Distributed File System Based on a Relational Database

Working with files and the safety of information has always been relevant, especially in financial institutions where the requirements for the safety of information and security are especially important. And in today’s conditions, when an earthquake can destroy the floor of a city in an instant, or when a missile hits an office and all servers turn into scrap metal, the issue of data safety becomes especially important. Also, you can’t put the cost of the software and the convenience of working with files in last place. Especially if an office worker needs to find the necessary information on a client, a financial contract or a company’s financial product in a few seconds. Also, during the operation of computer equipment, failures are possible, and some of them can lead to partial or complete loss of information. In this paper, it is proposed to create another level of abstraction for working with the file system, which will be based on a relational database as a storage of objects and access rights to objects. Also considered are possible protocols for transferring data to other programs that work with files, these can be both small sites and the operating system itself. This article will be especially interesting for financial institutions or companies operating in the banking sector. The purpose of this article is an attempt to introduce another level of abstraction for working with files. A level that is completely abstracted from the storage medium.

Building Hot Snapshot Copy Based on Windows File System

This paper describes a method for building hot snapshot copy based on windows-file system （HSCF）. The architecture and running mechanism of HSCF are discussed after giving a comparison with other on-line backup tecbnology. HSCF, based on a file system filter driver, protects computer data and ensures their integrity and consistency with following three steps： access to open files, synchronization and copy on-write. Its strategies for improving system performance are analyzed including priority setting, incremental snapshot and load balance. HSCF is a new kind of snapshot technology to solve the data integrity and consistency problem in online backup, which is different from other storage-level snapshot and Open File Solution.

Load-Balance Policy in Two Level-Cluster File System

In this paper, we explored a load-balancing algorithm in a cluster file system contains two levels of metadata-server, primary-level server quickly distributestasks to second-level servers depending on the closest load-balancing information. At the same time, we explored a method which accurately reflect I/O traffic and storage of storage-node： computing the heat-value of file, according to which we realized a more logical storage allocation. According to the experiment result, we conclude that this new algorithm shortens the executing time of tasks and improves the system performance compared with other load algorithm.

Persistent Data Layout in File Systems

Data layout in a file system is the organization of data stored in external storages. The data layout has a huge impact on performance of storage systems. We survey three main kinds of data layout in traditional file systems： in-place update file system, log-structured file system, and copy-on-write file sys- tem. Each file system has its own strengths and weaknesses under different circumstances. We also include a recent us- age of persistent layout in a file system that combines both flash memory and byte- addressable non- volatile memory. With this survey, we conclude that persistent data layout in file systems may evolve dramatically in the era of emerging non-volatile memory.

Zion File System Simulator

File systems are fundamental for computers and devices with data storage units. They allow operating systems to understand and organize streams of bytes and obtain readable files from them. There are numerous file systems available in the industry, all with their own unique features. Understanding how these file systems work is essential for computer science students, but their complex nature can be difficult and challenging to grasp, especially for students at the beginning of their career. The Zion File System Simulator was designed with this in mind. Zion is a teaching and experimenting tool, in the form of a small application, built to help students understand how the I/O manager of an operating system interacts with the drive through the file system. Users can see and analyze the structure of a simple, flat file system provided with Zion, or simulate the most common structures such as FAT or NTFS. Students can also create their own implementations and run them through the simulator to analyze the different behaviors. Zion runs on Windows, and the application is provided with dynamic-link libraries that include the interfaces of a file system and a volume manager. These interfaces allow programmers to build their own file system or volume manager in Visual Studio using any .NET language (3.0 or above). Zion gives the users the power to adjust simulated architectural parameters such as volume and block size, or performance factors such as seek and transfer time. Zion runs workloads of I/O operations such as “create,” “delete,” “read,” and “write,” and analyzes the resulting metrics including I/O operations, read/write time, and disk fragmentation. Zion is a learning tool. It is not designed for measuring accurate performance of file systems and volume managers. The robustness of the application, together with its expandability, makes Zion a potential laboratory tool for computer science classes, helping students learn how file systems work and interact with an operating system.

