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Hewlett Packard Enterprise (HPE) has introduced Release 3 of HPE GreenLake for Block Storage, featuring HPE Alletra Storage MP. This innovative scale-out disaggregated block storage allows enterprises to independently scale capacity and performance. Managed through the HPE GreenLake Cloud Platform, it offers an on-premises cloud experience, efficient scaling, and robust performance for modern mission-critical workloads.
Kamal Kashyap, Director of the Storage Business Unit at Hewlett Packard Enterprise India, emphasized the importance of data management for modern businesses. The new release enhances performance reporting, analytics, and troubleshooting capabilities, aiming to modernize storage management and meet the evolving needs of enterprise data management.
Release 3 introduces new multi-node switch models for enhanced performance and capacity. It also supports NVMe for Ethernet networking, alongside Fibre Channel, NVMe-oF/FC, and iSCSi connectivity options. Advanced AI-based performance reporting and analytics, along with a warranty program for quadruple data compression, are included. Additionally, HPE offers a 100% data availability guarantee.
The storage architecture of HPE GreenLake for Block Storage MP Release 3 differs from legacy systems, providing flexibility and scalability without creating silos. It allows for the addition of controllers and drives independently, promoting consistent performance and low latency.
The release includes switch models with up to four controller nodes and support for up to eight JBOF expansion bays. This enables scaling from 15.36 TB to approximately 2.8 PB seamlessly. The architecture ensures predictable performance and low latency across all media, controllers, and host ports.
HPE GreenLake for Block Storage MP leverages AIOps for infrastructure management, offering predictive insights and troubleshooting capabilities. Release 3 enhances AI-based performance reporting and analytics, providing trend analysis and capacity reports. It also includes technologies for data reduction and aggregation to improve economics without compromising performance.
