Rank-Based Prefetching and Multi-level Caching Algorithms to Improve the Efficiency of Read Operations in Distributed File Systems

2021 ◽  
pp. 227-243
Author(s):  
Anusha Nalajala ◽  
T. Ragunathan ◽  
Rathnamma Gopisetty ◽  
Vignesh Garrapally
Keyword(s):  
File Systems ◽  
Distributed File Systems ◽  
Multi Level

PABIRS: A data access middleware for distributed file systems

2015 ◽  
Author(s):  
Sai Wu ◽  
Gang Chen ◽  
Xianke Zhou ◽  
Zhenjie Zhang ◽  
Anthony K. H. Tung ◽  
...  
Keyword(s):  
File Systems ◽  
Data Access ◽  
Distributed File Systems

ALDM: Adaptive Loading Data Migration in Distributed File Systems

2013 ◽  
Vol 49 (6) ◽  
pp. 2645-2652 ◽  
Author(s):  
Zhipeng Tan ◽  
Wei Zhou ◽  
Dan Feng ◽  
Wenhua Zhang
Keyword(s):  
File Systems ◽  
Data Migration ◽  
Distributed File Systems

scholarly journals Performance Evaluations of Distributed File Systems for Scientific Big Data in FUSE Environment

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1471
Author(s):  
Jun-Yeong Lee ◽  
Moon-Hyun Kim ◽  
Syed Asif Raza Raza Shah ◽  
Sang-Un Ahn ◽  
Heejun Yoon ◽  
...  
Keyword(s):  
Data Storage ◽  
Scale Up ◽  
File Systems ◽  
Performance Evaluations ◽  
Distributed File Systems ◽  
Data Intensive Computing ◽  
Data Intensive ◽  
Tremendous Amount ◽  
Computing Environments ◽  
And Performance

Data are important and ever growing in data-intensive scientific environments. Such research data growth requires data storage systems that play pivotal roles in data management and analysis for scientific discoveries. Redundant Array of Independent Disks (RAID), a well-known storage technology combining multiple disks into a single large logical volume, has been widely used for the purpose of data redundancy and performance improvement. However, this requires RAID-capable hardware or software to build up a RAID-enabled disk array. In addition, it is difficult to scale up the RAID-based storage. In order to mitigate such a problem, many distributed file systems have been developed and are being actively used in various environments, especially in data-intensive computing facilities, where a tremendous amount of data have to be handled. In this study, we investigated and benchmarked various distributed file systems, such as Ceph, GlusterFS, Lustre and EOS for data-intensive environments. In our experiment, we configured the distributed file systems under a Reliable Array of Independent Nodes (RAIN) structure and a Filesystem in Userspace (FUSE) environment. Our results identify the characteristics of each file system that affect the read and write performance depending on the features of data, which have to be considered in data-intensive computing environments.

Octopus + : An RDMA-Enabled Distributed Persistent Memory File System

2021 ◽  
Vol 17 (3) ◽  
pp. 1-25
Author(s):  
Bohong Zhu ◽  
Youmin Chen ◽  
Qing Wang ◽  
Youyou Lu ◽  
Jiwu Shu
Keyword(s):  
High Speed ◽  
High Performance ◽  
File System ◽  
Direct Memory Access ◽  
File Systems ◽  
Distributed File Systems ◽  
Persistent Memory ◽  
Memory Modules ◽  
Non Volatile Memory ◽  
Volatile Memory

Non-volatile memory and remote direct memory access (RDMA) provide extremely high performance in storage and network hardware. However, existing distributed file systems strictly isolate file system and network layers, and the heavy layered software designs leave high-speed hardware under-exploited. In this article, we propose an RDMA-enabled distributed persistent memory file system, Octopus + , to redesign file system internal mechanisms by closely coupling non-volatile memory and RDMA features. For data operations, Octopus + directly accesses a shared persistent memory pool to reduce memory copying overhead, and actively fetches and pushes data all in clients to rebalance the load between the server and network. For metadata operations, Octopus + introduces self-identified remote procedure calls for immediate notification between file systems and networking, and an efficient distributed transaction mechanism for consistency. Octopus + is enabled with replication feature to provide better availability. Evaluations on Intel Optane DC Persistent Memory Modules show that Octopus + achieves nearly the raw bandwidth for large I/Os and orders of magnitude better performance than existing distributed file systems.

Distributed File Systems*

2014 ◽  
pp. 1-26
Author(s):  
Thomas Doeppner
Keyword(s):  
File Systems ◽  
Distributed File Systems

scholarly journals Performance-Driven Load Balancing for Distributed File Systems in Clouds

2018 ◽  
Vol 179 (40) ◽  
pp. 39-50
Author(s):  
Jasma Balasangameshwara ◽  
Chandrakala H.
Keyword(s):  
Load Balancing ◽  
File Systems ◽  
Distributed File Systems
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