Ddn Storage
Chief Research Officer
Ibm Almaden Research Center Apr 2017 - Jun 2018
Stsm and Chief Research Strategist Elastic Storage
Ibm Jul 2012 - Apr 2017
Chief Research Strategist Elastic Storage
Ibm Dec 2009 - Jul 2012
Senior Sw Development Architect - Scale-Out Storage
Ibm Jan 2007 - Jan 2008
Development Leader Oesv
Skills:
Storage Nas High Availability Virtualization Cloud Computing Cluster Storage Area Networks Aix San Unix Distributed Systems Shell Scripting Linux Data Center Enterprise Architecture Server Architecture Red Hat Linux Storage Virtualization Perl Network Attached Storage Solaris Operating Systems Disaster Recovery System Architecture Vmware Solution Architecture Redhat Vmware Infrastructure High Performance Computing Rhel Apache Tsm Linux System Administration File Systems Ibm Aix Unix Shell Scripting Tcp/Ip Vmware Esx Netapp Storage Management Enterprise Storage Ldap Storage Area Network Systems Management Nfs
Gregory T. Kishi - Oro Valley AZ, US Sven Oehme - Morgan Hill CA, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 12/16 G06F 17/30
US Classification:
707640, 707E17005
Abstract:
A mechanism is provided for reducing the backup time of data files from a memory. Data files are pre-staged by identifying the data files in the memory to be backed up to a backup storage system, sorting the data files by size thereby forming a set of small data files and a set of large data files, and copying the set of small data files to a cache while leaving the set of large data files in a disk subsystem. The set of small data files are then backed-up from the cache and the set of large data files are backed-up from the disk subsystem to a backup storage system. Thus, the time required to backup the set of small data files from the cache is performed at a faster rate as compared to backing up the set of large data files from the disk subsystem.
International Business Machines Corporation - Armonk NY, US Sven Oehme - Morgan Hill CA, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06F 17/30
US Classification:
707654
Abstract:
A mechanism is provided for reducing the backup time of data files from a memory. Data files are pre-staged by identifying the data files in the memory to be backed up to a backup storage system, sorting the data files by size thereby forming a set of small data files and a set of large data files, and copying the set of small data files to a cache while leaving the set of large data files in a disk subsystem. The set of small data files are then backed-up from the cache and the set of large data files are backed-up from the disk subsystem to a backup storage system. Thus, the time required to backup the set of small data files from the cache is performed at a faster rate as compared to backing up the set of large data files from the disk subsystem.
Highly Scalable And Distributed Data Sharing And Storage
Sven Oehme - San Jose CA, US Marc Thadeus Roskow - Los Gatos CA, US Stephen Leonard Schwartz - Tucson AZ, US Anna W. Topol - Jefferson Valley NY, US Daniel James Winarski - Tucson AZ, US
Assignee:
INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY
International Classification:
G06F 15/167
US Classification:
709213
Abstract:
Embodiments of the disclosure relate to storing and sharing data in a scalable distributed storing system using parallel file systems. An exemplary embodiment may comprise a network, a storage node coupled to the network for storing data, a plurality of application nodes in device and system modalities coupled to the network, and a parallel file structure disposed across the storage node and the application nodes to allow data storage, access and sharing through the parallel file structure. Other embodiments may comprise interface nodes for accessing data through various file access protocols, a storage management node for managing and archiving data, and a system management node for managing nodes in the system.
Policy-Based, Multi-Scheme Data Reduction For Computer Memory
- ARMONK NY, US Marc A. Kaplan - Lake Worth FL, US Leo Luan - Saratoga CA, US Sven Oehme - Morgan Hill CA, US Wayne A. Sawdon - San Jose CA, US Frank B. Schmuck - Campbell CA, US
International Classification:
G06F 17/30
Abstract:
Embodiments relate to policy-based, multi-scheme data reduction for a computer memory. An aspect includes receiving a plurality of policy rules by a policy engine of the computer memory, wherein a first policy rule specifies applying a first data reduction scheme to data in the computer memory based on the data matching first characteristics, wherein a second policy rule specifies applying a second data reduction scheme to data in the computer memory based on the data matching second characteristics, wherein the first data reduction scheme is different from the second data reduction scheme. Another aspect includes determining, by the policy engine, that first data in the computer memory matches the first characteristics, and that second data in the computer memory matches the second characteristics. Yet another aspect includes applying the first data reduction scheme to the first data, and applying the second data reduction scheme to the second data.
Dynamically Managing A High Speed Storage Tier Of A Data Storage System
- Armonk NY, US Marc A. Kaplan - Bethel CT, US Sven Oehme - Morgan Hill CA, US Wayne A. Sawdon - San Jose CA, US
International Classification:
G06F 17/30
Abstract:
A computer-implemented method according to one embodiment includes identifying an event associated with a high speed storage tier of a data storage system, determining a policy rule that is triggered in response to the event, and implementing one or more data management actions associated with the high speed storage tier according to the policy rule.
Youtube
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