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As cloud computing becomes the information technology mainstream, data center technology is accelerating at a breakneck speed. Concepts like software define infrastructure, data center analytics and Nonvolatile Memory Express (NVMe) over Fabrics are changing the very nature of data center management. According to industry research firm IDC, organizations will spend an additional $142.8 billion oninfrastructure for both public and private cloud environments in the next three years (2016-2018) to boost efficiency and business agility.
- Investing in the ecosystem to accelerate enterprise-ready, easy-to-deploy software defined infrastructure (SDI) solutions;
- Optimizing SDI solutions to deliver highly efficient clouds across a range of workloads by taking full advantage of Intel platform capabilities; and
- Aligning the industry and engaging the community through open industry standards, solutions and routes to market to accelerate cloud deployment.
As cloud infrastructure management is moving towards these new management paradigms, those at the leading edge are exploring how to make data center’s think for themselves. Industry leaders like Dr. Brian Womack, Director of Distributed Analytics Solutions in Intel’s Data Center Solutions Group, and Das Kamhout, Senior Principal Engineer at Intel are learning how to use data, artificial intelligence frameworks and machine learning to create data centers that think for themselves. Two key components of their vision are SNAP and TAP.
SNAP is a powerful open data center telemetry framework. It can be used to easily collect, process, and publish telemetry data at scale. It enables better data center scheduling and workload management through access to underlying telemetry data and platform metrics. The framework greatly improves system administrator control of the intelligent use of data center infrastructure in cloud environments by:
- Empowering systems to expose a consistent set of telemetry data;
- Simplifying telemetry ingestion across ubiquitous storage system;
- Improving the deployment model, packaging and flexibility for collecting telemetry;
- Allowing flexible processing of telemetry data on agent (e.g. machine learning); and
- Providing powerful clustered control of telemetry workflows across small or large clusters.
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| Visit Chip Chat to hear more more about creating a data center that thinks for itself! |
This content is being syndicated through multiple channels. The opinions expressed are solely those of the author and do not represent the views of GovCloud Network, GovCloud Network Partners or any other corporation or organization.
( Thank you. If you enjoyed this article, get free updates by email or RSS – © Copyright Kevin L. Jackson 2016)
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