基于Read-only zip file system的文字内码到点阵信息的转换研究

在嵌入式系统环境下，利用Ahera公司提供的zip file system，用C语言标准文件读写函数将内码转换为可以在液晶屏幕上显示字符图形的点阵信息。将该方案应用于电子阅读器的开发，较好的实现了设计目标，并在硬件、软件方面都留下较多地接口，有利于将来进行二次开发。

Esext3 for Reducing the Effect of Harboring Bug in File System

One of the most critical threats to the reliability and robustness for file system is harboring bug (silent data corruption). In this research we focus on checksum mismatch since it occurs not only in the user data but also in file system. Our proposed solution has the ability to check this bug in file system of Linux. In our proposed solution there is no need to invoke or revoke checker utility, it comes as the integrated part of file system and has the ability to check upcoming updates before harboring bug make unrecoverable changes that leads significant data loses. Demonstration testing shows satisfactory results in file server and web server environments in terms of less memory consumption and avoidable delay in system’s updating.

iHDFS: A Distributed File System Supporting Incremental Computing

Big data are always processed repeatedly with small changes, which is a major form of big data processing. The feature of incremental change of big data shows that incremental computing mode can improve the performance greatly. HDFS is a distributed file system on Hadoop which is the most popular platform for big data analytics. And HDFS adopts fixed-size chunking policy, which is inefficient facing incremental computing. Therefore, in this paper, we proposed iHDFS (incremental HDFS), a distributed file system, which can provide basic guarantee for big data parallel processing. The iHDFS is implemented as an extension to HDFS. In iHDFS, Rabin fingerprint algorithm is applied to achieve content defined chunking. This policy make data chunking has much higher stability, and the intermediate processing results can be reused efficiently, so the performance of incremental data processing can be improved significantly. The effectiveness and efficiency of iHDFS have been demonstrated by the experimental results.

Secure, Efficient and Searchable File System on Distributed Clouds

Many enterprises and personals are inclining to outsource their data to public clouds, but security and privacy are two critical problems cannot be ignored. The door of cloud provider may be broken, and the data may also be dug into by providers to find valuable information. In this paper, a secure and efficient storage file (SES FS) system is proposed to distribute files in several clouds and allows users to search the files securely and efficiently. In the proposed system, keywords were transformed into integers and secretly shared in a defined finite field, then the shares were mapped to random numbers in specified random domain in each cloud. Files were encrypted with distinct secret key and scattered within different clouds. Information about keyword/file was secretly shared among cloud providers. Legal users can search in the clouds to find correct encrypted files and reconstruct corresponding secret key. No adversary can find or detect the real file information even they can collude all the servers. Manipulation on shares by one or more clouds can be detected with high probability. The system can also detect malicious servers through introduced virtual points. One interesting property for the scheme is that new keywords can be added easily, which is difficult and usually not efficient for many searchable symmetric encryption systems. Detailed experimental result shows, with tolerable uploading delay, the scheme exhibits excellent performance on data retrieving aspect.

Efficient File Accessing Techniques on Hadoop Distributed File Systems

Hadoop framework emerged at the right moment when traditional tools were powerless in terms of handling big data. Hadoop Distributed File System (HDFS) which serves as a highly fault-tolerance distributed file system in Hadoop, can improve the throughput of data access effectively. It is very suitable for the application of handling large amounts of datasets. However, Hadoop has the disadvantage that the memory usage rate in NameNode is so high when processing large amounts of small files that it has become the limit of the whole system. In this paper, we propose an approach to optimize the performance of HDFS with small files. The basic idea is to merge small files into a large one whose size is suitable for a block. Furthermore, indexes are built to meet the requirements for fast access to all files in HDFS. Preliminary experiment results show that our approach achieves better performance.

A Survey of Non-Volatile Main Memory File Systems

Non-volatile memories(NVMs)provide lower latency and higher bandwidth than block devices.Besides,NVMs are byte-addressable and provide persistence that can be used as memory-level storage devices(non-volatile main memory,NVMM).These features change storage hierarchy and allow CPU to access persistent data using load/store instructions.Thus,we can directly build a file system on NVMM.However,traditional file systems are designed based on slow block devices.They use a deep and complex software stack to optimize file system performance.This design results in software overhead being the dominant factor affecting NVMM file systems.Besides,scalability,crash consistency,data protection,and cross-media storage should be reconsidered in NVMM file systems.We survey existing work on optimizing NVMM file systems.First,we analyze the problems when directly using traditional file systems on NVMM,including heavy software overhead,limited scalability,inappropriate consistency guarantee techniques,etc.Second,we summarize the technique of 30 typical NVMM file systems and analyze their advantages and disadvantages.Finally,we provide a few suggestions for designing a high-performance NVMM file system based on real hardware Optane DC persistent memory module.Specifically,we suggest applying various techniques to reduce software overheads,improving the scalability of virtual file system(VFS),adopting highly-concurrent data structures(e.g.,lock and index),using memory protection keys(MPK)for data protection,and carefully designing data placement/migration for cross-media file system.

