Netscout: Location’s Power in Networks Without Borders

The Power of Location in Networks Without Borders

NETSCOUT’s “visibility without borders” vision is focused on the idea that digital transformation and virtualization erase the borders across components and layers that exist in today’s networks, bringing end-to-end visibility into however networks work and perform. Demolishing the borders that isolate network elements frees operators from the constraints of location, however, it doesn’t abstract network performance from the location. On the contrary, networks without borders unlock the power of location.

Removing Borders within the Enterprise Network

Just as the internet takes down borders across cultures and nations whereas supporting extremely localized content and services, removing the borders in our communication networks permits operators to extract the value of location in ways in which aren’t attainable in today’s networks, wherever function remains tied to a fixed location among the network architecture, and traffic is treated as an identical stream of bits transmitted across the network.

As borders come down, operators not solely gain (and need) network visibility, they conjointly gain (and need) flexibility. During a virtualized network, they get to settle on what goes where. That functions ought to be kept during a centralized location or within the cloud? Which of them ought to instead be moved towards the edge? And wherever is that the suitable edge – the cell site, the basement of an enterprise, the central office, or a metropolitan data center? How distributed should the network be? And how should totally different traffic flows, services, and content varieties be managed among such distributed networks? That bit ought to be transmitted first?

Network Topology within the Age of 5G

In the age of 5G, networks become dynamic, agile, and self-optimizing, and performance progressively depends on real-time resource allocation and network topology–which during a virtualized network translates into the location of function.


And location doesn’t solely impact performance. It conjointly impacts the cost of deploying and running the network, the type of services and also the quality of service the network will support, and also the revenue streams it will command.


Latency may be a prime example of this. By deploying computing resources nearer to the edge and using network slicing to keep the latency low for specific types of traffic or services, operators need to modify their network’s topology, however, they’ll conjointly generate new revenues from new services that rely upon latency, like online gaming or some IoT enterprise applications.


Edge computing and network slicing are the main technologies that offer the location to its new prominence. They operate orthogonally: edge computing horizontally from the center to the periphery of the network; network slicing vertically with parallel channels that cross the network. Their intersection magnifies the power of location in optimizing the utilization of network resources. Not all traffic is formed equal, and edge computing and network slicing are designed to manage the variety in traffic requirements, among the capabilities of the deployed wireless infrastructure, and extract the very best value from the network topology.

Extracting more value From the Network

But the adoption of edge computing and network slicing is merely the primary step in extracting value from the choice of location. Even more significantly, operators need to decide a way to implement them to fully benefit from the latency – in addition as higher capacity, reliability, and security – that 5G guarantees. There’s no unique answer to the what-goes-where queries we asked earlier. every operator can need to find its own answers and because this is all new territory, the whole wireless ecosystem needs to learn – vendors included – a way to use the data available, however still mostly underused, to extract more value from their networks.


That begs the question of wherever the value of the network comes from. Traditional metrics, like throughout or dropped calls, are no longer enough to capture network value. To maximize network value, operators need to optimize network performance for specific outcomes and strategic goals.

Key queries Network Operators ought to raise to improve the value:

  • What should an operator optimize?
  • What cost-benefit tradeoffs it’s willing to make?
  • In the latency example, that applications ought to have guaranteed low latency?
  • And which of them are ok on best efforts?
  • How is the operator going to balance the requirements of various traffic flows cost-effectively?
  • How much is it willing to pay an extra cost and effort to lower the latency on some applications?
  • How much should it expect to save from running some traffic as best-efforts?
  • Operators got to answer these two sets of queries – what goes where and what to optimize – as they decide a way to deploy edge computing and network slicing in commercial deployments. they have visibility across the network to guide through this method, create the proper decisions, and still refine the capabilities of their networks.

Because edge computing and network slicing add two dimensions (horizontal and vertical) that move with one another, they conjointly increase the complexity of the optimization process and also the quantity of data to be processed. And to induce to the correct answers for their specific network, services, demand, and strategy, operators are moving to a lot of power but also a lot of intensive approaches to grasp their networks and use what they learn during a continuous optimization process:

  • Collect reliable, detailed, location-aware, real-time data on network performance at the application or service level
  • Develop the capabilities to access the data as required (e.g., performance data across vendors)
  • Drill down network data at the layer level, at the network slice level, and at the microservice level and relate it to the quality of experience and performance for various users or devices and services
  • Identify the relevant data (e.g., anomaly detection, user experience) and ignore the remainder
  • Analyze, monitor and troubleshoot the network in real-time, with a high spatial granularity
  • Generate responses to deal with issues and optimize the network topology and also the real-time resource allocation
  • Automate the method, and repeat to continue to improve network performance

This is a difficult transformation which will end up giving operators data that’s too detailed to lead to issue resolution or learning, making duplication and fragmentation if the optimization process is completed individually for various functions, or more generally creating excessive complexity. Visibility might end up obfuscating the workings of the network instead of exposing them.

Managing the complexity of modern Networking

To avoid falling into this predicament, operators need to establish a strong and reliable optimization process that permits them to travel deeper to induce a much better understanding of the network once needed, however without adding unmanageable overhead. the combination of learning (AI and machine learning) and automation can help operators manage the extra complexity that technologies like edge computing and network slicing bring and make it attainable to extract the worth of the location, and enable new ways in which to operate and profit from the network.


For sure, we are still taking the primary steps during this direction and also the move to a distributed, location-aware and function-aware network needs time, effort and commitment. however, it’s conjointly an opportunity that operators cannot afford to sit out if they need to make their 5G networks shine.

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