An enhanced journaling method for clustered file system with shared storage

The world keeps contributing to the increase in data everyday drastically. Scientific applications, weather forecasting, researches, hospitals, military services are few such major contributors. As the amount of data increases, the need to provide efficient, easy to use solutions has become one of the main issues for these type of computations. The best solution to this issue is the use of Distributed File Systems(DFS). Some existing Distributed File Systems are too complex to deploy and maintain, although they are extremely scalable and cheap since they can be entirely built out of commodity Operating System(OS) and hardware. GlusterFS solves this problem. Gluster File System(GFS) is open source and is capable of scaling to several petabytes(upto 72 brontobytes) and handling thousands of clients. It is based on a stackable user space design and can deliver exceptional performance for diverse workloads. GlusterFS is an easy to use clustered file system that meets enterprise-level requirements. It is written in user space which uses FUSE(Filesystem in user space) to hook itself with the VFS layer. It takes a layered approach to the file system, where features are added/removed as per the requirement. Enforcer is a major component in Gluster which finds its extensive use in production. Enforcer is concerned more about limits. It helps to restrict the usage(both in terms of size and count) at directory or volume level in the file system. In this paper, we discuss the internal working of the translators and key architectural components of GlusterFS. Along with that, one can gain a deep insight about the enforcement techniques, few challenges, undergoing researches and projects of GlusterFS

Building a Portable File System for Heterogeneous Clusters

AbstractpCFS is a highly available parallel, symmetrical (where nodes perform both compute and I/O work) cluster file system that we have designed to run in medium-sized clusters. In this paper, using exactly the same hardware and Linux version across all nodes we compare pCFS with two distinct configurations of PVFS: one using internal disks, and therefore not able to provide any tolerance against disk and/or I/O node failures, and another where PVFS I/O servers access LUNs in a disk array and thus provide high availability (in the following named HA-PVFS). We start by measuring I/O bandwidth and CPU consumption of PVFS and HA-PVFS setups; then, the same set of tests is performed with pCFS. We conclude that, when using the same hardware, pCFS compares very favourably with HA-PVFS, offering the same or higher I/O bandwidths at a much lower CPU consumption.KeywordsShared-disk cluster file systemsperformanceparallel file systems

Data availability is a challenging issue for large- scale cluster file systems built upon thousands of individual storage devices. Replication is a well-known solution used to improve data availability. However, how to efficiently guarantee replicas consistency under concurrent conflict mutations remains a challenge. Moreover, how to quickly recover replica consistency from a storage server crash or storage device failure is also a tough problem. In this paper, we present a replication-based data availability mechanism designed for a large-scale cluster file system prototype named LionFS. Unlike other replicated storage systems that serialize replica updates, LionFS introduces a relaxed consistency model to enable concurrent updating all replicas for a mutation operation, greatly reducing the latency of operations. LionFS ensures replica consistency if applications use file locks to synchronize the concurrent conflict mutations. Another novelty of this mechanism is its light-weight log, which only records failed mutations and imposes no overhead on failure-free execution and low overhead when some storage devices are unavailable. Furthermore, recovery of replica consistency needs not stop the file system services and running applications. Performance evaluation shows that our solution achieves 50-70% higher write performance than serial replica updates. The logging overhead is shown to be low, and the recovery time is proportional to the amount of data written during the failure.

CFS is a massively parallel distributed file system used for large scale cluster computing and mass storage. It is a global file system sharing among all cluster nodes. In this paper, based on implementation experience and measurements, we will present an evaluation of design alternatives to a cluster file system CFS which is targeted for In spur AS3000 and the performance of the file system.

Nowadays many clustered file systems adopt asynchronous encoding which transforms replicated data into erasure coding to maintain data availability with bounded storage overhead. Existing implementations of asynchronous encoding construct coding stripes with logically sequential data blocks, which suffers from heavy cross-rack traffic and necessitates data block redistribution. Recent work [12] solves this problem by carefully distributing replicated data blocks among racks at the time when they are being written, but it is not applicable to the cases when existing systems have different data layouts or the data layout changes. In this paper, we propose Dynamic Stripe Construction (DSC) to transform N-way replication to erasure coding. DSC does not induce to any cross-rack traffic for encoding, and it does not require data block redistribution after encoding. Besides, DSC is general enough to be applied to any existing CFSes with various erasure codes, and it can also be deployed on a distributed file system in a hot-plugging-in manner. To validate the effectiveness of DSC, we implement it on HDFS. Through extensive testbed experiments in a real storage cluster, we show that DSC can significantly increase the encoding throughput and reduce the foreground user response time over the traditional approach.

MapReduce is increasingly becoming a popular framework, and a potent programming model. The most popular open source implementation of MapReduce, Hadoop, is based on the Hadoop Distributed File System (HDFS). However, as HDFS is not POSIX compliant, it cannot be fully leveraged by applications running on a majority of existing HPC environments such as Teragrid and NERSC. These HPC environments typically support globally shared file systems such as NFS and GPFS. On such resourceful HPC infrastructures, the use of Hadoop not only creates compatibility issues, but also affects overall performance due to the added overhead of the HDFS. This paper not only presents a MapReduce implementation directly suitable for HPC environments, but also exposes the design choices for better performance gains in those settings. By leveraging inherent distributed file systems' functions, and abstracting them away from its MapReduce framework, MARIANE (MApReduce Implementation Adapted for HPC Environments) not only allows for the use of the model in an expanding number of HPC environments, but also allows for better performance in such settings. This paper shows the applicability and high performance of the MapReduce paradigm through MARIANE, an implementation designed for clustered and shared-disk file systems and as such not dedicated to a specific MapReduce solution. The paper identifies the components and trade-offs necessary for this model, and quantifies the performance gains exhibited by our approach in distributed environments over Apache Hadoop in a data intensive setting, on the Magellan test bed at the National Energy Research Scientific Computing Center (NERSC).

