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“The path to an enterprise-grade private cloud is arduous and complex, requiring specialized infrastructure and platform knowledge to build a scalable solution,” said Surya Varanasi, CTO, StorCentric. “The ideal software-defined platform needs key features such as unified management, 1-click operations, data management services, customizable services, custom backup and DR, multitenancy, and self-service to enable a long- term scalable solution. The coupling of RobinCNP with either StorCentric Nexsan E-Series or Nexsan Unity meet these requirements with truly revolutionary solutions.”
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.
“Agility and faster time to value are the primary drivers for the adoption of cloud for IT modernization strategies,” said Mehran Hadipour, vice president, business development and tech alliances at Robin.io. “Building a platform that can support VMs and containerized workloads that provides a high degree of automation and highly competitive cost of ownership compared to public cloud will be a game changer for customers embarking on application modernization.”
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.
