Analyzing Metadata Performance in Distributed File Systems

Distributed file systems are important building blocks in modern computing environments. The challenge of increasing I/O bandwidth to files has been largely resolved by the use of parallel file systems and sufficient hardware. However, determining the best means by which to manage large amounts of metadata, which contains information about files and directories stored in a distributed file system, has proved a more difficult challenge. The objective of this thesis is to analyze the role of metadata and present past and current implementations and access semantics. Understanding the development of the current file system interfaces and functionality is a key to understanding their performance limitations. Based on this analysis, a distributed metadata benchmark termed DMetabench is presented. DMetabench significantly improves on existing benchmarks and allows stress on metadata operations in a distributed file system in a parallelized manner. Both intranode and inter-node parallelity, current trends in computer architecture, can be explicitly tested with DMetabench. This is due to the fact that a distributed file system can have different semantics inside a client node rather than semantics between multiple nodes. As measurements in larger distributed environments may exhibit performance artifacts difficult to explain by reference to average numbers, DMetabench uses a time-logging technique to record time-related changes in the performance of metadata operations and also protocols additional details of the runtime environment for post-benchmark analysis. Using the large production file systems at the Leibniz Supercomputing Center (LRZ) in Munich, the functionality of DMetabench is evaluated by means of measurements on different distributed file systems. The results not only demonstrate the effectiveness of the methods proposed but also provide unique insight into the current state of metadata performance in modern file systems

ElasTraS: An Elastic Transactional Data Store in the Cloud

Over the last couple of years, "Cloud Computing" or "Elastic Computing" has emerged as a compelling and successful paradigm for internet scale computing. One of the major contributing factors to this success is the elasticity of resources. In spite of the elasticity provided by the infrastructure and the scalable design of the applications, the elephant (or the underlying database), which drives most of these web-based applications, is not very elastic and scalable, and hence limits scalability. In this paper, we propose ElasTraS which addresses this issue of scalability and elasticity of the data store in a cloud computing environment to leverage from the elastic nature of the underlying infrastructure, while providing scalable transactional data access. This paper aims at providing the design of a system in progress, highlighting the major design choices, analyzing the different guarantees provided by the system, and identifying several important challenges for the research community striving for computing in the cloud.Comment: 5 Pages, In Proc. of USENIX HotCloud 200

Scientific Computing Meets Big Data Technology: An Astronomy Use Case

Scientific analyses commonly compose multiple single-process programs into a dataflow. An end-to-end dataflow of single-process programs is known as a many-task application. Typically, tools from the HPC software stack are used to parallelize these analyses. In this work, we investigate an alternate approach that uses Apache Spark -- a modern big data platform -- to parallelize many-task applications. We present Kira, a flexible and distributed astronomy image processing toolkit using Apache Spark. We then use the Kira toolkit to implement a Source Extractor application for astronomy images, called Kira SE. With Kira SE as the use case, we study the programming flexibility, dataflow richness, scheduling capacity and performance of Apache Spark running on the EC2 cloud. By exploiting data locality, Kira SE achieves a 2.5x speedup over an equivalent C program when analyzing a 1TB dataset using 512 cores on the Amazon EC2 cloud. Furthermore, we show that by leveraging software originally designed for big data infrastructure, Kira SE achieves competitive performance to the C implementation running on the NERSC Edison supercomputer. Our experience with Kira indicates that emerging Big Data platforms such as Apache Spark are a performant alternative for many-task scientific applications