HybridFS — A High Performance and Balanced File System Framework with Multiple Distributed File Systems

2017 ◽  
Author(s):  
Lidong Zhang ◽  
Yongwei Wu ◽  
Ruini Xue ◽  
Tse-Chuan Hsu ◽  
Hongji Yang ◽  
...  
Keyword(s):  
High Performance ◽  
File System ◽  
File Systems ◽  
Distributed File Systems ◽  
System Framework

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.

scholarly journals Modeling of distributed file System in big data storage by event- B

2018 ◽  
Vol 210 ◽  
pp. 04042
Author(s):  
Ammar Alhaj Ali ◽  
Pavel Varacha ◽  
Said Krayem ◽  
Roman Jasek ◽  
Petr Zacek ◽  
...  
Keyword(s):  
Big Data ◽  
Data Storage ◽  
High Performance ◽  
File System ◽  
Formal Method ◽  
File Systems ◽  
Distributed File System ◽  
Distributed File Systems ◽  
Data Systems ◽  
Big Data Systems

Nowadays, a wide set of systems and application, especially in high performance computing, depends on distributed environments to process and analyses huge amounts of data. As we know, the amount of data increases enormously, and the goal to provide and develop efficient, scalable and reliable storage solutions has become one of the major issue for scientific computing. The storage solution used by big data systems is Distributed File Systems (DFSs), where DFS is used to build a hierarchical and unified view of multiple file servers and shares on the network. In this paper we will offer Hadoop Distributed File System (HDFS) as DFS in big data systems and we will present an Event-B as formal method that can be used in modeling, where Event-B is a mature formal method which has been widely used in a number of industry projects in a number of domains, such as automotive, transportation, space, business information, medical device and so on, And will propose using the Rodin as modeling tool for Event-B, which integrates modeling and proving as well as the Rodin platform is open source, so it supports a large number of plug-in tools.

High Performance Storage for Big Data Analytics and Visualization

2018 ◽  
pp. 254-275
Author(s):  
Armando Fandango ◽  
William Rivera
Keyword(s):  
Big Data ◽  
High Speed ◽  
High Performance ◽  
File System ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
File Systems ◽  
Distributed Applications ◽  
System Level ◽  
File Formats

Scientific Big Data being gathered at exascale needs to be stored, retrieved and manipulated. The storage stack for scientific Big Data includes a file system at the system level for physical organization of the data, and a file format and input/output (I/O) system at the application level for logical organization of the data; both of them of high-performance variety for exascale. The high-performance file system is designed with concurrent access, high-speed transmission and fault tolerance characteristics. High-performance file formats and I/O are designed to allow parallel and distributed applications with easy and fast access to Big Data. These specialized file formats make it easier to store and access Big Data for scientific visualization and predictive analytics. This chapter provides a brief review of the characteristics of high-performance file systems such as Lustre and GPFS, and high-performance file formats such as HDF5, NetCDF, MPI-IO, and HDFS.

scholarly journals Performance gains in an ESM using parallel ad-hoc file systems

2020 ◽  
Author(s):  
Stefan Versick ◽  
Ole Kirner ◽  
Jörg Meyer ◽  
Holger Obermaier ◽  
Mehmet Soysal
Keyword(s):  
High Performance ◽  
Ad Hoc ◽  
File System ◽  
File Systems ◽  
System Modelling ◽  
Earth System ◽  
Post Processing ◽  
In Situ Data ◽  
The Impact

<p>Earth System Models (ESM) got much more demanding over the last years. Modelled processes got more complex and more and more processes are considered in models. In addition resolutions of the models got higher to improve weather and climate forecasts. This requires faster high performance computers (HPC) and better I/O performance.</p><p>Within our Pilot Lab Exascale Earth System Modelling (PL-EESM) we do performance analysis of the ESM EMAC using a standard Lustre file system for output and compare it to the performance using a parallel ad-hoc overlay file system. We will show the impact for two scenarios: one for todays standard amount of output and one with artificial heavy output simulating future ESMs.</p><p>An ad-hoc file system is a private parallel file system which is created on-demand for an HPC job using the node-local storage devices, in our case solid-state-disks (SSD). It only exists during the runtime of the job. Therefore output data have to be moved to a permanent file system before the job has finished. Quasi in-situ data analysis and post-processing allows to gain performance as it might result in a decreased amount of data which you have to store - saving disk space and time during the transfer of data to permanent storage. We will show first tests for quasi in-situ post-processing.</p>

scholarly journals Performance evaluation of a throughput-aware framework for ensemble data assimilation: the case of NICAM-LETKF

