Published on November 2019 | Big Data, Artificial Intelligence

A Survey Of Workload Management In Big Data Storage

The information explosion has made the world of data over the last five years. The advancement of mobile technology, the availability of tablets and smartphones, and the rapid growth of social media have all contributed to both production and consumption of data at never-before-seen volumes. Other contributing factors have been recommendation engines, cool new visualization capabilities for business intelligence (BI), advances in software. Different types of data with varying degrees of complexity are produced at multiple levels of velocity. A huge increase in data storage and processing requirements has led to Big Data, for which next generation storage systems are being designed and implemented. As Big Data stresses the storage layer in new ways, a better understanding of these workloads and the availability of flexible workload generators are increasingly important to facilitate the proper design and performance tuning of storage subsystems like data replication, metadata management, and caching. This paper is a survey paper, which reveals the workload management in Big Data storage.