Tom Nolle is founder and principal analyst at Andover Intel, a unique consulting and analysis firm that looks at evolving technologies and applications first from the perspective of the buyer and the buyers’ needs. Tom is a programmer, software architect, and manager of large software and network products by background, and he has been providing consulting services and technology analysis for decades. He’s a regular author of articles on networking, software development, and cloud computing as well as emerging technologies like IoT, AI, and the metaverse.
As a subset of distributed computing, edge computing isn’t new, but it exposes an opportunity to distribute latency-sensitive application resources more optimally.
The justification for HPE buying Juniper may be a mundane, economy-of-scale play or a move to gain Juniper's AI networking technology. Or there may be a vision for something more ambitious.
Despite prevailing cynicism, many enterprises find their primary IT vendor to be the most trusted source of network transformation insight, helping to drive both strategy and purchasing.
AI usage will surely drive additional network traffic, but for most enterprises it likely won’t require a major overhaul of the entire data center network.
If not VPNs, then what? These three ideas for connectivity might change the network future by rethinking the traditional service provider concept.
Enterprises see the potential for AI to benefit network management, but progress so far is limited by AI’s ability to work with company-specific network data and the range of devices that AI can see.
Many enterprises want to pursue a unified virtual network strategy, but visibility challenges and pressure to curb cloud costs are complicating 2024 network planning.
The network is the preferred vector of attack. Make it your prime defense.
Look for genuine value when under pressure to implement hyped technologies such as AI-driven network automation, private 5G, open networking and zero trust.
Network outages can often be traced to four error-prone activities: fault analysis and response, configuration changes, scaling and failover, and security policies.
Using generative AI technology for network operations issues can yield results that sound credible but are actually completely wrong.
Enterprises hope to invest in security, operational stability, and better application-delivery performance using technologies that include SD-WAN and AI.
Enterprises hate it when the companies they buy gear from pass the buck when issues arise, over-hype their sales pitches, and don’t give a heads-up about new products.
Wider acceptance of private 5G will require expanding its uses among early adopters and finding suitable applications for it in other industries.
Single-vendor networking has an edge in enterprises, but SD-WAN and SASE are making a case for open white-box networks.
Network operators need help finding applications that demand fast data delivery in order to justify infrastructure investment.
Avoid over-reliance on costly user-to-cloud and user-to-data-center links to create better performing apps.
Component-based applications can have a significant bad impact on cloud costs and app performance that network pros could head off if consulted during the design phase.
Networking teams need a say in architecting cloud-based applications from the outset to assure it’s even possible for the network and cloud services to adequately support the apps.
Enterprises need a single networking model that can support SD-WAN and virtual networking in order to get the most out of their cloud resources.
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