AI Delving Deep into Data Center Networks

Intelligent networking is already making its way into the enterprise, forever changing the ways in which both traffic and resources are managed. But how is this likely to play out? What aspects of modern network management are ripe for intelligence now and what is likely to evolve over time?

According to Mind Commerce, the total market for AI-driven networking solutions is expected to hit $5.8 billion by 2023. In fact, by that time, more than half of the total AI spend will go toward the network. Much of this will be linked to the deployment of software-defined networking (SDN), as well as edge computing, the IoT and emerging 5G topologies on the mobile side. Ultimately, the rudimentary intelligence will lead to self-organizing networks (SON) and cognitive network management solutions capable of supporting autonomous decision-making across wide swaths of network infrastructure.

One of the initial applications for AI on the network is visibility. As traffic becomes more complex and data infrastructure becomes more distributed over wide area infrastructure, the need to gain deep packet-level visibility and real-time telemetry increases. Barefoot Networks and Netronome recently teamed up to bring intelligent insight into end-to-end network infrastructure as a means to detect and prevent root-cause problems that impede application performance. The pair will combine the Netronome SmartNIC with Barefoot’s Wedge 100BF switch and Smart Programmable Real-time INT (SPRINT) platform to help network managers maintain control of scale-out environments. The combined system will feed data to Barefoot’s Deep Insight Analytics Engine for rapid analysis of anomalies and dynamic event visualization to triangulate performance issues to VMs, NICs or network switches.

Chip-level solutions are incorporate higher degrees of intelligence as well. Cavium recently introduced the Packet Trakker system on the XPliant series of programmable Ethernet switches. The system works with the Cavium Network Operating System and related analytics engines to enable real-time and historical resource utilization and performance data monitoring. Microburst detection of destabilizing data flows, latency fluctuations and packet loss help ensure proper network and application performance, while automated alarms alert system management applications to potential equipment failure or noticeable performance degradation.

But even as intelligence is changing the network, it is also altering the way in which data resources are provisioned and consumed, says Market Realist’s Paige Tanner. This is most pronounced in an increasingly intelligent Internet of Things, which is upping the reliance on the cloud and causing many providers to increase the speed and agility of their internal infrastructure. A case in point is Amazon, which is rapidly deploying NAND flash and other solutions to accommodate the needs of increasingly smart ecommerce applications. As well, cloud providers of all sizes are rolling out GPU-as-a-Service, on-server memory solutions and the latest high-speed processors from Intel, AMD and Qualcomm to handle the expected loads from autonomous cars, self-service kiosks and intelligent agents in the home and workplace.

Intelligent networking will give the enterprise a boost when it comes to supporting next-generation applications and services, but it still requires a guiding hand when applying data-driven analytics to working production environments. As with any automated system, an intelligent agent can magnify the damage of a process that is fundamentally flawed, particularly when it does not have access to all of the data needed to make an informed decision.

But as application performance becomes increasingly dependent upon the fine-tuning of a vast and complex network infrastructure, expect smart monitoring and management to quickly transition from a competitive advantage to a core necessity.

Arthur Cole is a freelance journalist with more than 25 years’ experience covering enterprise IT, telecommunications and other high-tech industries.

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