SMA 2020, September 17-19, Jeju, Republic of Korea, S. Chum et al. SLA-Aware Adaptive Mapping Scheme in Bigdata Distributed Storage Systems Sopanhapich Chum Jerry Li Department of Computer Science Memory Solutions Lab. Dankook University Samsung Semiconductor Inc. Yongin, Korea San Jose, CA, USA
[email protected] [email protected] Heekwon Park Jongmoo Choi Memory Solutions Lab. Department of Computer Science Samsung Semiconductor Inc. Dankook University San Jose, CA, USA Yongin, Korea
[email protected] [email protected] ABSTRACT by 2025, with a compounded annual growth rate of 61% [23]. In As data are processed by diverse clients ranging from urgent time- addition, these data are processed instantly to extract information critical to best-effort, supporting different QoS (Quality of Service) for decision making, recommendation and autonomous control becomes a vital component in a distributed storage system. In this using various analytic models and frameworks [1, 8, 11, 20, 27, 30]. paper, we propose a novel SLA (Service Level Agreement)-aware We indeed live in Bigdata era [24]. adaptive mapping scheme that can differentiate between urgent Distributed storage (also called as Cloud storage) plays a key and normal clients based on their I/O requirements. The scheme ba- role in Bigdata era. There are various distributed storage systems sically divides storage into two regions, normal and urgent, which including GFS [14], HDFS [26], Ceph [2, 33], Azure Storage [16], makes it feasible to isolate urgent clients from normal ones. In Amazon S3 [22], Openstack Swift [18], Haystack [6], Lustre [35], addition, it changes the size of the isolated region in an adaptive GlusterFS [17] and so on.