An efficient wear-leveling-aware multi-grained allocator for persistent memory file systems

Persistent memory(PM)file systems have been developed to achieve high performance by exploiting the advanced features of PMs,including nonvolatility,byte addressability,and dynamic random access memory(DRAM)like performance.Unfortunately,these PMs suffer from limited write endurance.Existing space management strategies of PM file systems can induce a severely unbalanced wear problem,which can damage the underlying PMs quickly.In this paper,we propose a Wear-leveling-aware Multi-grained Allocator,called WMAlloc,to achieve the wear leveling of PMs while improving the performance of file systems.WMAlloc adopts multiple min-heaps to manage the unused space of PMs.Each heap represents an allocation granularity.Then,WMAlloc allocates less-worn blocks from the corresponding min-heap for allocation requests.Moreover,to avoid recursive split and inefficient heap locations in WMAlloc,we further propose a bitmap-based multi-heap tree(BMT)to enhance WMAlloc,namely,WMAlloc-BMT.We implement WMAlloc and WMAlloc-BMT in the Linux kernel based on NOVA,a typical PM file system.Experimental results show that,compared with the original NOVA and dynamic wear-aware range management(DWARM),which is the state-of-the-art wear-leveling-aware allocator of PM file systems,WMAlloc can,respectively,achieve 4.11×and 1.81×maximum write number reduction and 1.02×and 1.64×performance with four workloads on average.Furthermore,WMAlloc-BMT outperforms WMAlloc with 1.08×performance and achieves 1.17×maximum write number reduction with four workloads on average.

NICFS:a file system based on persistent memory and SmartNIC

Emergence of new hardware,including persistent memory and smart network interface card(SmartNIC),has brought new opportunities to file system design.In this paper,we design and implement a new file system named NICFS based on persistent memory and SmartNIC.We divide the file system into two parts:the front end and the back end.In the front end,data writes are appended to the persistent memory in a log-structured way,leveraging the fast persistence advantage of persistent memory.In the back end,the data in logs are fetched,processed,and patched to files in the background,leveraging the processing capacity of SmartNIC.Evaluation results show that NICFS outperforms Ext4 by about 21%/10%and about 19%/50%on large and small reads/writes,respectively.

RCache: A Read-Intensive Workload-Aware Page Cache for NVM Filesystem

Byte-addressable non-volatile memory(NVM),as a new participant in the storage hierarchy,gives extremely high performance in storage,which forces changes to be made on current filesystem designs.Page cache,once a significant mechanism filling the performance gap between Dynamic Random Access Memory(DRAM)and block devices,is now a liability that heavily hinders the writing performance of NVM filesystems.Therefore state-of-the-art NVM filesystems leverage the direct access(DAX)technology to bypass the page cache entirely.However,the DRAM still provides higher bandwidth than NVM,which prevents skewed read workloads from benefiting from a higher bandwidth of the DRAM and leads to sub-optimal performance for the system.In this paper,we propose RCache,a readintensive workload-aware page cache for NVM filesystems.Different from traditional caching mechanisms where all reads go through DRAM,RCache uses a tiered page cache design,including assigning DRAM and NVM to hot and cold data separately,and reading data from both sides.To avoid copying data to DRAM in a critical path,RCache migrates data from NVM to DRAM in a background thread.Additionally,RCache manages data in DRAM in a lock-free manner for better latency and scalability.Evaluations on Intel Optane Data Center(DC)Persistent Memory Modules show that,compared with NOVA,RCache achieves 3 times higher bandwidth for read-intensive workloads and introduces little performance loss for write operations.

FastDu:Efficient Directory Summaries Harvest by Tracking File System Changes

FastDu is a file system service that tracks file system changes by intercepting file system calls to maintain directory summaries, which play important roles in both storage administration and improvement of user experiences for some applications. In most circumstances, directory summaries are independently harvested by applications via traversing the file system hierarchy and calling stat 0 on every file in each directory. For large file systems, this brute-force traverse-based approach can take many hours to complete, even if only a small percentage of the files have changed. This paper describes FastDu, which uses a pre-built database to store harvested directory summaries, and tracks the file system changes by intercept- ing file system calls, so that new harvesting is restricted to the small subset of directories that contain modified files. Tests using FastDu show that this approach reduces the time needed to get a directory summary by one or two orders of magnitude with almost negligible penalty to application-aware file system performance.

