Today’s highly labor-intensive enterprise datacenters rely on many manual processes and are prone to error. They also lack the ability to balance the need to get maximum return from prior investments with the need to leverage the latest technologies. This pressure is being exacerbated as enterprises seek to use innovative services such as machine learning (ML), image processing, and augmented reality.
Goals and Objectives
Enterprises must shift toward the use of agile datacenters that are more automated, making heavy use of software-defined infrastructure and predictive analytics to improve operations and reduce downtime. They also need to adopt new asset consumption models that reduce the costs and complexities associated with asset life-cycle management.
Hyperconverged / composable systems, accelerated computing systems (GPUs, FPGAs), NVMe-based storage, software-defined network/compute, storage, network accelerators, AI-enabled cloud automation/orchestration platforms, IT/rack-level sensors
Use Case Summary
Deployment and continual enhancement of modern, software-defined infrastructure in modular, self-healing, and autonomous datacenter resources in enterprise-owned/operated facilities