Directory-Based Metadata Optimizations for Small Files in PVFS

In today's file systems each file is made up of data and metadata. The metadata contains some information about the associated data, like ownership and permissions of the file. While this usually is useful, there are situations when the additional overhead of such a design becomes a problem in terms of performance. This is especially true for cluster file systems, because due to their design every metadata operation is even more expensive. If a user creates several thousand temporary files that are going to be deleted soon anyway, it is not necessary to store detailed information about them. In this thesis several changes are made to the parallel cluster file system PVFS to better deal with such cases. To do this, PVFS is altered such that certain unnecessary metadata is discarded and therefore metadata performance is increased. Several tests with a large quantity of files are done to measure the benefits of these changes. The reduction of the metadata overhead halves the time needed for some common file system operations. The speedup of those operations is also analyzed in detail by visualizing the internal workflow of PVFS. Also, additional work that could be done to further increase the metadata performance as well as possible additions to the actual implementation are presented

Optimizations for Energy-Aware, High-Performance and Reliable Distributed Storage Systems

With the decreasing cost and wide-spread use of commodity hard drives, it has become possible to create very large-scale storage systems with less expense. However, as we approach exabyte-scale storage systems, maintaining important features such as energy-efficiency, performance, reliability and usability became increasingly difficult. Despite the decreasing cost of storage systems, the energy consumption of these systems still needs to be addressed in order to retain cost-effectiveness. Any improvements in a storage system can be outweighed by high energy costs. On the other hand, large-scale storage systems can benefit more from the object storage features for improved performance and usability. One area of concern is metadata performance bottleneck of applications reading large directories or creating a large number of files. Similarly, computation on big data where data needs to be transferred between compute and storage clusters adversely affects I/O performance. As the storage systems become more complex and larger, transferring data between remote compute and storage tiers becomes impractical. Furthermore, storage systems implement reliability typically at the file system or client level. This approach might not always be practical in terms of performance. Lastly, object storage features are usually tailored to specific use cases that makes it harder to use them in various contexts. In this thesis, we are presenting several approaches to enhance energy-efficiency, performance, reliability and usability of large-scale storage systems. To begin with, we improve the energy-efficiency of storage systems by moving I/O load to a subset of the storage nodes with energy-aware node allocation methods and turn off the unused nodes, while preserving load balance on demand. To address the metadata performance issue associated with large creates and directory reads, we represent directories with object storage collections and implement lazy creation of objects. Similarly, in-situ computation on large-scale data is enabled by using object storage features to integrate a computational framework with the existing object storage layer to eliminate the need to transfer data between compute and storage silos for better performance. We then present parity-based redundancy using object storage features to achieve reliability with less performance impact. Finally, unified storage brings together the object storage features to meet the needs of distinct use cases; such as cloud storage, big data or high-performance computing to alleviate the unnecessary fragmentation of storage resources. We evaluate each proposed approach thoroughly and validate their effectiveness in terms of improving energy-efficiency, performance, reliability and usability of a large-scale storage system

Metadata And Data Management In High Performance File And Storage Systems

With the advent of emerging e-Science applications, today\u27s scientific research increasingly relies on petascale-and-beyond computing over large data sets of the same magnitude. While the computational power of supercomputers has recently entered the era of petascale, the performance of their storage system is far lagged behind by many orders of magnitude. This places an imperative demand on revolutionizing their underlying I/O systems, on which the management of both metadata and data is deemed to have significant performance implications. Prefetching/caching and data locality awareness optimizations, as conventional and effective management techniques for metadata and data I/O performance enhancement, still play their crucial roles in current parallel and distributed file systems. In this study, we examine the limitations of existing prefetching/caching techniques and explore the untapped potentials of data locality optimization techniques in the new era of petascale computing. For metadata I/O access, we propose a novel weighted-graph-based prefetching technique, built on both direct and indirect successor relationship, to reap performance benefit from prefetching specifically for clustered metadata serversan arrangement envisioned necessary for petabyte scale distributed storage systems. For data I/O access, we design and implement Segment-structured On-disk data Grouping and Prefetching (SOGP), a combined prefetching and data placement technique to boost the local data read performance for parallel file systems, especially for those applications with partially overlapped access patterns. One high-performance local I/O software package in SOGP work for Parallel Virtual File System in the number of about 2000 C lines was released to Argonne National Laboratory in 2007 for potential integration into the production mode

