YOU ARE AT:Big Data AnalyticsAI driving rapid change in network computing and data centers (Reader Forum)

AI driving rapid change in network computing and data centers (Reader Forum)

In the world of network computing and data centers, any 2022 planning report destined for company decision makers likely has three familiar words in the summary text: distributed IT infrastructure.

Accelerated change brought on by the pandemic is forcing companies large and small to reimagine the data center. In order to stay relevant with customers and employees working in hybrid places, businesses increasingly are choosing between a smorgasbord of public cloud, on-premise computing and colocation to determine what best suits business needs.

While each company’s strategies may differ, a common denominator for many businesses is their rush to put AI and data analytics front and center of their planning. AI such as recommender systems, simulation software and natural language processing are considered essential in a push to boost productivity, deliver new products and services, address massive supply chain issues and more. 

Researcher 650 Group predicts Enterprise and Cloud Data center equipment spend will exceed $200B in 2022, growing in excess of 6 percent from 2021. Enterprise spend remains focused on supporting multi-cloud and hybrid IT while Cloud spend continues to add to capacity and focus on new workloads and applications.

Based on conversations with partners and customers, some key NVIDIA initiatives include:

AI as a Service

Companies that are reluctant to spend time and resources investing in their own AI infrastructure, whether for financial reasons or otherwise, will begin turning to third-party providers to achieve rapid time to market.

Large businesses, including the Fortune 500, will deploy a hybrid approach to AI by choosing a combination of on-premise and cloud solutions, said Alan Weckel, founder and technology analyst at 650 Group.

Small and medium businesses will primarily rely on AI-as-a-Service offerings for their AI workloads. Within AI, workloads, compute and networking are significantly outpacing growth experienced by the industry over the past five years, Weckel said.

Data center is the new unit of computing

Applications that previously ran on a single computer don’t fit into a single box any more. The new world of computing increasingly will be software defined and hardware accelerated.

As applications become disaggregated and leverage massive data sets, the network will be seen as the fast lane between many servers acting together as an enormous  computer. Software-defined data processing units will serve as distributed switches, load balancers, firewalls and virtualized storage devices that stitch this data center-scale computer together.

Growing trust in zero trust

As applications and devices move seamlessly between the data center and the edge, enterprises will have to validate and compose these apps from microservices. Zero trust assumes that everything and everyone connected to a company system must be authenticated and monitored to verify bad actors aren’t attempting to penetrate the network. Everything has to become protected both at the edge and on every node within the network. Data will need to be encrypted using IPSEC and TLS encryption, and every node protected with advanced routers and firewalls.

Enterprises’ next data centers will belong to someone else

Many businesses turned away from owning their own data centers when they moved to cloud computing, so, in 2022, companies will realize it’s time to start leveraging colocation services for high-performance AI infrastructure. The ease of deployment and access to infrastructure experts who can help ensure 24/7/365 uptime will enable more enterprises to benefit from on-demand resources delivered securely, wherever and whenever they’re needed.

As companies increasingly adopt AI to drive efficiency and revenue, the number of applications that will be made available across many of the world’s largest industries will jump exponentially. 

That will create a massive data pipeline that flows into giant data centers like Microsoft Azure from autonomous cars, robots in the factory, cameras in the warehouse and medical equipment in the hospital.

This pipeline will allow for inference learning at the edge and iterative model training at the core. At the same time, updated deep learning models and inference rules will flow outward from the data centers back to users, computers and smartphones at the edge.

ABOUT AUTHOR

Editorial Reports

White Papers

Webinars

Featured Content