligence (AI) services, a vast amount of data is amassed from various services and devices into data centres (DCs). Numerous users share the data by issuing transactions. Consequently, the electricity consumption of DCs increases by the proliferation of AI services. Hence, a control method to maintain data integrity and improve the throughput of transaction processing while reducing the electricity consumption of servers has to be realised for AI services. In this paper, a multi-version energy efficient role ordering (MVEERO) scheduler is newly proposed to maintain data integrity and improve the throughput of transaction processing while reducing the electricity consumption of servers. In evaluation, the execution time of transactions and the electricity consumption of a server cluster in the MVEERO scheduler are shown to be maximally reduced 31% and 13%, respectively, to the energy-efficient role ordering in virtual machine environment (EERO-VM) scheduler which is previously proposed in our studies.

Keywords: transaction processing; energy-aware system; role-based scheduler; multi-version concurrency control; MVEERO scheduler; RBAC model; AI service; data centre; electricity consumption; virtual machine.

DOI: 10.1504/IJWGS.2026.151906

International Journal of Web and Grid Services, 2026 Vol.22 No.1, pp.45 - 62

Received: 29 Oct 2025
Accepted: 12 Nov 2025
Published online: 25 Feb 2026 *

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