VMware's EVO: RAIL serves as a small, flexible building block to simplify and streamline creation and deployment of virtualized environments, private clouds.
On Monday VMware will launch a stack of its virtualization software able to be preinstalled on hardware, for customers that want a "hyper-converged" building block for building out virtualization environments.
VMware calls the building block EVO: RAIL, short for an evolutionary compute unit that slides into a 2U rack server space on a rail. The design is reminiscent of Open Compute project's hardware that slid into racks on rails for easy serviceability.
It might sound as if VMware wants to compete with parent company EMC and Cisco's subsidiary, VCE, the maker of the converged infrastructure Vblocks (VMware is actually an investor in VCE), or possibly with HP with its converged infrastructure hardware units. VCE's converged infrastructure amounts to a rack of x86 hardware loaded with VMware software and a Cisco Nexus 5000 switch offloading virtual machine storage and network traffic to the top-of-rack switch. But Vblocks are full racks of servers; VMware's EVO:RAILs are fractions of racks: four server nodes sliding into the 2U space of a 42-space rack.
By "hyper-converged," VMware appears to mean that the hardware will come with all the elements configured for the most advanced forms of virtualization, including Virtual SAN or VSAN storage, introduced early this year. VSAN converts the disks on a rack of VM hosts into a shared pool. With such an assembly, it's easier to approximate infrastructure driven strictly by policies and software systems rather than by operations staff.
With virtualization so well established in the data center, VMware wants to make it simpler and easier to add infrastructure without requiring IT to do a lot of configuration. "We are trying to keep it very simple. We are being very prescriptive," said Mornay Van Der Walt, VP emerging solutions at VMware, in an interview.
The company also wants to enable private clouds as a more automated form of virtualization. Instead of assembling their own servers, storage and networking, EVO: RAIL customers can simply order a single SKU through VMware and designate their hardware supplier of choice. Several will be available, including at least two or three that participate in production of Open Compute Project designs, according to Van Der Walt. He didn't name any specific manufacturers, but the group includes Hyve, Quanta Computer, and Applied Micro.
"Buying converged infrastructure [from VCE or HP] represents an investment somewhere north of $500,000," said Van Der Walt. Current products from the hardware partners who will sell EVO: RAIL in the future fall more in the $80,000 to $250,000 range, he said. "We think we will see a major shift from converged to hyper-converged building blocks."
Each node in the hyper-converged infrastructure should be able, on average, to host 100 virtual machines or 250 virtual desktops. That means each 2U space in the EVO: RAIL server rack has the potential to run 400 virtual machines or 1,000 desktops, a higher concentration than found in the typical data center virtualized environment.
VMware expects those ratios to make EVO: RAIL assemblies attractive for virtual desktop infrastructure deployments or virtual private clouds, Van Der Walt said. And with pricing that starts around $80,000, customers may look at the speed of installation and time to running VMs as arguments to use the "hyper-converged" approach for new infrastructure.
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Charles Babcock is an editor-at-large for InformationWeek and author of Management Strategies for the Cloud Revolution, a McGraw-Hill book. He is the former editor-in-chief of Digital News, former software editor of Computerworld and former technology editor of Interactive ... View Full Bio
Multicloud Infrastructure & Application ManagementEnterprise cloud adoption has evolved to the point where hybrid public/private cloud designs and use of multiple providers is common. Who among us has mastered provisioning resources in different clouds; allocating the right resources to each application; assigning applications to the "best" cloud provider based on performance or reliability requirements.