A Non-Forced-Write Atomic Commit Protocol for Cluster File Systems

Distributed metadata consistency is one of the critical issues of metadata clusters in distributed file systems. Existing methods to maintain metadata consistency generally need several log forced write operations. Since synchronous disk IO is very inefficient, the average response time of metadata operations is greatly increased. In this paper, an asynchronous atomic commit protocol （ACP） named Dual-Log （DL） is presented. It does not need any log forced write operations. Optimizing for distributed metadata operations involving only two metadata servers, DL mutually records the redo log in counterpart metadata servers by transferring through the low latency network. A crashed metadata server can redo the metadata operation with the redundant redo log. Since the latency of the network is much lower than the latency of disk IO, DL can improve the performance of distributed metadata service significantly. The prototype of DL is implemented based on local journal. The performance is tested by comparing with two widely used protocols, EP and S2PC-MP, and the results show that the average response time of distributed metadata operations is reduced by about 40%-60%, and the recovery time is only I second under 10 thousands uncompleted distributed metadata operations.

A GPU-Accelerated In-Memory Metadata Management Scheme forLarge-Scale Parallel File Systems

Driven by the increasing requirements of high-performance computing applications,supercomputers are prone to containing more and more computing nodes.Applications running on such a large-scale computing system are likely to spawn millions of parallel processes,which usually generate a burst of I/O requests,introducing a great challenge into the metadata management of underlying parallel file systems.The traditional method used to overcome such a challenge is adopting multiple metadata servers in the scale-out manner,which will inevitably confront with serious network and consistence problems.This work instead pursues to enhance the metadata performance in the scale-up manner.Specifically,we propose to improve the performance of each individual metadata server by employing GPU to handle metadata requests in parallel.Our proposal designs a novel metadata server architecture,which employs CPU to interact with file system clients,while offloading the computing tasks about metadata into GPU.To take full advantages of the parallelism existing in GPU,we redesign the in-memory data structure for the name space of file systems.The new data structure can perfectly fit to the memory architecture of GPU,and thus helps to exploit the large number of parallel threads within GPU to serve the bursty metadata requests concurrently.We implement a prototype based on BeeGFS and conduct extensive experiments to evaluate our proposal,and the experimental results demonstrate that our GPU-based solution outperforms the CPU-based scheme by more than 50%under typical metadata operations.The superiority is strengthened further on high concurrent scenarios,e.g.,the high-performance computing systems supporting millions of parallel threads.

BlockHDFS:Blockchain-integrated Hadoop distributed file system for secure provenance traceability

Hadoop Distributed File System(HDFS)is one of the widely used distributed file systems in big data analysis for frameworks such as Hadoop.HDFS allows one to manage large volumes of data using low-cost commodity hardware.However,vulnerabilities in HDFS can be exploited for nefarious activities.This reinforces the importance of ensuring robust security to facilitate file sharing in Hadoop as well as having a trusted mechanism to check the authenticity of shared files.This is the focus of this paper,where we aim to improve the security of HDFS using a blockchain-enabled approach(hereafter referred to as BlockHDFS).Specifically,the proposed BlockHDFS uses the enterprise-level Hyperledger Fabric platform to capitalize on files'metadata for building trusted data security and traceability in HDFS.

用FileSystemObject对象模型操作文件系统

详细介绍了面向对象编程的操作文件系统的文件对象模型(FileSystemObject,以下简称FSO)的对象、属性和方法,并举例说明了该对象模型的应用。实例证明:用FSO对象模型操作文件系统比传统文件操作语句更简单、更方便、更灵活,功能更强大。

An adaptive dynamic feedback load balancing algorithm based on QoS in distributed file system

An adaptive dynamic load balancing algorithm based on QoS is proposed to improve the performance of load balancing in distributed file system,combining the advantages of a variety of load balancing algorithms.The new algorithm uses a tuple containing the number of files and the total file size as the QoS measure for the requested task.The master node sets a threshold for the requested task based on the QoS to filter storage nodes that meet the requirements of the task.In order to guarantee the reliability of the new algorithm,we consider the impact of CPU utilization,memory usage,disk IO occupancy rate,network bandwidth usage and hard disk usage on load balancing performance when calculating the real-time load balancing of storage nodes.The heterogeneity of the network is considered when the master node schedule task assignments to ensure the fairness of the algorithm.The comprehensive evaluation value is determined based the performance load ratio,which is calculated from the real-time load value of the storage node and a performance value after normalization.The master node assigns tasks to the storage node with the highest comprehensive evaluation value.The storage nodes provide adaptive feedback based on changes in the degree of connectivity,rather than periodic update of the load information.The actual distributed file system environment is set up on the server cluster,the performance of the new algorithm is tested through a contrast experiment.The experimental results show that the new algorithm can effectively reduce the average response time of the system,improve throughput,and enable the system load to reach a good balance.