TY - GEN
T1 - Chameleon
T2 - 32nd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2018
AU - Zhao, Nannan
AU - Anware, Ali
AU - Cheng, Yue
AU - Salman, Mohammed
AU - Li, Daping
AU - Wan, Jiguang
AU - Xie, Changsheng
AU - He, Xubin
AU - Wang, Feiyi
AU - Butt, Ali
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/3
Y1 - 2018/8/3
N2 - NAND flash-based Solid State Devices (SSDs) offer the desirable features of high performance, energy efficiency, and fast growing capacity. Thus, the use of SSDs is increasing in distributed storage systems. A key obstacle in this context is that the natural unbalance in distributed I/O workloads can result in wear imbalance across the SSDs in a distributed setting. This, in turn can have significant impact on the reliability, performance, and lifetime of the storage deployment. Extant load balancers for storage systems do not consider SSD wear imbalance when placing data, as the main design goal of such balancers is to extract higher performance. Consequently, data migration is the only common technique for tackling wear imbalance, where existing data is moved from highly loaded servers to the least loaded ones. In this paper, we explore an innovative holistic approach, Chameleon, that employs data redundancy techniques such as replication and erasure-coding, coupled with endurance-Aware write offloading, to mitigate wear level imbalance in distributed SSD-based storage. Chameleon aims to balance the wear among different flash servers while meeting desirable objectives of: extending life of flash servers; improving I/O performance; and avoiding bottlenecks. Evaluation with a 50 node SSD cluster shows that Chameleon reduces the wear distribution deviation by 81% while improving the write performance by up to 33%.
AB - NAND flash-based Solid State Devices (SSDs) offer the desirable features of high performance, energy efficiency, and fast growing capacity. Thus, the use of SSDs is increasing in distributed storage systems. A key obstacle in this context is that the natural unbalance in distributed I/O workloads can result in wear imbalance across the SSDs in a distributed setting. This, in turn can have significant impact on the reliability, performance, and lifetime of the storage deployment. Extant load balancers for storage systems do not consider SSD wear imbalance when placing data, as the main design goal of such balancers is to extract higher performance. Consequently, data migration is the only common technique for tackling wear imbalance, where existing data is moved from highly loaded servers to the least loaded ones. In this paper, we explore an innovative holistic approach, Chameleon, that employs data redundancy techniques such as replication and erasure-coding, coupled with endurance-Aware write offloading, to mitigate wear level imbalance in distributed SSD-based storage. Chameleon aims to balance the wear among different flash servers while meeting desirable objectives of: extending life of flash servers; improving I/O performance; and avoiding bottlenecks. Evaluation with a 50 node SSD cluster shows that Chameleon reduces the wear distribution deviation by 81% while improving the write performance by up to 33%.
KW - Flash cluster
KW - Key value store
KW - SSDs
KW - Wear balance
KW - Wear leveling
UR - http://www.scopus.com/inward/record.url?scp=85052196079&partnerID=8YFLogxK
U2 - 10.1109/IPDPS.2018.00125
DO - 10.1109/IPDPS.2018.00125
M3 - Conference contribution
AN - SCOPUS:85052196079
SN - 9781538643686
T3 - Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018
SP - 1163
EP - 1172
BT - Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 21 May 2018 through 25 May 2018
ER -