AI burdens and strengthens networks in equal measure

More and more companies are turning to software-defined networks such as SD-WAN and orchestrating their infrastructure via the cloud to satisfy the performance hunger of AI applications and workloads. But despite progress in these areas, three challenges characterize this new era of connectivity:

Challenge #1: System overload due to high traffic 

AI clusters drive energy and bandwidth requirements far beyond conventional limits: a single GPU rack can generate up to 100 kilowatts of thermal power and traffic of tens of terabits per second during data processing, which causes enormous loads on a physical and logical level. Physically, hardware, cabling, power supply and cooling often reach their limits. This results in bandwidth bottlenecks and hotspots, which increase the susceptibility to errors. On a logical level, the massive data traffic overloads network and software infrastructures, resulting in traffic congestion, storage bottlenecks and security risks: Cybersecurity solutions are often not suitable for the high data throughput and are therefore less able to detect anomalies.

Challenge #2: Larger attack surface at the edge

Edge computing and the associated decentralization are the basic prerequisite for the agile use of AI. However, this significantly larger and more distributed IT infrastructure creates a large number of new points of attack for hackers: every sensor, every gateway and every remote server becomes a potential weak point that criminals can use to cause downtimes, for example. Targeted outages at edge locations are particularly popular with cyber criminals to infiltrate central systems while defenders are distracted.

Challenge #3: Cascading failures due to operational overload

Although more and more network management processes are being automated, the workload for administrators is increasing dramatically. Reasons for this include the shortage of skilled workers and the increasing complexity of network infrastructures due to AI and edge applications. The human factor therefore remains a critical weak point that increases the risk of misconfigurations, missed or incorrect updates and reactive maintenance due to operational overload. The two options to mitigate the resulting cascading failures are an even higher degree of automation and the implementation of OOB (out-of-band) solutions.

However, artificial intelligence not only poses challenges, but also offers companies new opportunities to increase their efficiency, security and resilience - especially in conjunction with out-of-band networks: 

Opportunity #1: Less downtime thanks to predictive analytics

AI-based predictive analysis tools help companies to identify their capacity limits, predict outages and optimize maintenance windows. The age of purely reactive network management is coming to an end. The integration of NetOps automation tools extends these functions by taking over recurring tasks and eliminating configuration errors - two eminent factors in the occurrence of downtimes.

Opportunity #2: Lower MTTR due to self-healing networks

AI is becoming the key to reliable and resilient networks. AI systems now exist that analyze telemetry data, detect anomalies and automatically initiate recovery measures even before users notice a disruption. Intelligent out-of-band solutions complement these capabilities by maintaining a connection to network resources even if the production network fails. Together, AI and OOB solutions thus form the basis for self-healing networks and drastically reduce the mean time to recovery (MTTR).

Opportunity #3: Hybrid models for legacy and AI-native systems

Companies that want to make their network infrastructure fit for the future must reconcile the reliability of legacy networks with the benefits of AI-supported orchestration and monitoring. In this context, true modernization does not mean simply replacing old hardware and software, but rather integrating new solutions into existing ones in a meaningful way. Out-of-band management helps companies to do this by providing a universal control level for legacy and AI-native cutting-edge systems.

„Operating AI systems is hard work for network administrators and puts a strain on hardware and software,“ explains Dirk Schuma, Sales Manager EMEA North at Opengear. „The combination of AI functionality and out-of-band solutions is a real game changer in this context, as it has the potential to significantly increase the resilience of networks.“

Source: Opengear