Proceedings of the 13th ACM Workshop on Hot Topics in Storage and File Systems最新文献

Self-service data protection for stateful containers 有状态容器的自助数据保护

Proceedings of the 13th ACM Workshop on Hot Topics in Storage and File Systems Pub Date : 2021-07-27 DOI: 10.1145/3465332.3470876

Umesh Deshpande, Nick Linck, S. Seshadri

引用次数: 2

How the common retention acceleration method of 3D NAND flash memory goes wrong? 常见的3D NAND闪存保留加速方法是如何出错的?

Proceedings of the 13th ACM Workshop on Hot Topics in Storage and File Systems Pub Date : 2021-07-27 DOI: 10.1145/3465332.3470877

Qiao Li, Min Ye, Tei-Wei Kuo, C. Xue

引用次数: 6

Libpubl

Proceedings of the 13th ACM Workshop on Hot Topics in Storage and File Systems Pub Date : 2021-07-27 DOI: 10.1145/3465332.3470874

Jaewon Jung, Jungsik Choi, Hwansoo Han

引用次数: 0

CRDTs for truly concurrent file systems 用于真正并发文件系统的crdt

Proceedings of the 13th ACM Workshop on Hot Topics in Storage and File Systems Pub Date : 2021-07-27 DOI: 10.1145/3465332.3470872

Romain Vaillant, Dimitrios Vasilas, M. Shapiro, T. Nguyen

{"title":"CRDTs for truly concurrent file systems","authors":"Romain Vaillant, Dimitrios Vasilas, M. Shapiro, T. Nguyen","doi":"10.1145/3465332.3470872","DOIUrl":"https://doi.org/10.1145/3465332.3470872","url":null,"abstract":"Building scalable and highly available geo-replicated file systems is hard. These systems need to resolve conflicts that emerge in concurrent operations in a way that maintains file system invariants, is meaningful to the user, and does not depart from the traditional file system interface. Conflict resolution in existing systems often leads to unexpected or inconsistent results. This paper introduces ElmerFS, a geo-replicated, truly concurrent file system designed with the aim of addressing these challenges. ElmerFS is based on two key ideas: (1) the use of Conflict-Free Replicated Data Types (CRDTs) for representing file system structures, which ensures that replicas converge to a correct state, and (2) conflict resolution rules, which are determined by the choice of CRDT types and their composition, are designed with the principle of being intuitive to the user. We argue that if the state of the file system after resolving a conflict conveys to the user the resolved conflict in an intuitive way, the user can complement or reverse it using traditional file system operations. We discuss the challenges in the design of geo-replicated weakly consistent file systems, and present the design of ElmerFS.","PeriodicalId":281760,"journal":{"name":"Proceedings of the 13th ACM Workshop on Hot Topics in Storage and File Systems","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121091265","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: 3

Automatic I/O stream management based on file characteristics 基于文件特征的自动I/O流管理

Proceedings of the 13th ACM Workshop on Hot Topics in Storage and File Systems Pub Date : 2021-07-27 DOI: 10.1145/3465332.3470879

Yuqi Zhang, Ni Xue, Yangxu Zhou

引用次数: 2

DNA archival storage, a bottom up approach DNA档案存储，自下而上的方法

Proceedings of the 13th ACM Workshop on Hot Topics in Storage and File Systems Pub Date : 2021-07-27 DOI: 10.1145/3465332.3470880

Omer S. Sella, Amir Apelbaum, T. Heinis, Jasmine Quah, A. Moore

引用次数: 2

Enabling near-data processing in distributed object storage systems 支持分布式对象存储系统的近数据处理

Proceedings of the 13th ACM Workshop on Hot Topics in Storage and File Systems Pub Date : 2021-07-27 DOI: 10.1145/3465332.3470881

