A data-driven storage recommendation service for multitenant storage management environments

Abstract

Storage management aims to improve the data center performance by optimizing the underlying storage resources more efficiently. The advent of cloud computing technologies introduces a paradigm shift from conventional on-premise storage management solutions to multitenant storage management as a service models. The advantage of centralized multitenant storage management platforms lies in the integrated procedures and the unified platform of data collection, governance, and analytics to gauge the effectiveness and efficiency of tenant storage environments. In this paper, we introduce our data-driven storage optimization framework, which is a centralized storage data analytics module to provide recommendations for the storage administrator. We use three example use cases to illustrate the exploitation of individual data center operational data to obtain actionable insights for better storage management solutions.