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Not All Networks Are Built for the Edge

Julio Petrovitch
NetAlly

From smart factories and autonomous vehicles to real-time analytics and intelligent building systems, the demand for instant, local data processing is exploding. To meet these needs, organizations are leaning into edge computing. The promise? Faster performance, reduced latency and less strain on centralized infrastructure.

But there's a catch: Not every network is ready to support edge deployments. The shift from cloud to edge isn't a silver bullet … it comes with its own set of performance, connectivity and security challenges that can derail return on investment if IT teams aren't prepared. Before rushing into edge, it's worth asking: Is your network actually built for it?

Recent research from IDC shows that global spending on edge computing is expected to reach around $261 billion in 2025. Despite its advantages, edge computing introduces a new layer of complexity. Moving workloads closer to the source doesn't inherently solve latency. Local bottlenecks like Wi-Fi congestion, inefficient routing, and oversubscribed nodes can impact performance. For example, a retail store using edge-based video analytics might run into delays, not because the analytics system is slow, but because the Wi-Fi is overloaded. With numerous devices fighting for bandwidth or a single access point stretched too thin, performance can take a hit. Measuring round-trip latency at the point of deployment is essential to validate that the edge network is delivering on its promise.

Coverage gaps and internal bandwidth limitations also pose risks. Many edge and IoT devices are deployed in low-signal environments (ceilings, walls, utility spaces) where connectivity can be unreliable without precise, location-based testing.

Meanwhile, increased east-west traffic from localized processing can strain internal links that weren't designed for high-volume lateral communication. Imagine a building automation system where sensors are installed behind ceiling tiles or inside utility closets. On paper, the network coverage might look sufficient — but in practice, those materials can block or degrade the signal. Without testing connectivity at the exact device location, these sensors could drop offline or send delayed data, undermining the reliability of the entire system.

The surge in east-west traffic at the edge doesn't just strain network capacity; it also complicates security monitoring. Traditional perimeter defenses and cloud-based firewalls may not see lateral communications between devices. Without continuous visibility and anomaly detection, malicious activity can blend in with normal machine-to-machine chatter.

Beyond performance and reliability, security must be front and center. Every new sensor, kiosk, or edge server adds another potential entry point for attackers. Unlike data centers and company HQs with hardened perimeters, edge devices are often deployed in uncontrolled environments like retail floors, factory lines, or remote offices where they may be more vulnerable to physical tampering. Centralized monitoring technologies like Endpoint Detection and Response are less effective at the network edge, so the risk of rogue access points or unsecured ports is higher. Malicious activity or unusual network behavior will be harder to detect. Finally, edge devices themselves often use outdated operating systems and basic software with many security flaws.

Maximizing the value of edge computing starts with proactive planning and rigorous validation. That begins by measuring latency before and after deployment — not just at the network level, but for each specific application and service. Round-trip testing and packet analysis can confirm whether devices are reliably connecting with intended endpoints and performing within acceptable thresholds.

General proximity is not enough when it comes to wireless coverage, it must be assessed at the physical device location. Research from 2024 confirms that signal strength can deteriorate dramatically with just a few meters of distance or light obstruction. The study, measuring Wi-Fi signal quality from 1 meter to 15 meters from a router, found a significant drop in signal strength and data speed as distance increased, with performance further degraded by walls, furniture, and other obstructions — as would be expected. For instance, imagine a smart sensor mounted in a warehouse ceiling. On a map, it's well within range of the nearest access point, but thick steel rafters and high shelving panels obstruct the Wi-Fi path. At that exact location, signal strength can fall below usable thresholds, causing intermittent dropouts or delayed transmissions that wouldn't be caught unless measured in proximity to the sensor itself.

It's also important that signal quality and load testing simulate real-world conditions to ensure infrastructure can handle demand as deployments scale. With east-west (internal, device-to-device) traffic increasing, IT teams should test throughput across switch-to-switch and access-layer connections. At the same time, north-south (external, device-to-cloud) traffic should be validated to confirm critical applications can reliably reach data center and cloud services. Together, these tests ensure both internal and external paths can support elevated loads without introducing bottlenecks.

Edge computing can unlock significant performance gains, reduce latency, and shift compute load from centralized infrastructure — but only when the underlying network is both performance-ready and secure. Success depends on more than shifting workloads closer to devices. It requires deliberate testing, full visibility, and cross-functional coordination. By validating latency, assessing wireless coverage, stress-testing both east-west and north-south links, and securing every endpoint, IT leaders can avoid common pitfalls and deliver the reliability, responsiveness, and protection their users expect.

