Halloween may spook people out for only one day. But network managers face nightmare scenarios that can keep them awake all year-round.
So let’s take a walk down that dark back alley and look at some scary network scenarios.
#1 A Nightmare on Upgrade Street
End customers ultimately expect constant connectivity.
So, even brief downtime can be a costly nightmare for network operators due to SLA penalties, overtime pay, and customer compensations – not to mention their reputation. Traditionally, upgrades or patches have meant scheduling downtime during off-hours maintenance windows.
How can today’s network engineers and operations teams achieve simpler, cost-effective ways to manage software upgrades on the network?
DriveNets’ no-maintenance window capability aims to change all that by enabling live software upgrades with virtually no downtime (less than 1 second of traffic impact)
#2 Haunted by Tail Latency
In AI networking, tail latency is culprit behind performance slowdowns, representing the slowest packet delays — the rare, worst-case moments when congestion, buffering, or retransmissions drag the entire system down. Even when average latency may look fine, high tail latency forces GPUs to wait on stragglers, reducing network utilization, extending training times, and ultimately wasting compute resources. So, as clusters scale, such delays multiply, and expose bottlenecks in bandwidth, flow control, or routing.
Monitoring and minimizing tail latency is critical but can often cause performance issues.
How can organizations optimize tail latency?
The DriveNets Ethernet-based solution with advanced scheduling fabric offers an optimal approach for establishing robust, reliable AI networking infrastructures that minimize disruptions, improve performance, and ensure consistent, timely data delivery across AI clusters.
#3 The curse of the AI workloads
AI workloads are large and getting larger. Model accuracy and the race to outpace the competition is calling for larger infrastructure. AI workloads for training demands high-performance, lossless, and predictable connectivity between GPUs to reach the most efficient and optimal job completion time (JCT) targets.
Different traffic patterns and workload characteristics, various AI models run differently, and often use different communication algorithms. Different packet sizes and flow sizes represent some of the different characteristics of the traffic that the network needs to propagate. The AI network cannot be fine-tuned to one specific traffic pattern but must satisfy all. Whatever challenges or attributes of the networks which are noticeable in moderate scale are multiplied when scale increases.
How should enterprises best optimize their networks for AI workloads?
Optimize your enterprise AI with the right cluster fabric.
DriveNets Network Cloud-AI offers enterprises a superior solution. With its scheduled fabric, it can deliver the highest performance without the drawbacks of vendor lock-in, high cost, and complicated operations.
#4 Very messy network architecture
Experiencing exponential growth in traffic across their networks, cable multiple-system operators (MSOs) have been feeling the ground shaking beneath them for a while.
Some operators run multiple-generation DOCSIS, as well as some xPON services across their network, yielding a mix of aggregation nodes, edge sites, termination systems, and core routers. In some cases there is a single instance of each function per access technology/generation.
Bottom line – it’s a very messy architecture for cable MSOs.
All cable MSOs know the burden of operating a complex and inflexible large-scale network infrastructure with dozens of different routing boxes across different domains. Operating such a diverse network with multiple hardware types and generations requires a wide variety of management skills, software stack updates, and the availability of spare parts.
How are cable MSOs simplifying their network architecture through disaggregation and virtualization?
An MSO network built according to a new MSO modernization process and utilizing the DriveNets Network Cloud solution becomes a much simpler beast than the one you probably know today.
#5 Never-ending network management
Current network topologies and operation models are fragmented and complex.
Organizations rely on multiple vendors, tens of platform types, and hundreds of components.
This diverse ecosystem – including aging hardware equipment and thousands of software versions, each with distinct data models, bugs, and security patches – requires constant management.
The support infrastructure also is fragmented, requiring dedicated teams for design, lab operations, IT, and development. In addition, the operating model is heavily manual, relying on command-line interfaces (CLIs), third-party tools, and various manual interfaces between systems. All these increase the risk of human error and hamper efficiency and scalability, creating challenges for network management and optimization.
How can operators build a network designed to consolidate the subnetworks and optical networks, transforming it to be as efficient and scalable as hyperscaler networks?
Put an end to the Networking Nightmares!
DriveNets offers an open, software-based, converged, and automated solution, backed by extensive experience with the largest networks in the world.
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DriveNets Network Cloud: Transforming Service Provider Networks