Using file system virtualization to avoid metadata bottlenecks

Abstract—Parallel file systems are very sensitive to adverse conditions, and the lack of synergy between such file systems and some of the applications running on them has a negative impact on the overall system performance. Our observations indicate that the increased pressure on metadata management is one of the relevant causes of performance drops. This paper proposes a virtualization layer above the native file system that, transparently to the user, reorganizes the underlying directory tree, mitigating bottlenecks by taking advantage of the native file system optimizations and limiting the effects of potentially harmful application behavior. We developed COFS (COmposite File System) as a proof-of-concept virtual layer to evaluate the feasibility of the proposal.Peer ReviewedPostprint (published version

A Study of Client-based Caching for Parallel I/O

The trend in parallel computing toward large-scale cluster computers running thousands of cooperating processes per application has led to an I/O bottleneck that has only gotten more severe as the the number of processing cores per CPU has increased. Current parallel file systems are able to provide high bandwidth file access for large contiguous file region accesses; however, applications repeatedly accessing small file regions on unaligned file region boundaries continue to experience poor I/O throughput due to the high overhead associated with accessing parallel file system data. In this dissertation we demonstrate how client-side file data caching can improve parallel file system throughput for applications performing frequent small and unaligned file I/O. We explore the impacts of cache page size and cache capacity using the popular FLASH I/O benchmark and explore a novel cache sharing approach that leverages the trend toward multi-core processors. We also explore a technique we call progressive page caching that represents cache data using dynamic data structures rather than fixed-size pages of file data. Finally, we explore a cache aggregation scheme that leverages the high-level file I/O interfaces provided by the PVFS file system to provide further performance enhancements. In summary, our results indicate that a correctly configured middleware-based file data cache can dramatically improve the performance of I/O workloads dominated by small unaligned file accesses. Further, we demonstrate that a well designed cache can offer stable performance even when the selected cache page granularity is not well matched to the provided workload. Finally, we have shown that high-level file system interfaces can significantly accelerate application performance, and interfaces beyond those currently envisioned by the MPI-IO standard could provide further performance benefits

GekkoFS: A temporary burst buffer file system for HPC applications

Many scientific fields increasingly use high-performance computing (HPC) to process and analyze massive amounts of experimental data while storage systems in today’s HPC environments have to cope with new access patterns. These patterns include many metadata operations, small I/O requests, or randomized file I/O, while general-purpose parallel file systems have been optimized for sequential shared access to large files. Burst buffer file systems create a separate file system that applications can use to store temporary data. They aggregate node-local storage available within the compute nodes or use dedicated SSD clusters and offer a peak bandwidth higher than that of the backend parallel file system without interfering with it. However, burst buffer file systems typically offer many features that a scientific application, running in isolation for a limited amount of time, does not require. We present GekkoFS, a temporary, highly-scalable file system which has been specifically optimized for the aforementioned use cases. GekkoFS provides relaxed POSIX semantics which only offers features which are actually required by most (not all) applications. GekkoFS is, therefore, able to provide scalable I/O performance and reaches millions of metadata operations already for a small number of nodes, significantly outperforming the capabilities of common parallel file systems.Peer ReviewedPostprint (author's final draft

Astro - A Low-Cost, Low-Power Cluster for CPU-GPU Hybrid Computing using the Jetson TK1

With the rising costs of large scale distributed systems many researchers have began looking at utilizing low power architectures for clusters. In this paper, we describe our Astro cluster, which consists of 46 NVIDIA Jetson TK1 nodes each equipped with an ARM Cortex A15 CPU, 192 core Kepler GPU, 2 GB of RAM, and 16 GB of flash storage. The cluster has a number of advantages when compared to conventional clusters including lower power usage, ambient cooling, shared memory between the CPU and GPU, and affordability. The cluster is built using commodity hardware and can be setup for relatively low costs while providing up to 190 single precision GFLOPS of computing power per node due to its combined GPU/CPU architecture. The cluster currently uses one 48-port Gigabit Ethernet switch and runs Linux for Tegra, a modified version of Ubuntu provided by NVIDIA as its operating system. Common file systems such as PVFS, Ceph, and NFS are supported by the cluster and benchmarks such as HPL, LAPACK, and LAMMPS are used to evaluate the system. At peak performance, the cluster is able to produce 328 GFLOPS of double precision and a peak of 810W using the LINPACK benchmark placing the cluster at 324th place on the Green500. Single precision benchmarks result in a peak performance of 6800 GFLOPs. The Astro cluster aims to be a proof-of-concept for future low power clusters that utilize a similar architecture. The cluster is installed with many of the same applications used by top supercomputers and is validated using the several standard supercomputing benchmarks. We show that with the rise of low-power CPUs and GPUs, and the need for lower server costs, this cluster provides insight into how ARM and CPU-GPU hybrid chips will perform in high-performance computing