Device42, a cloud discovery platform, this month launched a multicloud migration and recommendation engine the company claims is the first to support all major cloud providers. Using machine learning to drive its suggestions, Device42 says the service can perform real-time discovery of IT resources to create an inventory, leveraging dependency mapping to show the relationship and impact of resources on business units.
Organizations often face risks of business outages and disruptions when attempting to migrate to the cloud. And according to IDG research, only 25% achieve their initial goals. Additional reporting by Unisys has found that more than one-third of businesses fail to capture “notable benefits” from their cloud computing projects.
Device42’s new recommendation engine aims to help with cloud migration via AI-driven analysis. It works by first performing a discovery of all resources and apps, creating a directory. Once the inventory finishes, the engine delivers a cost analysis to recommend which apps to move to the cloud and which cloud — Amazon Web Services (AWS) or VMWare on AWS, Microsoft Azure, GCP, or Oracle — might be best for each app.
“We know migration is a big challenge for many organizations, and we’ve heard it loud and clear from our customers. We built this engine to help our customers automate the processes and help them reduce risk,” Device42 founder and CEO Raj Jalan said in a statement.
‘Right-sizing’ cloud deployments
According to RightScale, in 2017 26% of enterprises with more than 1,000 employees spent over $6 million a year in the public cloud. But it’s estimated that a fair amount of that enterprise cloud spend is going to waste. The same report found that the average waste in cloud costs was 35%, netting out to $10 billion each year across AWS, Azure, and GCP.
Device42’s engine can provide data about the cost of resources and their performance impact, as well guidelines to support best practices. It helps determine the most efficient course of action, including whether to re-architect apps, and works to identify the right sizes for cloud instances.
According to Jalan, the engine matches operating systems from on-premises solutions to the cloud so apps function after migration. Savings come from reservation purchase options and algorithms that factor in networking and storage costs, along with CPU and memory.
“The [engine] provides the visibility and information users need to make key decisions across cloud instances,” Jalan continued.