{"title":"The Static Allocation is Not a Static: Optimizing SSD Address Allocation Through Boosting Static Policy","authors":"Yang Zhou;Fang Wang;Zhan Shi;Dan Feng","doi":"10.1109/TPDS.2024.3407367","DOIUrl":null,"url":null,"abstract":"The address allocation policy in SSD aims to translate the logical address of I/O requests into a physical address, and the static address allocation is widely used in modern SSD. Through extensive experiments, we find that there are significant differences in the utilization of SSD parallelism among different static address allocation policies. We also observe that the fixed address allocation design prevents SSDs from continuing to meet the challenges posed by cloud workloads and misses the possibility of further optimization. These situations stem from our excessive reliance on SSD parallelism over time. In this paper, we propose \n<monospace>HsaP</monospace>\n, a \n<underline>h</u>\nybrid \n<underline>s</u>\ntatic address \n<underline>a</u>\nllocation \n<underline>p</u>\nolicy, that adaptively chooses the best static allocation policy to meet the SSD performance at runtime. \n<monospace>HsaP</monospace>\n is a \n<italic>dynamic</i>\n scheduling scheme based on \n<italic>static</i>\n address allocation policy. The static policy ensures that \n<monospace>HsaP</monospace>\n has stable performance and light-weight overhead, while dynamic scheduling can effectively combine different allocation policies, selecting the best-performing static mapping mode for a given SSD state. Meanwhile, \n<monospace>HsaP</monospace>\n can further improve the read and write performance of SSDs simultaneously through plane reallocation and data rewrite. Experimental results show that \n<monospace>HsaP</monospace>\n achieves significant read and write performance gain of a wide range of the latest cloud block storage traces compared to several state-of-the-art address allocation approaches.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 8","pages":"1373-1386"},"PeriodicalIF":5.6000,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Parallel and Distributed Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10542449/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
The address allocation policy in SSD aims to translate the logical address of I/O requests into a physical address, and the static address allocation is widely used in modern SSD. Through extensive experiments, we find that there are significant differences in the utilization of SSD parallelism among different static address allocation policies. We also observe that the fixed address allocation design prevents SSDs from continuing to meet the challenges posed by cloud workloads and misses the possibility of further optimization. These situations stem from our excessive reliance on SSD parallelism over time. In this paper, we propose
HsaP
, a
h
ybrid
s
tatic address
a
llocation
p
olicy, that adaptively chooses the best static allocation policy to meet the SSD performance at runtime.
HsaP
is a
dynamic
scheduling scheme based on
static
address allocation policy. The static policy ensures that
HsaP
has stable performance and light-weight overhead, while dynamic scheduling can effectively combine different allocation policies, selecting the best-performing static mapping mode for a given SSD state. Meanwhile,
HsaP
can further improve the read and write performance of SSDs simultaneously through plane reallocation and data rewrite. Experimental results show that
HsaP
achieves significant read and write performance gain of a wide range of the latest cloud block storage traces compared to several state-of-the-art address allocation approaches.
期刊介绍:
IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to:
a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing.
b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems.
c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation.
d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.