I. Adams, Neha Agrawal, M. Mesnier

{"title":"Enabling near-data processing in distributed object storage systems","authors":"I. Adams, Neha Agrawal, M. Mesnier","doi":"10.1145/3465332.3470881","DOIUrl":"https://doi.org/10.1145/3465332.3470881","url":null,"abstract":"Most general-purpose distributed storage systems are not designed with near data processing (NDP) in mind. They do not respect semantic data boundaries when writing data, for example splitting a record across servers. This reduces NDP effectiveness by requiring data collation before computation. While semantic data awareness and NDP functions can be retroactively added to existing distributed storage, it is often complex and difficult to accomplish in practice. We propose sharing storage system layout information with data writers so they can adjust data layouts to prevent data alignment issues regardless of the underlying architectures. By doing so, we can simplify NDP implementation by reducing the need for data reassembly, and reduce the need for complex storage system or application extensions. We demonstrate a hinting mechanism on both HDFS with computational block storage and an erasure coded MinIO deployment, reducing data movement by up to 99% when querying CSV data with NDP co-located with the stored data. This was accomplished purely with client side data alignment, no modifications to the server side write paths, and no inter-node collation of data.","PeriodicalId":281760,"journal":{"name":"Proceedings of the 13th ACM Workshop on Hot Topics in Storage and File Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127783172","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: 4

A Machine Learning Framework to Improve Storage System Performance 提高存储系统性能的机器学习框架

Proceedings of the 13th ACM Workshop on Hot Topics in Storage and File Systems Pub Date : 2021-07-27 DOI: 10.1145/3465332.3470875

I. Akgun, A. S. Aydin, Aadil Shaikh, L. Velikov, E. Zadok

{"title":"A Machine Learning Framework to Improve Storage System Performance","authors":"I. Akgun, A. S. Aydin, Aadil Shaikh, L. Velikov, E. Zadok","doi":"10.1145/3465332.3470875","DOIUrl":"https://doi.org/10.1145/3465332.3470875","url":null,"abstract":"Storage systems and their OS components are designed to accommodate a wide variety of applications and dynamic workloads. Storage components inside the OS contain various heuristic algorithms to provide high performance and adaptability for different workloads. These heuristics may be tunable via parameters, and some system calls allow users to optimize their system performance. These parameters are often predetermined based on experiments with limited applications and hardware. Thus, storage systems often run with these predetermined and possibly suboptimal values. Tuning these parameters manually is impractical: one needs an adaptive, intelligent system to handle dynamic and complex workloads. Machine learning (ML) techniques are capable of recognizing patterns, abstracting them, and making predictions on new data. ML can be a key component to optimize and adapt storage systems. In this position paper, we propose KML, an ML framework for storage systems. We implemented a prototype and demonstrated its capabilities on the well-known problem of tuning optimal readahead values. Our results show that KML has a small memory footprint, introduces negligible overhead, and yet enhances throughput by as much as 2.3x.","PeriodicalId":281760,"journal":{"name":"Proceedings of the 13th ACM Workshop on Hot Topics in Storage and File Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131467187","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: 8

Isolating namespace and performance in key-value SSDs for multi-tenant environments 在多租户环境中隔离键值ssd中的名称空间和性能

Proceedings of the 13th ACM Workshop on Hot Topics in Storage and File Systems Pub Date : 2021-07-27 DOI: 10.1145/3465332.3470883

Donghyun Min, Youngjae Kim

引用次数: 2

SentiLog SentiLog

Proceedings of the 13th ACM Workshop on Hot Topics in Storage and File Systems Pub Date : 2021-07-27 DOI: 10.1145/3465332.3470873

Di Zhang, Dong Dai, Runzhou Han, Mai Zheng

{"title":"SentiLog","authors":"Di Zhang, Dong Dai, Runzhou Han, Mai Zheng","doi":"10.1145/3465332.3470873","DOIUrl":"https://doi.org/10.1145/3465332.3470873","url":null,"abstract":"As core components of High-performance computing (HPC) platforms, parallel file systems (PFSes) grow quickly in scale and complexity, hence are subject to various failures and anomalies. Identifying their anomalies in runtime is critically helpful for HPC operators and administrators. Analyzing the runtime logs to detect the anomalies of large-scale systems has been proven effective in many recent studies. However, applying them to parallel file systems logs faces significant challenges due to the large volume and irregularity of PFSes logs. This study proposes SentiLog, a new approach to analyzing PFSes system logs for detecting anomalies. Unlike existing solutions, SentiLog works by training a general sentimental, natural language model based on the logging-relevant source code collected from a set of PFSes. In this way, SentiLog learns information embedded by developers from the source code. Our preliminary results show SentiLog is able to accurately predict anomalies and performs better than state-of-the-art log analysis solutions on two representative PFSes (Lustre and BeeGFS). This preliminary study shows sentiment analysis could be a promising method to analyze complex and irregular system logs.","PeriodicalId":281760,"journal":{"name":"Proceedings of the 13th ACM Workshop on Hot Topics in Storage and File Systems","volume":"13 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116835946","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: 12