StorCentric & Announce Cloud-Native Solutions For VM-as-a-Service & Virtual Desktops and StorCentric today announced the general availability (GA) of hyperconverged cloud-native solutions that simplify and accelerate channel solution providers’ and end users’ journey to the private cloud. The joint offerings allow the Robin Cloud Native Platform (CNP) to be coupled with StorCentric’s Nexsan Unity enterprise-class unified storage or with StorCentric’s Nexsan E-Series high-density, high-performance, highly scalable storage to deliver powerful solutions for enterprises that require agility and as-a-service application delivery, where cost has been a major impediment. 

The joint solutions are delivered as a hyper-converged appliance and offer a cloud-like experience for hosting virtual machines (VMs) and virtual desktop infrastructure (VDI). Compared to public cloud offerings, the solutions offer 2x improved performance and 50% faster application provisioning, with a 50% reduction in operating costs. 

Cloud Native HCI Solution from Robin and StorCentric 

Rising datacenter complexity is overwhelming IT organizations. As a result, they have begun turning to hyper-converged infrastructure (HCI) for simplicity and ease-of-use. Using Robin CNP with StorCentric Nexsan E-Series or Nexsan Unity storage provides a software-defined infrastructure on which containerized and non-containerized applications can be delivered as-a-service and deployed in minutes instead of hours, offering a high degree of automation for lifecycle operations. 

As Kubernetes scales inside the enterprise, users are looking to leverage the technology for running mission-critical workloads such as stateful applications like databases, big data and AI/ML applications. Unlike stateless applications, these applications have important storage and networking requirements. The Kubernetes community has focused on the need to support stateful workloads—the work done around Stateful Sets is a good indicator of this progress. But this effort is far from mature and there exists operational overhead in provisioning the clusters needed for persistent volumes. Many IT organizations are spending multiple cycles to get Kubernetes set up for stateful workloads, leading to friction and delays. 

The problem grows larger when big data and other data-intensive workloads become part of the equation. Beyond the operational overhead, performance is also a critical criterion for these workloads. The enterprise decision makers are torn between selecting a DIY approach to running stateful workloads on Kubernetes and finding the right platform that is suitable for data-intensive workloads. 

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