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What is Windows Server Failover Clustering?

Windows Server's Failover Clustering feature, also applicable in Azure Local environments, enables a network of independent servers to work together, significantly improving the availability and scalability of clustered roles, which were formerly known as clustered applications and services. This system of interconnected nodes employs a blend of hardware and software solutions to guarantee that when one node fails, another node can automatically assume its duties through a failover process. The constant oversight of clustered roles ensures that any malfunction can lead to a swift restart or migration, maintaining continuous service. Furthermore, the system supports Cluster Shared Volumes (CSVs), which provide a unified, distributed namespace that facilitates reliable shared storage access across all participating nodes, thus reducing the risk of service disruptions. Failover Clustering is commonly used for high-availability file shares, SQL Server instances, and Hyper-V virtual machines, demonstrating its effectiveness across different applications. This capability is found in Windows Server versions 2016, 2019, 2022, and the anticipated 2025, along with support in Azure Local environments, making it a robust option for organizations aiming to bolster their system resilience. By implementing Failover Clustering, businesses can ensure that their essential applications remain operational, even amidst hardware malfunctions, thereby safeguarding their critical operations. As a result, organizations can achieve higher uptime and reliability, ultimately enhancing their overall productivity and service delivery.

What is Bright Cluster Manager?

Bright Cluster Manager provides a diverse array of machine learning frameworks, such as Torch and TensorFlow, to streamline your deep learning endeavors. In addition to these frameworks, Bright features some of the most widely used machine learning libraries, which facilitate dataset access, including MLPython, NVIDIA's cuDNN, the Deep Learning GPU Training System (DIGITS), and CaffeOnSpark, a Spark package designed for deep learning applications. The platform simplifies the process of locating, configuring, and deploying essential components required to operate these libraries and frameworks effectively. With over 400MB of Python modules available, users can easily implement various machine learning packages. Moreover, Bright ensures that all necessary NVIDIA hardware drivers, as well as CUDA (a parallel computing platform API), CUB (CUDA building blocks), and NCCL (a library for collective communication routines), are included to support optimal performance. This comprehensive setup not only enhances usability but also allows for seamless integration with advanced computational resources.

Media

Media

Integrations Supported

Active Directory
Microsoft 365
Microsoft Azure
Microsoft Hyper-V
PowerShell
SIOS DataKeeper
SQL
Windows Server

Integrations Supported

Active Directory
Microsoft 365
Microsoft Azure
Microsoft Hyper-V
PowerShell
SIOS DataKeeper
SQL
Windows Server

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

learn.microsoft.com/en-us/windows-server/failover-clustering/failover-clustering-overview

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

developer.nvidia.com/bright-cluster-manager

Categories and Features

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

HPC

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