012 IEEE 28th Symposium on Mass Storage Systems and Technologies (MSST)最新文献_第3页

Jitter-free co-processing on a prototype exascale storage stack 在百亿亿级存储堆栈原型上的无抖动协同处理

012 IEEE 28th Symposium on Mass Storage Systems and Technologies (MSST) Pub Date : 2012-04-16 DOI: 10.1109/MSST.2012.6232382

John Bent, S. Faibish, J. Ahrens, G. Grider, J. Patchett, P. Tzelnic, J. Woodring

{"title":"Jitter-free co-processing on a prototype exascale storage stack","authors":"John Bent, S. Faibish, J. Ahrens, G. Grider, J. Patchett, P. Tzelnic, J. Woodring","doi":"10.1109/MSST.2012.6232382","DOIUrl":"https://doi.org/10.1109/MSST.2012.6232382","url":null,"abstract":"In the petascale era, the storage stack used by the extreme scale high performance computing community is fairly homogeneous across sites. On the compute edge of the stack, file system clients or IO forwarding services direct IO over an interconnect network to a relatively small set of IO nodes. These nodes forward the requests over a secondary storage network to a spindle-based parallel file system. Unfortunately, this architecture will become unviable in the exascale era. As the density growth of disks continues to outpace increases in their rotational speeds, disks are becoming increasingly cost-effective for capacity but decreasingly so for bandwidth. Fortunately, new storage media such as solid state devices are filling this gap; although not cost-effective for capacity, they are so for performance. This suggests that the storage stack at exascale will incorporate solid state storage between the compute nodes and the parallel file systems. There are three natural places into which to position this new storage layer: within the compute nodes, the IO nodes, or the parallel file system. In this paper, we argue that the IO nodes are the appropriate location for HPC workloads and show results from a prototype system that we have built accordingly. Running a pipeline of computational simulation and visualization, we show that our prototype system reduces total time to completion by up to 30%.","PeriodicalId":348234,"journal":{"name":"012 IEEE 28th Symposium on Mass Storage Systems and Technologies (MSST)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128579581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 52

Exploiting superpages in a nonvolatile memory file system 利用非易失性内存文件系统中的超页

012 IEEE 28th Symposium on Mass Storage Systems and Technologies (MSST) Pub Date : 2012-04-16 DOI: 10.1109/MSST.2012.6232384

Sheng Qiu, A. L. Narasimha Reddy

引用次数: 5

BloomStore: Bloom-Filter based memory-efficient key-value store for indexing of data deduplication on flash BloomStore:基于bloomfilter的内存高效键值存储，用于对flash上的重复数据删除进行索引

012 IEEE 28th Symposium on Mass Storage Systems and Technologies (MSST) Pub Date : 2012-04-16 DOI: 10.1109/MSST.2012.6232390

Guanlin Lu, Youngjin Nam, D. Du

{"title":"BloomStore: Bloom-Filter based memory-efficient key-value store for indexing of data deduplication on flash","authors":"Guanlin Lu, Youngjin Nam, D. Du","doi":"10.1109/MSST.2012.6232390","DOIUrl":"https://doi.org/10.1109/MSST.2012.6232390","url":null,"abstract":"Due to its better scalability, Key-Value (KV) store has superseded traditional relational databases for many applications, such as data deduplication, on-line multi-player gaming, and Internet services like Amazon and Facebook. The KV store efficiently supports two operations (key lookup and KV pair insertion) through an index structure that maps keys to their associated values. The KV store is also commonly used to implement the chunk index in data deduplication, where a chunk ID (SHA1 value computed based on the chunk's content) is a key and its associative chunk metadata (e.g., physical storage location, stream ID) is the value. For a deduplication system, typically the number of chunks is too large to store the KV store solely in RAM. Thus, the KV store maintains a large (hash-table based) index structure in RAM to index all KV pairs stored on secondary storage. Hence, its available RAM space limits the maximum number of KV pairs that can be stored. Moving the index data structure from RAM to flash can possibly overcome the space limitation. In this paper, we propose efficient KV store on flash with a Bloom Filter based index structure called BloomStore. The unique features of the BloomStore include (1) no index structure is required to be stored in RAM so that a small RAM space can support a large number of KV pairs and (2) both index structure and KV pairs are stored compactly on flash memory to improve its performance. Compared with the state-of-the-art KV store designs, the BloomStore achieves a significantly better key lookup performance and roughly the same insertion performance with multiple times less RAM usage based on our experiments with deduplication workloads.","PeriodicalId":348234,"journal":{"name":"012 IEEE 28th Symposium on Mass Storage Systems and Technologies (MSST)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129234491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 66

