New AI Solution Aims To Create 'Self-Driving' Datacenter Network
- By John K. Waters
This week Montreal, Canada-based networking and datacenter solutions vendor Kaloom announced a new product that it says will facilitate the creation and deployment of "self-driving" datacenter networks.
The company's new flowEye offering is a real-time, artificial intelligence (AI)-powered, in-band network telemetry (INT) and analytics solution that traces actual packet routes as they travel through the datacenter.
Traditional network telemetry and analytics solutions that rely on sampled or synthetic traffic, or packet-probing protocols, were not built for virtual architectures, and they're not keeping up with demands for real-time visibility into the multiple data types, according to Kaloom. In contrast, flowEye works with packets that travel through virtual system infrastructures that include controllers, routers, gateways and security, each of which may be installed on different hardware.
The solution "leverages the capabilities of the P4 programmable ASIC based switches to obtain granular, real-time insight of the network state regarding key metrics pertaining to packets based on actual traffic and their flow as they traverse the network," the company says.
Paul Parker-Johnson, chief analyst at ACG Research, noted in a statement that INT, analytics and automation are well-known for their potential to improve datacenter operations and enhance application deliveries. He sees Kaloom's perspective on analytics and automation in the datacenter environment as "both innovative and forward-looking."
"Kaloom's flowEye innovations significantly increase the granularity of insight that can be applied to pursuing those goals," he said. "And the fact that its functions are programmable into functioning datacenter resources means the outputs from its analytics are available faster than existing solutions, and at lower overall cost, since no additional equipment needs to be installed."
The flowEye solution follows packet routes through the datacenter, tracking where they've been and the impacts of throughput among both virtual and physical elements. The networking nodes along the path use the INT instructions to tell devices what state to collect and write that information into the packet as it transits the network.
One key advantage, according to the company: The improved granularity provided in real-time facilitates root cause analysis, so network problems can be pinpointed before they occur and corrective actions can be taken. "Our automated real-time monitoring, analytics, and troubleshooting capabilities will change the way datacenters are currently managed," said Kaloom CTO Suresh Krishnan in a statement. "Kaloom has taken a unique approach and can now provide an industry-first, real-time visibility of the packets and flows for the actual traffic, thus guaranteeing optimum operational visibility for datacenters."
Kaloom is currently probably best-known for its automated Software Defined Fabric (SDF) product, an industrialized software solution for open networking whiteboxes, which it launched last year. Kaloom targeted its SDF at hyperscale and distributed datacenter environments, where there's a need for low-latency, low-cost, programmable solutions.
Another Kaloom offering, Cloud Edge Fabric, is a fully automated datacenter networking fabric solution specifically designed for simultaneous 4G and 5G applications at the datacenter edge, providing native support for network slicing along with embedded 5G user plane function.
John K. Waters is the editor in chief of a number of Converge360.com sites, with a focus on high-end development, AI and future tech. He's been writing about cutting-edge technologies and culture of Silicon Valley for more than two decades, and he's written more than a dozen books. He also co-scripted the documentary film Silicon Valley: A 100 Year Renaissance, which aired on PBS. He can be reached at firstname.lastname@example.org.