In the big data era, retaining the capability to process and store the sheer amount of data has become a necessity for data-intensive computing. To meet the requirement of big data processing, the storage-centric computing concept of processing data within storage devices has gained its popularity over the years, because the latency and energy consumed by moving data between host systems and storage devices gradually exceed that of processing data. To process data for data-intensive computing, one of the fundamental data processing technique is external sorting, which is widely used in database management systems (DBMS) and Hadoop framework. On the other hand, to store the ever-increasing volumes of data, shingled magnetic recording (SMR) drives have been proposed to increase the areal density of conventional hard disk drives (HDDs) via overlapping adjacent tracks. The SMR drive is widely regarded as a promising technology for the big data application because SMR drives can boost the capacity of HDDs without significant technology changes. Nevertheless, the overlapped track layout of SMR drive imposes the sequential write constraint on incoming write traffic, thus worsening the efficiency of performing external sorting on SMR drives. Such an observation motivates us to propose an SMR-based External Merge Sort (SMR-EMS) strategy for SMR-based large-scale storage systems with the goals of alleviating the negative impacts of sequential write constraint and enhancing the performance of external sorting on SMR drives via utilizing the concept of storage-centric computing. Experiments were conducted to demonstrate the capability of the proposed strategy on improving the efficiency of external merge sorting on SMR drives.
|頁（從 - 到）||333-348|
|期刊||Future Generation Computer Systems|
|出版狀態||已出版 - 3月 2021|