Total cost of ownership (TCO) in IT systems is a function of their capacity to do work, which in turn, is a function of storage and compute performance. In a Hyperconverged Infrastructure (HCI), this capacity is expressed in terms of the number of virtual machines (VMs) that can be supported for any given configuration. TCO is also affected by architectural design, as HCIs combine storage and compute resources into a scale-out cluster.

In this TCO analysis we calculate the storage and compute performance of NetApp HCI and two leading competitive systems and compare how their different architectures impact total cost at varying levels of scale.

Hyperconverged Infrastructure

Benefits of HCI

HCI technology combines compute power with local storage and storage networking, running one of the available hypervisor platforms in a clustered architecture. A turnkey, scale-out solution, HCIs are designed to be deployed quickly, simple to operate and easily expanded, making them a good fit for IT generalists. These comprehensive infrastructures are flexible enough to support a variety of different applications and can be configured to meet the resource requirements of a diverse workload mix. HCIs have also been popular for specific applications that required a new or dedicated infrastructure, such as VDI or for isolated environments like branch offices or remote departments. But, as this technology has matured, HCIs have crossed a threshold of acceptance in enterprise data centers. A primary finding of the Evaluator Group Study “CI – HCI in the Enterprise 2019”, enterprise IT now considers HCI a viable solution for most of the workloads they have and for a small majority, essentially any workload they have.

Challenges of HCI

Hyperconverged clusters expand easily, but the mean unit of scale is the node itself. And, often nodes in the cluster must have the same storage configurations. This lack of resource flexibility runs counter to a primary benefit of the hyperconverged infrastructure, the ability to support a wide variety of workloads. Companies want to consolidate applications, and the infrastructure supporting those applications, into an HCI cluster. In fact, consolidation was the number one use case in the study mentioned above, chosen by over 70% of companies with HCI in production. However, consolidation creates a nonhomogeneous collection of workloads that can require a variable mix of compute and storage resources.

Unfortunately, most HCI clusters force you to add these resources in lock-step with each other, based upon the configuration of the appropriate nodes. But in addition to scaling, there’s a more fundamental challenge in sizing HCI that creates inefficiency and increases cost.

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