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Hyperscale computing refers to the facilities and provisioning required in distributed computing environments to efficiently scale from a few servers to thousands of servers. Hyperscale computing is usually used in environments such as big data and cloud computing. Also, it is generally connected to platforms like Apache Hadoop.
The structural design of hyperscale computing is often different from conventional computing. In the hyperscale design, high-grade computing constructs, such as those usually found in blade systems, are typically abandoned. Hyperscale favors stripped-down product design that is extremely cost effective. This minimal level of investment in hardware makes it easier to fund the system's software requirements.
The following design elements are discontinued in hyperscale computing:
The hyperscale computing architecture is available in the form of a single unit, which makes use of converged networking, a blend of network-attached and local storage, or as management software incorporated in a modest form factor.
Customers who adopt hyperscale computing solutions benefit from an exceptionally low-cost investment as a system with minimal configuration can run a base level of virtual machines in a dedicated and private system. Hyperscale computing architecture also works effectively in large-scale implementations, where thousands of virtual machines are being operated.
Hyperscale architecture includes key features, such as horizontal scalability intended for improved performance and high throughput as well as redundancy intended for fault tolerance and high availability. Well-designed applications coupled with an efficient hyperscale architecture offers enterprises a potent tool to control an agile business, allowing them to gain an edge over their competitors.