As much I have tested veeam b&r software for backup and recovery of linux systems using IBM spectrum scale (GPFS), the backup process of volume (using either mount point or device) is successful but it's recovery fails and makes the volume unreadable, even dismount the volume.
- Create a single mount point /U01 for these 2 LUN and did data replication between these LUN using IBM spectrum scale (GPFS v.5.1.1.0). The /U01 now shows 2 TB size (actual 1 TB data which is replicated between the 2 underline LUN)
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IBM Spectrum Scale is a high performance, highly available, clustered file system and associated management software, available on a variety of platforms. IBM Spectrum Scale can scale in several dimensions, including performance (bandwidth and IOPS), capacity and number of nodes* (instances) that can mount the file system. IBM Spectrum Scale addresses the needs of applications whose performance (or performance-to-capacity ratio) demands cannot be met by traditional scale-up storage systems; and IBM Spectrum Scale is therefore deployed for many I/O-demanding enterprise applications that require high performance or scale. IBM Spectrum Scale provides various configuration options, access methods (including traditional POSIX-based file access), and many features such as snapshots, compression, and encryption. Note that IBM Spectrum Scale is not itself an application in the traditional sense, but instead provides the storage infrastructure for applications.
Lenovo DSS-G with storage enclosures supports online enclosure expansion. This enables a customer to grow the number of enclosures in an existing DSS-G building block without bringing down the file system, maximizing flexibility to scale storage capacity based on need.
Spectrum Scale Erasure Code Edition brings the IBM Spectrum Scale RAID functionality to the next level, supporting the creation of scale-out network-dispersed Spectrum Scale clusters on storage rich servers. You get the same benefits of IBM Spectrum Scale and IBM Spectrum Scale RAID without the need for storage enclosures:
IBM Spectrum Scale is a cluster file system that provides concurrent access to one or more file systems from multiple nodes. The nodes can be SAN-attached, network-attached, a mixture of SAN-attached and network-attached, or in a shared-nothing cluster configuration. Spectrum Scale enables high-performance access to a common set of data to support a scale-out solution or to provide a high-availability platform.
To help differentiate between the varying severities of autism, the Autism Spectrum Scale is used. This diagnostic tool is used to assess and diagnose ASD in adults and children. There are three functional levels of autism based on the scale. These scores are based on the level of support a person needs to function in daily life and the level of impairment.
Level 3 of the scale requires very substantial support to function in their daily life. They have severe deficits in social communication and interaction, including initiating and maintaining relationships, and they have limited or no ability to communicate, either verbally or nonverbally. They may also engage in intense repetitive behaviours or have specific interests that significantly interfere with their ability to function independently. They require support in all areas of their life, including self-care, and they may have difficulty adapting to new situations.
It is important to note that the autism spectrum scale is not a measure of intelligence or cognitive ability. Individuals with autism can have a wide range of cognitive abilities, from intellectual disability to exceptional intelligence. The scale is simply a way to categorise individuals based on their level of impairment and the level of support they require.
IBM Spectrum Scale is a cluster file system that provides concurrent access to a single file system or set of file systems from multiple nodes. The nodes can be SAN attached, network attached, a mixture of SAN attached and network attached, or in a shared nothing cluster configuration. This enables high performance access to this common set of data to support a scale-out solution or to provide a high availability platform.
We propose a novel framework to characterize the thermalization of many-body dynamical systems close to integrable limits using the scaling properties of the full Lyapunov spectrum. We use a classical unitary map model to investigate macroscopic weakly nonintegrable dynamics beyond the limits set by the KAM regime. We perform our analysis in two fundamentally distinct long-range and short-range integrable limits which stem from the type of nonintegrable perturbations. Long-range limits result in a single parameter scaling of the Lyapunov spectrum, with the inverse largest Lyapunov exponent being the only diverging control parameter and the rescaled spectrum approaching an analytical function. Short-range limits result in a dramatic slowing down of thermalization which manifests through the rescaled Lyapunov spectrum approaching a non-analytic function. An additional diverging length scale controls the exponential suppression of all Lyapunov exponents relative to the largest one.
Renormalized Lyapunov spectrum {} against the rescaled index for SRN in log scale (corresponding to the parameters and data of Fig. 3). The dashed line is to guide the eye for the fit of exponential decay.
Compass software-as-a-service delivery eliminates the traditional burdens associated with infrastructure solutions. It also lets organizations scale as they grow, stay compliant with licensing, and reduce cost of acquisition and use.
Although 5G has dramatically improved network capacity and spectrum efficiency (SE), the explosive growth of Internet of Things (IoT) demands for more spectrum and energy resources to support high device density and massive traffics. It is estimated that at least 5.2 GHz bandwidth is required for just eHealth Care IoT if spectrum is accessed exclusively, or 1.3 GHz even with dynamic sharing strategy. It is clear that shortage of spectrum resources is a major bottleneck for the success of IoT popularity. On the other hand, current IoT devices use standards such as Bluetooth, LoRA, Sigfox, narrow-band IoT (NB-IoT), or Zigbee, which require power-hungry active radio frequency components like oscillators and converters. Battery-driven IoT devices can hardly sustain years of life-cycle goal even with infrequent transmission and optimized low-power protocols. Thus, sustainable energy consumption is another challenge. With tens of billions of IoTs desire for connectivity by 2030, there is a pressing need to address both SE and energy efficiency (EE) challenges to accommodate for such densified IoT networks. This research seeks to improve SE and EE performance while providing guaranteed quality of service (QoS) for IoTs at large-scale, thereby providing a feasible and practical connectivity solution in massive IoT era. Outcomes from this project can bring following impacts: 1) a hybrid and cooperative communication architect for IoTs, which combines benefits from both active and passive mode; 2) integration of research and curriculum design, capstone projects to both undergraduate and graduate students; 3) cutting-edge research experiences to a primarily undergraduate institution (PUI).
The core approach is to enable IoT device with a wireless-powered hybrid communication structure that can not only minimize energy footprint with energy harvesting from ambient signals, but also integrate coordinated passive and active communication to support versatile QoS needs with efficient spectrum utilization through user cooperation. This project offers a holistic solution to deliver following innovations. 1) A novel PHY transmission architect. It combines a bio-inspired symbiotic radio to coordinate excessive interference. Optimization problems for SE and EE metrics are introduced from PHY resource allocation perspective. 2) The co-designed MAC layer protocol to ensure proper user and resource coordination. Two protocols will be introduced, one for maximum performance and the other for lower complexity. 3) System validation with software and hardware implementations. Extensive experimental verification is designed to systematically validate the performance of proposed schemes and algorithms.
To achieve acceptable levels of insight and accuracy, AI applications require access to immense amounts of training data and processing power. 3 Such requirements can make the infrastructure transformation necessary to enable AI complex, high risk, and costly, hindering adoption. To address these challenges, in June 2018 IBM announced the IBM Systems Reference Architecture for AI. Based on IBM PowerAI, IBM Spectrum Computing (the HPC-focused component), and IBM storage, this new reference architecture offers a proven solution for AI computing and deep learning that simplifies complex operations and reduces deployment and operational risk. Developed through realworld customer experience, the IBM Reference Architecture for AI provides a comprehensive guide to help organizations create successful AI infrastructure proofs-of-concept, expand these into production, and then scale the solutions as needed to accommodate AI application and data growth. 2351a5e196
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