The market for hyperscale data centers is heating up. That raises the inevitable questions over how to network an incomprehensibly large collection of hardware.
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Artificial intelligence algorithms will help manage, monitor and secure increasingly complex software-defined networks.
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In this age of digital transformation, nothing is more suitable for a remake than the enterprise data center. The demands of a service-driven economy are quickly proving to be too much for rigid, silo-based infrastructure, and this is before we begin to contemplate the implications of Big Data and the Internet of Things.
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One of the chief advantages to cloud computing is its ability to implement the latest technologies in record time. While the enterprise struggles to deploy Flash technology and even basic server virtualization in the data center, the cloud is already pushing forward with machine learning (ML) and artificial intelligence (AI).
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Although detractors may decry it as a waste of money and resources, the enterprise seems to have settled on the hybrid cloud as the infrastructure play for the emerging digital services economy.
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Application development for the Internet of Things (IoT) is proving to be a bit trickier than initially thought. When processes are intended to function across a diverse collection of systems and infrastructure, how is the enterprise expected to maintain data integrity or prevent theft or illegal copying of proprietary code?
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No matter how scaled-out, hyperconverged or abstracted data center infrastructure becomes, one element remains the same: Servers need high-speed access to vast amounts of storage.
This produces a paradox, however, because as more storage comes online, it takes longer to find and retrieve data locked somewhere within its volumes.
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Software-defined networking helps solve some of the operational complexity issues associated with the hybrid cloud.
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The enterprise is anxious to automate as much of its data ecosystem as possible, starting with the cloud. But is automation the best solution for every challenge, and if not, how can enterprise executives determine what should be automated and what should remain under human control?
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The data volumes expected from the Internet of Things (IoT) are certain to be large – too large, in fact, for even an army of trained analysts to turn into useful information in a reasonable amount of time.
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Enterprise Illumination 