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Torsten Volk, an analyst at Enterprise Management Associates in Boulder, Colo., sees an evergrowing role for machine learning and networking. Overall, IT operations are poised for cost savings and risk reductions through the adoption of machine learning and networking, he said, but challenges remain. Volk used a table to illustrate the challenges machine learning needs to address; chief among them is the need for enterprises to minimize manual steps that eat up as much as 50% of a firm's staff time. In addition, machine learning must overcome barriers created by enterprises dealing with limited information, which hampers their ability to respond to changing user trends and application demands.
Above all, machine learning and networking need to be about constant optimization and ensuring business alignment is also part of the picture. In Volk's view, serverless functions and container management can help to change the economics of IT operations, but without clear objectives and good metrics, machine learning and other advanced network technologies may not be able to live up to their value proposition.
Dig deeper into Volk's thoughts on machine learning and networking.
Arista launches a new virtual router
Drew Conry-Murray, writing in Packet Pushers, weighed in on Arista Networks' announcement of its new vEOS Router, a virtual router designed to run on public clouds. The product is deployed with a hypervisor or cloud instance and follows on the heels of cEOS, a containerized version of EOS launched earlier in 2017 as part of Arista's Any Cloud initiative. According to Conry-Murray, vEOS fits into the vendor's larger strategy by linking across multiple public clouds to Arista CloudVision to share compliance policies, security and management.
Conry-Murray noted that organizations can continue to use other cloud tools in conjunction with vEOS, whether on premises or in cloud instances. The new router, with throughput of up to 500 Mbps, will ship later this year in a subscription model for $295 per month, with availability in Amazon Web Services and Microsoft Azure. Higher throughput options -- 1 Gbps and 10 Gbps -- are under consideration, Conry-Murray said.
Read more of Conry-Murray's thoughts on Arista's virtual router.
Challenges with collecting and analyzing cybersecurity data
Jon Oltsik, an analyst at Enterprise Strategy Group in Milford, Mass., looked into potential pitfalls associated with enterprise security teams collecting ever-increasing reams of data. ESG research indicates that 38% of organizations collect more than 10 TB of data every month, primarily from firewall logs, network devices, antivirus and user activity logs. "Let's face it, well-intentioned security teams are being buried by data today. They go through heroic efforts and do what they can, but there is an obvious and logical outcome here: As security data volume grows, security professionals will only be able to derive an incremental amount of value," Oltsik said.
For organizations swamped with security data, Oltsik recommends making data available through standard APIs or putting data in standard formats such as the Common Information Model used by Splunk. He also recommends embracing automation, artificial intelligence and machine learning, as well as adopting distributed security data management services. "To make this data more powerful, we need to make it easier to consume, analyze, and operationalize. It will take the security industry and cybersecurity professionals working collectively to make this happen," Oltsik added.
Explore more of Oltsik's thoughts on cybersecurity data.
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