Multi-version and Energy-Efficient Role-Based Transaction Processing for AI Services

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.