Table of Contents
Hyperconvergence for the enterprise market is both mature and consolidated. VMware Cloud Foundation (VCF) holds the lion’s share of the market in terms of deployments, and also enjoys technology leadership. At the same time, alternative solution stacks are gaining popularity by offering compelling value and innovative approaches.
In fact, interest has shifted from core virtualization features to the platform ecosystem and integration of core, cloud, and edge components. Other aspects of hyperconvergence infrastructure (HCI) that are quickly gaining traction include automation and orchestration, as well as integration with Kubernetes. The final goal is to build hybrid cloud infrastructures that can provide a consistent user experience across different environments while enabling applications and data mobility.
Traditional HCI approach, with scale-out architectures based on nodes that add both storage and compute resources to the cluster, are now joined by disaggregated HCI (dHCI) solutions, which provide a complete separation between the storage layer and compute nodes. These two schemes have both advantages and disadvantages in terms of resource utilization, system management and expansion, scalability, and efficiency, but the primary goal of HCI—infrastructure simplification—is reached via both approaches. In fact, no matter the back-end architecture, the management system and processes usually present similar features. That is even more common now that HCI and dHCI solutions can share or use resources in conjunction with external systems, and an increasing number of vendors can now offer both solutions to their customers.
In the last year, the COVID-19 pandemic has accelerated efforts around digital transformation and uptake of virtual desktop infrastructure (VDI) farms. This has created some intriguing opportunities around HCI, as newly optimized infrastructures add resources that can support cutting-edge applications. For example, adoption of VDI has increased the number of GPUs available in the datacenter. Most of these GPUs are engaged with VDI workloads during working hours, leaving them available at other times to drive machine learning (ML) and artificial intelligence (AI) processing for improving business and industrial processes.
How to Read this Report
This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding consider reviewing the following reports:
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