TY - GEN
T1 - Triple-A
T2 - 19th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2014
AU - Jung, Myoungsoo
AU - Choi, Wonil
AU - Shalf, John
AU - Kandemir, Mahmut Taylan
PY - 2014
Y1 - 2014
N2 - Solid State Disk (SSD) arrays are in a position to (as least partially) replace spinning disk arrays in high performance computing (HPC) systems due to their better performance and lower power consumption. However, these emerging SSD arrays are facing enormous challenges, which are not observed in disk-based arrays. Specifically, we observe that the performance of SSD arrays can significantly degrade due to various array-level resource contentions. In addition, their maintenance costs exponentially increase over time, which renders them diff cult to deploy widely in HPC systems. To address these challenges, we propose Triple-A, a non-SSD based Autonomic All-Flash Array, which is a self-optimizing, from-scratch NAND f ash cluster. Triple-A can detect two different types of resource contentions and autonomically alleviate them by reshaping the physical data-layout on its f ash array network. Our experimental evaluation using both real workloads and a micro-benchmark show that Triple-A can offer a 53% higher sustained throughput and a 80% lower I/O latency than non-autonomic SSD arrays
AB - Solid State Disk (SSD) arrays are in a position to (as least partially) replace spinning disk arrays in high performance computing (HPC) systems due to their better performance and lower power consumption. However, these emerging SSD arrays are facing enormous challenges, which are not observed in disk-based arrays. Specifically, we observe that the performance of SSD arrays can significantly degrade due to various array-level resource contentions. In addition, their maintenance costs exponentially increase over time, which renders them diff cult to deploy widely in HPC systems. To address these challenges, we propose Triple-A, a non-SSD based Autonomic All-Flash Array, which is a self-optimizing, from-scratch NAND f ash cluster. Triple-A can detect two different types of resource contentions and autonomically alleviate them by reshaping the physical data-layout on its f ash array network. Our experimental evaluation using both real workloads and a micro-benchmark show that Triple-A can offer a 53% higher sustained throughput and a 80% lower I/O latency than non-autonomic SSD arrays
UR - http://www.scopus.com/inward/record.url?scp=84897778859&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84897778859&partnerID=8YFLogxK
U2 - 10.1145/2541940.2541953
DO - 10.1145/2541940.2541953
M3 - Conference contribution
AN - SCOPUS:84897778859
SN - 9781450323055
T3 - International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS
SP - 441
EP - 454
BT - ASPLOS 2014 - 19th International Conference on Architectural Support for Programming Languages and Operating Systems
Y2 - 1 March 2014 through 5 March 2014
ER -