Abstract: This paper presents an oversold memory resource sharing method for multi-tenant databases in an online analysis and processing scenario.The current static resource allocation strategy,which assigns a fixed resource quota to each tenant,leads to suboptimal resource utilization.To enhance resource utilization and platform revenue,it is important to share unused free resources among tenants without impacting their performance.While existing resource sharing methods for multi-tenant databases primarily focus on CPU resources,there is a lack of memory resource sharing methods that support overselling.To address this gap,the paper introduces a novel approach MMOS that accurately forecasts the memory requirements interval of each tenant and dynamically adjusts their resource allocation based on the upper limit of the interval.This allows for efficient management of free memory resources,enabling support for more tenants and achieving memory overselling while maintaining optimal performance.Experimental results demonstrate the effectiveness of the proposed method in dynamically changing tenant load scenarios.With different resource pools,the number of supported tenants can be increased by 2~2.6 times,leading to a significant increase in peak resource utilization by 175%~238%.Importantly,the proposed method ensures that the business and performance of each tenant remain unaffected.

Key words: Multi-tenant database, Resource overselling, Memory resources, Resource forecasting, Resource allocation