2016 ◽  
Vol 9 (7) ◽  
pp. 2293-2300 ◽  
Author(s):  
Hisashi Yashiro ◽  
Koji Terasaki ◽  
Takemasa Miyoshi ◽  
Hirofumi Tomita
Keyword(s):  
Data Assimilation ◽  
High Performance ◽  
Weather Prediction ◽  
File Systems ◽  
Atmospheric Model ◽  
Distributed File Systems ◽  
Ensemble Data Assimilation ◽  
Computational Performance ◽  
Ensemble Data ◽  
Data Throughput

Abstract. In this paper, we propose the design and implementation of an ensemble data assimilation (DA) framework for weather prediction at a high resolution and with a large ensemble size. We consider the deployment of this framework on the data throughput of file input/output (I/O) and multi-node communication. As an instance of the application of the proposed framework, a local ensemble transform Kalman filter (LETKF) was used with a Non-hydrostatic Icosahedral Atmospheric Model (NICAM) for the DA system. Benchmark tests were performed using the K computer, a massive parallel supercomputer with distributed file systems. The results showed an improvement in total time required for the workflow as well as satisfactory scalability of up to 10 K nodes (80 K cores). With regard to high-performance computing systems, where data throughput performance increases at a slower rate than computational performance, our new framework for ensemble DA systems promises drastic reduction of total execution time.

scholarly journals Log-Less Metadata Management on Metadata Server for Parallel File Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Jianwei Liao ◽  
Guoqiang Xiao ◽  
Xiaoning Peng
Keyword(s):  
Performance Improvement ◽  
File System ◽  
File Systems ◽  
Metadata Management ◽  
Distributed File Systems ◽  
Data Throughput ◽  
Management Schemes ◽  
Metadata Processing ◽  
Metadata Server ◽  
Metadata Service

This paper presents a novel metadata management mechanism on the metadata server (MDS) for parallel and distributed file systems. In this technique, the client file system backs up the sent metadata requests, which have been handled by the metadata server, so that the MDS does not need to log metadata changes to nonvolatile storage for achieving highly available metadata service, as well as better performance improvement in metadata processing. As the client file system backs up certain sent metadata requests in its memory, the overhead for handling these backup requests is much smaller than that brought by the metadata server, while it adopts logging or journaling to yield highly available metadata service. The experimental results show that this newly proposed mechanism can significantly improve the speed of metadata processing and render a better I/O data throughput, in contrast to conventional metadata management schemes, that is, logging or journaling on MDS. Besides, a complete metadata recovery can be achieved by replaying the backup logs cached by all involved clients, when the metadata server has crashed or gone into nonoperational state exceptionally.

Research and Design of a Trusted Distributed File System Based on HDFS

2014 ◽  
Vol 602-605 ◽  
pp. 3282-3284
Author(s):  
Fa Gui Liu ◽  
Xiao Jie Zhang
Keyword(s):  
File System ◽  
File Systems ◽  
Distributed File System ◽  
Distributed File Systems ◽  
Security Mechanism ◽  
Advanced Persistent Threat ◽  
Research And Design

Distributed file systems such as HDFS are facing the threat of Advanced Persistent Threat, APT. Although security mechanisms such as Kerberos and ACL are implemented in distributed file systems, most of them are not sufficient to solve the threats caused by APT. With the observation into traits of APT, we propose a trusted distributed file system based on HDFS, which guarantees another further security facing APT compared to the current security mechanism.

NVMM-Oriented Hierarchical Persistent Client Caching for Lustre

2021 ◽  
Vol 17 (1) ◽  
pp. 1-22
Author(s):  
Wen Cheng ◽  
Chunyan Li ◽  
Lingfang Zeng ◽  
Yingjin Qian ◽  
Xi Li ◽  
...  
Keyword(s):  
High Performance ◽  
File System ◽  
File Systems ◽  
Main Memory ◽  
Full Potential ◽  
Storage Devices ◽  
Hierarchical Storage ◽  
Client Cache ◽  
Client Side ◽  
Performance Computing

In high-performance computing (HPC), data and metadata are stored on special server nodes and client applications access the servers’ data and metadata through a network, which induces network latencies and resource contention. These server nodes are typically equipped with (slow) magnetic disks, while the client nodes store temporary data on fast SSDs or even on non-volatile main memory (NVMM). Therefore, the full potential of parallel file systems can only be reached if fast client side storage devices are included into the overall storage architecture. In this article, we propose an NVMM-based hierarchical persistent client cache for the Lustre file system (NVMM-LPCC for short). NVMM-LPCC implements two caching modes: a read and write mode (RW-NVMM-LPCC for short) and a read only mode (RO-NVMM-LPCC for short). NVMM-LPCC integrates with the Lustre Hierarchical Storage Management (HSM) solution and the Lustre layout lock mechanism to provide consistent persistent caching services for I/O applications running on client nodes, meanwhile maintaining a global unified namespace of the entire Lustre file system. The evaluation results presented in this article show that NVMM-LPCC can increase the average read throughput by up to 35.80 times and the average write throughput by up to 9.83 times compared with the native Lustre system, while providing excellent scalability.

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