Julio Petrovitch is a Product Manager at NetAlly

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Not All Networks Are Built for the Edge

Julio Petrovitch
NetAlly

From smart factories and autonomous vehicles to real-time analytics and intelligent building systems, the demand for instant, local data processing is exploding. To meet these needs, organizations are leaning into edge computing. The promise? Faster performance, reduced latency and less strain on centralized infrastructure.

But there's a catch: Not every network is ready to support edge deployments. The shift from cloud to edge isn't a silver bullet … it comes with its own set of performance, connectivity and security challenges that can derail return on investment if IT teams aren't prepared. Before rushing into edge, it's worth asking: Is your network actually built for it?

Recent research from IDC shows that global spending on edge computing is expected to reach around $261 billion in 2025. Despite its advantages, edge computing introduces a new layer of complexity. Moving workloads closer to the source doesn't inherently solve latency. Local bottlenecks like Wi-Fi congestion, inefficient routing, and oversubscribed nodes can impact performance. For example, a retail store using edge-based video analytics might run into delays, not because the analytics system is slow, but because the Wi-Fi is overloaded. With numerous devices fighting for bandwidth or a single access point stretched too thin, performance can take a hit. Measuring round-trip latency at the point of deployment is essential to validate that the edge network is delivering on its promise.

Coverage gaps and internal bandwidth limitations also pose risks. Many edge and IoT devices are deployed in low-signal environments (ceilings, walls, utility spaces) where connectivity can be unreliable without precise, location-based testing.

Meanwhile, increased east-west traffic from localized processing can strain internal links that weren't designed for high-volume lateral communication. Imagine a building automation system where sensors are installed behind ceiling tiles or inside utility closets. On paper, the network coverage might look sufficient — but in practice, those materials can block or degrade the signal. Without testing connectivity at the exact device location, these sensors could drop offline or send delayed data, undermining the reliability of the entire system.

The surge in east-west traffic at the edge doesn't just strain network capacity; it also complicates security monitoring. Traditional perimeter defenses and cloud-based firewalls may not see lateral communications between devices. Without continuous visibility and anomaly detection, malicious activity can blend in with normal machine-to-machine chatter.

Beyond performance and reliability, security must be front and center. Every new sensor, kiosk, or edge server adds another potential entry point for attackers. Unlike data centers and company HQs with hardened perimeters, edge devices are often deployed in uncontrolled environments like retail floors, factory lines, or remote offices where they may be more vulnerable to physical tampering. Centralized monitoring technologies like Endpoint Detection and Response are less effective at the network edge, so the risk of rogue access points or unsecured ports is higher. Malicious activity or unusual network behavior will be harder to detect. Finally, edge devices themselves often use outdated operating systems and basic software with many security flaws.

Maximizing the value of edge computing starts with proactive planning and rigorous validation. That begins by measuring latency before and after deployment — not just at the network level, but for each specific application and service. Round-trip testing and packet analysis can confirm whether devices are reliably connecting with intended endpoints and performing within acceptable thresholds.

General proximity is not enough when it comes to wireless coverage, it must be assessed at the physical device location. Research from 2024 confirms that signal strength can deteriorate dramatically with just a few meters of distance or light obstruction. The study, measuring Wi-Fi signal quality from 1 meter to 15 meters from a router, found a significant drop in signal strength and data speed as distance increased, with performance further degraded by walls, furniture, and other obstructions — as would be expected. For instance, imagine a smart sensor mounted in a warehouse ceiling. On a map, it's well within range of the nearest access point, but thick steel rafters and high shelving panels obstruct the Wi-Fi path. At that exact location, signal strength can fall below usable thresholds, causing intermittent dropouts or delayed transmissions that wouldn't be caught unless measured in proximity to the sensor itself.

It's also important that signal quality and load testing simulate real-world conditions to ensure infrastructure can handle demand as deployments scale. With east-west (internal, device-to-device) traffic increasing, IT teams should test throughput across switch-to-switch and access-layer connections. At the same time, north-south (external, device-to-cloud) traffic should be validated to confirm critical applications can reliably reach data center and cloud services. Together, these tests ensure both internal and external paths can support elevated loads without introducing bottlenecks.

Edge computing can unlock significant performance gains, reduce latency, and shift compute load from centralized infrastructure — but only when the underlying network is both performance-ready and secure. Success depends on more than shifting workloads closer to devices. It requires deliberate testing, full visibility, and cross-functional coordination. By validating latency, assessing wireless coverage, stress-testing both east-west and north-south links, and securing every endpoint, IT leaders can avoid common pitfalls and deliver the reliability, responsiveness, and protection their users expect.

Julio Petrovitch is a Product Manager at NetAlly

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Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

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The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...