Cloud + Edge = Rethink Traditional Network Monitoring

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Cloud + Edge = Rethink Traditional Network Monitoring

Jul 12, 2021

The continued evolution toward cloud and edge is creating gaps for enterprises. Traditional network monitoring strategies, which weren’t designed to support this shift, are falling behind. The result is that organizations are losing their ability to identify potential problems, to fix issues quickly and to maintain a good user experience.

Businesses often find themselves struggling to keep up because cloud access and traditional monitoring are two different disciplines. One implies application and the other implies infrastructure, and the tools and techniques that are best suited to each aren’t designed to address the other.

But monitoring the cloud requires more than one, and even more than two, monitoring disciplines. Enterprises must have the ability to monitor:

  • the application or Kubernetes
  • path stability and networking toward the application or container
  • the underlying structure and compute
  • the path to the data center
  • the edge
  • the overlay
  • the end user and the connection to each end device

Given the expanded scope of what needs to be monitored, organizations also need a way to turn all those different data streams and metrics into useful information. They’re receiving data from the end user, the branch, the data center infrastructure, the cloud, Kubernetes and containers. Human oversight of so much information isn’t sustainable, particularly as businesses scale their use of cloud and edge services.

It’s time to rethink your monitoring strategy.

Moving beyond legacy solutions

Traditional tools aren’t designed to accomplish more complex monitoring activities such as understanding path stability toward a hyperscaler, for example. Managing path stability all the way through from the VPN to the branch, and from there to the Internet connection, the cloud onramps and through services such as Equinix to the hyperscalers—it’s far beyond the scope of many established network monitoring strategies. However, it’s a crucial chain to bring into the picture in modern environments. And, of course, IT needs a single pane of glass to maintain visibility across this sprawling network and its components, which simply isn’t possible with legacy monitoring solutions.

The challenges of so much complexity grow exponentially when internal monitoring teams are fragmented, or when too many disparate tools and dashboards are in use. Poor collaboration and slow identification and remediation of issues are among the primary problems for IT organizations working in silos, where a holistic approach to network monitoring is hampered at every turn by functional boundaries and system perimeters. The difficulties increase as new cloud and edge services are added and more devices try to connect through the network.


The reliance on cloud services is on the rise, and Gartner projects cloud will make up just over 14% of the total global enterprise IT spending market in 2024. That’s up from slightly more than 9% in 2020.


Cloud architecture doesn’t fit neatly into the traditional monitoring tools enterprises rely on today. In a modern environment that includes cloud services, identifying application performance problems requires monitoring not only the IT stack, but also the network and the infrastructure, and putting all of that insight to work holistically. The ability to stream huge volumes of raw from the different components within the monitoring discipline into a data lake becomes a critical component in an effective monitoring strategy.

The use of AIOps enables the analysis of all that data at scale to identify actionable insights—existing performance issues and trends over time in application performance, for example. By applying powerful AI/ML technologies, it’s then possible to begin predicting how changes across the network might impact performance. For enterprises that are already working to move their entire footprint to the cloud, this next-level monitoring strategy becomes not only more important, but absolutely critical to maintaining visibility into network performance and health.

With deep integration across the entire network and strong AI/ML capabilities, the right cloud-based monitoring platform delivers the functionality businesses need as their cloud and edge services increase. The ability to ingest numerous data streams and apply analytics across large datasets is key to a successful cloud journey.

Enterprises need to have the insight to discover and assess patterns and trends, and to understand their normal network state and identify when variations could occur that are likely to disrupt performance.

MTR drops when the right data is available to inform actions, and IT can proactively use observations gained from its monitoring toolset to avoid large outages and deliver better value to the business.

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