We introduce the design and implementation of DCFS (Dawning Cluster File System), a cluster file system developed for the Dawning4000 LINUX clusters. DCFS provides the single name space and binary compatibility with local file system for users and applications on Dawning4000. In addition, it improves system manageability for administrators by embedded file system management considerations into DCFS implementation and provides an easy use interface. Important features of DCFS are presented, including a global management domain, a communication abstraction layer, pipelined file I/O, enhanced multitask capability in storage servers, and fault-tolerance strategy.

A file system is used to manage data on storage media. The FAT (File Allocation Table) file system was originally designed for floppy drives that were less than 500KB in size, and these drives were not capable of fast random reads and writes. FAT has been adapted to work on other types of storage devices since, and it is still widely used today. It is the standard file system used by microprocessors and embedded devices with constrained resources. Micro-controllers, like the Arduino, only officially support the FAT file system when interacting with a SD card. FAT performs well when data is read or written sequentially, but when data is read or written randomly, there is an impact on performance for large files on page based flash devices that cannot utilize caching strategies. Applications that perform random reading and writing are impacted by this architectural issue. For example, flash data structures, like a B-tree, will have poor performance since random reading is utilized to look up values. TEFS (Tiny Embedded File System) uses a simplified tree indexing structure to take advantage of the fast random reads and writes of flash storage and guarantees that the number of page reads and writes will stay constant as the file size increases when randomly reading or writing. Experimental results show that TEFS has significantly better performance than FAT on the Arduino for random I/Os, and the more efficient TEFS page interface is even slightly faster than FAT for sequential reading and writing.

We present part of our recent work on performance enhancement of cluster file systems using shared disks over a SAN. This work is built around the proposal of pCFS, a file system specifically targeting those environments. In we presented the objectives and design principles of pCFS and a proof-of-concept implementation, carried out by modifying Red Hatpsilas GFS , showing significant improvements in operations over files shared among processes running in different nodes. pCFS differs from GFS in two main aspects: its use of cooperative caching and a finer grain of locking. The first aspect, which used the LAN to enhance performance in write sharing situations, was described elsewhere ; we now introduce a complementary strategy - locking file regions instead of the whole file - which enables us to use the SAN while delivering a high level of performance in those same write sharing situations. pCFS may apply inter-node locks to regions, allowing processes to operate in parallel with a minimum of coherency overhead among nodes; a process cannot access outside its region(s) and, when a writer unlocks a region, others can then lock it and be able to see modified data immediately. Through a set of experiments where a file is shared between processes running in different nodes, we show that the described approach allows a gain of, at least, an order of magnitude over plain GFS.

This paper presents the design and implementation of the Mobile File and Adaptation System (MFAS), a system to provide efficient data access mechanisms over variable quality of service networks. MFAS supports fine-grained file caching and consistency, and provides a mechanism for mobility-aware applications to adapt the caching and consistency behavior as network conditions change. Over low-bandwidth links, MFAS shows substantial performance gains over typical network file systems by sending only the modified portions of files. To our knowledge, MFAS is the first system that efficiently supports application-directed consistency policies at the subfile level.

Efficacy and Safety of Endoscopic Retrograde Cholangiopancreatography in Children of Pancreaticobiliary Maljunction Without Obvious Biliary Dilatation

We describe the design and the implementation of the CFS (Cluster File System) storage system which is dedicated to video streams. Our goal is to provide a system with the following features: 1) High number of supported streams at a low cost. 2) Transparent management with respect to the clients. 3) Reliability regarding data storage and service continuity. The CFS is implemented on a cluster of PCs connected with a high speed internal network. Its management is fully distributed among the cluster nodes so that there is no central component. Data are stored and retrieved among the cluster nodes using a Streaming RAID strategy, to enhance reliability. We evaluate the overhead introduced by the CFS management with respect to disk performance, by varying the number of nodes involved in file storage, the block size and the reliability level. The impact of the global server load on this overhead is moreover discussed to demonstrate scalability.

To balance performance and storage efficiency, modern clustered file systems often first store data with replication, followed by encoding the replicated data with erasure coding. We argue that the commonly used random replication does not take into account erasure coding in its design, thereby raising both performance and availability issues in the subsequent encoding operation. We propose encoding-aware replication, which carefully places the replicas so as to (i) eliminate cross-rack downloads of data blocks during the encoding operation, (ii) preserve availability without data relocation after the encoding operation, and (iii) maintain load balancing across replicas as in random replication before the encoding operation. We conduct extensive HDFS-based testbed experiments and discrete-event simulations, and demonstrate the performance gains of encoding-aware replication over random replication.

Parallel and distributed file systems are widely used to provide high throughput in high-performance computing and Cloud computing systems. To increase the parallelism, I/O requests are partitioned into multiple sub-requests (or `flows') and distributed across different data nodes. The performance of file systems is extremely poor if data nodes have highly unbalanced response time. Client-side caching offers a promising direction for addressing this issue. However, current work has primarily used client-side memory as a read cache and employed a write-through policy which requires synchronous update for every write and significantly under-utilizes the client-side cache when the applications are write-intensive. Realizing that the cost of an I/O request depends on the struggler sub-requests, we propose a cost-aware client-side file caching (CCFC) strategy, that is designed to cache the sub-requests with high I/O cost on the client end. This caching policy enables a new trade-off across write performance, consistency guarantee and cache size dimensions. Using benchmark workloads MADbench2, we evaluate our new cache policy alongside conventional write-through. We find that the proposed CCFC strategy can achieve up to 110% throughput improvement compared to the conventional write-through policies with the same cache size on an 85-node cluster.