Cisco wants to analyse your network traffic in the cloud. The company this week unveiled a new offering, dubbed Cisco AI Network Analytics, that will collect “massive amounts of network data” from participating customer sites. It will then offer subscribers “highly individualised network baselines” that constantly learn and adapt as new devices, users and applications are added or removed to a network.
The feature will provide predictive maintenance functions for IT teams, flagging network bottlenecks or other vulnerabilities before they cause issues, Cisco said.
The new software tool, dubbed Cisco AI Network Analytics, will be incorporated into of Cisco DNA Assurance, the company’s networking control and assurance dashboard, and be included in its licensing tier as a subscription offering. It will be generally available in the next version of Cisco DNA Center this summer.
The release comes as the network giant also rolled out a range of new ruggedised switches and other components for use at the industrial edge; e.g. by operators in oil fields and other frontier locations, as well as new network segmentation and other SD-WAN tools at its “Cisco Live” event in San Diego this week. Cisco AI Network Analytics was, arguably, the highlight: uptake will depend on user trust that security is rock-solid.
Cisco AI Network Analytics
The company said the new offering will provide more visibility, greater insights and “guided actions” for IT teams stretched by growing network complexity.
Since the data will be anonymised at the cloud level it can be used to develop further improvements in the offering and train models that help customers optimise their networks: Cisco AI Network Analytics can securely compare a campus network’s performance against other sites of similar size and configuration, “helping to identify opportunities for network upgrades while optimizing IT spending” the company said.
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Senior VP, Engineering, Anand Oswal explained: “Imagine a network with 6000+ access points, 10 wireless controllers, a data center, dozens of branch offices, and over 10,000 roaming wireless devices covering an area the size of a small city.”
“Every AP [access point] collects telemetry on its operating environment, radio performance, interference statistics, and the identities of devices that are connecting to them. The SD-WAN fabric connects distributed branch offices and remote workers to cloud applications and data center resources, managing thousands of connections and traffic flows over the course of a work day.”
“Trying to manually analyze and troubleshoot the traffic flowing through thousands of APs, switches, and routers is a near impossible task, even for the most sophisticated NetOps team. In a wireless environment, onboarding and interference errors can crop up randomly and intermittently, making it even more difficult to determine probable causes…The irony of having mountains of telemetry and activity logs awaiting analysis by overworked IT teams is that there is too much noise from too much data.”
Having cloud-based AI runnning on the network telemetry allows algorithms to perform troubleshooting steps that a network engineer would typically execute, but much faster and against a much larger data set than humans’ can handle.
“In large campus networks and remote branch offices, the number of alerts and false-positives for minor to major issues can come fast and furious at times, making triage the first step for NetOps teams. The AI processing helps triage issues by categorizing them according to severity, location, number of affected devices, and the ability to automatically remedy a subset of issues” Cisco said.
John Apostolopoulos, CTO/VP of Cisco’s Enterprise Networking Business, said in a blog: “We have designed our networking gear from the ASIC, OS, and software levels to gather key data, via our IBN architecture which provides unified data collection and performs algorithmic analysis across the entire network (wired, wireless, LAN, WAN, datacenter)… because we have been the #1 enterprise network vendor for the past 20+ years, we have a massive collection of network data, including a database of problems and associated root causes.”
“This combination of capabilities enables us our products to quickly identify if a problem exists, its associated root cause, and to identify fixes to solve it. The network operator can accept the proposed fixes and then they are applied. The feedback loop continues and we gather more data to determine if the network is operating as intended. If not, we identify why and continue to improve the network.”
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