IBM Spectrum Scale Slidecast
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In this slidecast, Jim Gutowski and Scott Fadden from IBM describe the advantages IBM Spectrum Scale Storage brings to the enterprise. IBM Spectrum Scale is a proven, scalable, high-performance data and file management solution (based upon IBM General Parallel File System or GPFS, also formerly known as code name Elastic Storage). IBM Spectrum Scale provides world-class storage management with extreme scalability, flash accelerated performance, and automatic policy-based storage tiering from flash through disk to tape. IBM Spectrum Scale reduces storage costs up to 90% while improving security and management efficiency in cloud, big data & analytics environments." Watch the video presentation starting 4/8/2015: http://wp.me/p3RLHQ-e0a Learn more: http://ibm.co/1y1QIrj
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IBM Spectrum Scale Slidecast
- 1. © 2014 IBM Corporation IBM Spectrum Scale Flash accelerated, software defined storage for cloud, big data & analytics, and technical computing Jim Gutowski & Scott Fadden
- 2. © 2014 IBM Corporation2 SOURCE: *2014 IBM Institute for Business Value Study on Infrastructure Matters; Gartner IT Metrics The top two challenges organizations face with IT infrastructure are storage related – Data Management and Cost Efficiency 2.5 Billion Gigabytes of data per day Data Explosion 90% of data created in last two years Traditional Storage Models are being disrupted by the explosion of data Data Innovation30% lower TCO with Flash 50% lower storage management cost and Hybrid delivery with Software Defined Storage Data Innovation 0.4% overall IT budget growth in 2015 670% more data in 5 years for storage administrators Data Economics
- 3. © 2014 IBM Corporation3 NFS Map Reduce Connector OpenStack Cinder Swift Glance Disk Tape Powered by POSIX Spectrum Scale Shared Nothing Cluster Client workstations Users and applications Compute Farm Single name space Site A Site B Site C Off Premise IBM Spectrum Scale 3 Flash
- 4. © 2014 IBM Corporation4 Spectrum Scale – in Summary Key Capabilities Single global namespace Automated policy-based tiering Enterprise ready Open, POSIX compliant Benefits Improves performance by removing data-related bottlenecks Enhances collaboration across geographies Reduces cost per performance by placing data on most applicable storage Enables sharing with a single file system for multiple applications Up to Yottabytes of data!
- 5. © 2014 IBM Corporation5 Thank you! For more information http://www.ibm.com/systems/storage/spectrum/scale/
- 6. © 2014 IBM Corporation5 Thank you! For more information http://www.ibm.com/systems/storage/spectrum/scale/
Notes de l'éditeur
- RICH: Hi and welcome! This is the “Rich Report” from InsideBigData – and with me today is Jim Gutowski and Scott Fadden from IBM. Welcome guys! JIM: Hi Rich – thanks for having us on the Rich Report! RICH: So, audience – you may have heard about IBM’s recent introduction of the IBM Spectrum Storage family (solution?) – and if you listened to my Rich Report last week, you heard Jay Muelhoefer talk about Software Defined Infrastructure. Well, today, Jim & Scott are going to help us drill down into more detail on IBM Spectrum Scale – which was formerly IBM GPFS (also formerly referred to as Elastic Storage). Rich Q: So Jim – I think we’re all aware of GPFS, but can you explain in a sentence how IBM Spectrum Scale is taking storage to the next level? JIM: Hi Rich - Sure…IBM Spectrum Scale is the latest revision of GPFS – and it’s been more than an HPC solution for a long time! But, most of our customers who speak on our behalf are HPC customers – but what many may not know is that we have many clients and users in the Big Data space – such as Nuance Communications, CitiBank, and Cypress Semiconductor – who have been using our solution for years!
- Jim talks: set the stage for what is happening in the industry today – challenges customers face around Data Explosion, Economics, and Innovation “And now, I’ll turn it over to Scott to explain the Spectrum Scale architecture”….
- SCOTT: Thanks Jim! [Scott give elevator pitch on Spectrum Scale] RICH Q: For large scale HPC, the choice of parallel filesystems seems to always come down to GPFS and Lustre. What are the advantages of GPFS/Spectrum Scale? IBM A: There are other customers who need ILM, integration with tape, tying {....} together - Lustre cannot do that RICH Q: Can Spectrum Scale software be used with existing storage infrastructure from other vendors? IBM A: RICH Q: Does your business model lend itself towards other vendors selling Spectrum Scale on their storage hardware as part of a turnkey solution? IBM A: RICH Q: What are the forces driving technical computing towards object storage? (GUYS – you decide if / how you want to answer) Background notes for this slide: Spectrum Scale delivers High Bandwidth for Large Files Parallel access from all clients to all storage: end to end Parallel NSD protocol enabling Application node(s) to simultaneously access all the NSDs High Bandwidth access to file(s) Wide striping Large block size Parallel access to files from multiple nodes Efficient deep pre-fetching: read ahead, write behind Highly multi-threaded daemon 400GB/s to single file-system Spectrum Scale delivers High file-system IOPS for Small Files and Metadata High file-system IOPS Distributed Metadata Architecture Option to configure dedicated fast disks to store file-system metadata Data for small files stored within the inode thereby reducing disk I/O for small file workloads. Integrated Tiered storage Traditional Storage: Small files require two I/O operations metadata(inode) file-data Spectrum Scale Optimization – Access small files with ONE I/O operation Allow data for small files and directories to be stored in the inode. This has the potential advantages of speeding up directory scans and reducing disk I/O for small file workloads. Supported Inode-Size: 512B, 1KiB, 4KiB
- SCOTT OR JIM: Talk to Support, Services (instant), and proven track record from IBM. Lustre has been un-supported and/or semi-supported by multiple vendors over the years - and what happens when you have a problem with that feature? RICH: Provide positive observation comment before closing.