Erasure Coding
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IBM Spectrum Scale Erasure Code Edition: Planning and Implementation Guide
This IBM® Redpaper introduces the IBM Spectrum® Scale Erasure Code Edition (ECE) as a scalable, high-performance data and file management solution. ECE is designed to run on any commodity server that meets the ECE minimum hardware requirements. ECE provides all the functionality, reliability, scalability, and performance of IBM Spectrum Scale with the added benefit of network-dispersed IBM Spectrum Scale RAID, which provides data protection, storage efficiency, and the ability to manage storage in hyperscale environments that are composed from commodity hardware. In this publication, we explain the benefits of ECE and the use cases where we believe it fits best. We also provide a technical introduction to IBM Spectrum Scale RAID. Next, we explain the key aspects of planning an installation, provide an example of an installation scenario, and describe the key aspects of day-to-day management and a process for problem determination. We conclude with an overview of possible enhancements that are being considered for future versions of IBM Spectrum Scale Erasure Code Edition. Overall knowledge of IBM Spectrum Scale Erasure Code Edition is critical to planning a successful storage system deployment. This paper is targeted toward technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for delivering cost effective storage solutions. The goal of this paper is to describe the benefits of using IBM Spectrum Scale Erasure Code Edition for the creation of high performing storage systems.
High Performance Computing
This book constitutes revised selected papers from 10 workshops that were held as the ISC High Performance 2017 conference in Frankfurt, Germany, in June 2017. The 59 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They stem from the following workshops: Workshop on Virtualization in High-Performance Cloud Computing (VHPC) Visualization at Scale: Deployment Case Studies and Experience Reports International Workshop on Performance Portable Programming Models for Accelerators (P^3MA) OpenPOWER for HPC (IWOPH) International Workshop on Data Reduction for Big Scientific Data (DRBSD) International Workshop on Communication Architectures for HPC, Big Data, Deep Learning and Clouds at Extreme Scale Workshop on HPC Computing in a Post Moore's Law World (HCPM) HPC I/O in the Data Center ( HPC-IODC) Workshop on Performance and Scalability of Storage Systems (WOPSSS) IXPUG: Experiences on Intel Knights Landing at the One Year Mark International Workshop on Communication Architectures for HPC, Big Data, Deep Learning and Clouds at Extreme Scale (ExaComm)
Data Deduplication Approaches
In the age of data science, the rapidly increasing amount of data is a major concern in numerous applications of computing operations and data storage. Duplicated data or redundant data is a main challenge in the field of data science research. Data Deduplication Approaches: Concepts, Strategies, and Challenges shows readers the various methods that can be used to eliminate multiple copies of the same files as well as duplicated segments or chunks of data within the associated files. Due to ever-increasing data duplication, its deduplication has become an especially useful field of research for storage environments, in particular persistent data storage. Data Deduplication Approaches provides readers with an overview of the concepts and background of data deduplication approaches, then proceeds to demonstrate in technical detail the strategies and challenges of real-time implementations of handling big data, data science, data backup, and recovery. The book also includes future research directions, case studies, and real-world applications of data deduplication, focusing on reduced storage, backup, recovery, and reliability. - Includes data deduplication methods for a wide variety of applications - Includes concepts and implementation strategies that will help the reader to use the suggested methods - Provides a robust set of methods that will help readers to appropriately and judiciously use the suitable methods for their applications - Focuses on reduced storage, backup, recovery, and reliability, which are the most important aspects of implementing data deduplication approaches - Includes case studies