Enhancing shared RAID performance through online profiling 通过在线分析增强共享RAID性能

012 IEEE 28th Symposium on Mass Storage Systems and Technologies (MSST) Pub Date : 2012-04-16 DOI: 10.1109/MSST.2012.6232383

Ji-guang Wan, Jibin Wang, Yan Liu, Qing Yang, Jianzong Wang, C. Xie

{"title":"Enhancing shared RAID performance through online profiling","authors":"Ji-guang Wan, Jibin Wang, Yan Liu, Qing Yang, Jianzong Wang, C. Xie","doi":"10.1109/MSST.2012.6232383","DOIUrl":"https://doi.org/10.1109/MSST.2012.6232383","url":null,"abstract":"Enterprise storage systems are generally shared by multiple servers in a SAN environment. Our experiments as well as industry reports have shown that disk arrays show poor performance when multiple servers share one RAID due to resource contention as well as frequent disk head movements. We have studied IO performance characteristics of several shared storage settings of practical business operations. To avoid the IO contention, we propose a new dynamic data relocation technique on shared RAID storages, referred to as DROP, Dynamic data Relocation to Optimize Performance. DROP allocates/manages a group of cache data areas and relocates/drops the portion of hot data at a predefined sub array that is a physical partition on the top of the entire shared array. By analyzing profiling data to make each cache area owned by one server, we are able to determine optimal data relocation and partition of disks in the RAID to maximize large sequential block accesses on individual disks and at the same time maximize parallel accesses across disks in the array. As a result, DROP minimizes disk head movements in the array at run time giving rise to high IO performance. A prototype DROP has been implemented as a software module at the storage target controller. Extensive experiments have been carried out using real world IO workloads to evaluate the performance of the DROP implementation. Experimental results have shown that DROP improves shared IO performance greatly. The performance improvements in terms of average IO response time range from 20% to a factor 2.5 at no additional hardware cost.","PeriodicalId":348234,"journal":{"name":"012 IEEE 28th Symposium on Mass Storage Systems and Technologies (MSST)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124473017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Storage challenges at Los Alamos National Lab 洛斯阿拉莫斯国家实验室的存储挑战

012 IEEE 28th Symposium on Mass Storage Systems and Technologies (MSST) Pub Date : 2012-04-16 DOI: 10.1109/MSST.2012.6232376

John Bent, G. Grider, B. Kettering, A. Manzanares, Meghan McClelland, Aaron Torres, Alfred Torrez

引用次数: 44

Adaptive pipeline for deduplication 重复数据删除的自适应管道

012 IEEE 28th Symposium on Mass Storage Systems and Technologies (MSST) Pub Date : 2012-04-01 DOI: 10.1109/MSST.2012.6232377

Jingwei Ma, Bin Zhao, G. Wang, X. Liu

{"title":"Adaptive pipeline for deduplication","authors":"Jingwei Ma, Bin Zhao, G. Wang, X. Liu","doi":"10.1109/MSST.2012.6232377","DOIUrl":"https://doi.org/10.1109/MSST.2012.6232377","url":null,"abstract":"Deduplication has become one of the hottest topics in the field of data storage. Quite a few methods towards reducing disk I/O caused by deduplication have been proposed. Some methods also have been studied to accelerate computational sub-tasks in deduplication. However, the order of computational sub-tasks can affect overall deduplication throughput significantly, because computational sub-tasks exhibit quite different workload and concurrency in different orders and with different data sets. This paper proposes an adaptive pipelining model for the computational sub-tasks in deduplication. It takes both data type and hardware platform into account. Taking the compression ratio and the duplicate ratio of the data stream, and the compression speed and the fingerprinting speed on different processing units as parameters, it determines the optimal order of the pipeline stages (computational sub-tasks) and assigns each stage to the processing unit which processes it fastest. That is, “adaptive” refers to both data adaptive and hardware adaptive. Experimental results show that the adaptive pipeline improves the deduplication throughput up to 50% compared with the plain fixed pipeline, which implies that it is suitable for simultaneous deduplication of various data types on modern heterogeneous multi-core systems.","PeriodicalId":348234,"journal":{"name":"012 IEEE 28th Symposium on Mass Storage Systems and Technologies (MSST)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126491353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6