Government networks are growing larger and more complex each day. Increasing numbers of network devices, servers and applications mean there’s less leeway for downtime, hiccups or problems of any sort, not to mention bandwidth.

Fortunately, the tricky art of network capacity planning is getting help from new artificial intelligence and automation tools that can help government IT pros support network growth and scalability while accounting for the challenges of today’s modern networking environments.

Let’s look at some of these challenges and explore how AI and automation can help government IT pros efficiently use their resources to alleviate these issues:

Hybrid IT complicates network capacity planning

The growing adoption of hybrid IT environments in government makes it hard to anticipate, plan, and manage network capacity. Legacy network monitoring tools for on-premises networks work well when the agency owns all the network devices, but what happens when it moves to the cloud?

Agencies need tools to scale across these dynamic environments, so they can achieve visibility into network performance across hybrid IT and use insights to make smart, informed capacity planning decisions. For instance, if users experience a degraded application experience, IT managers need deeper insights into whether the issue is with the app, the database connected to the app, systems where the app runs, or the network, such as monitoring critical network paths, so they can determine the appropriate measures needed to improve performance and health.

Networks are changing constantly

Today’s networks are in a state of flux. Legacy networks were reasonably flat and static; IT knew where switches and personnel were located and could monitor and control traffic flow. But enterprise architectures have evolved to extraordinarily complex environments with billions of connected devices, applications and users.

In the climate of constant change, it’s imperative for federal IT teams to find ways to maximize network capacity in ways mindful of the bandwidth needs of both users and applications. They must also prioritize and dynamically allocate bandwidth for different applications, so capacity is scaled during peak times and reduced during off-peak hours.

However, without a common way to keep up with a constantly changing network, it can be hard to predict and scale to meet traffic demands, identify problems and bottlenecks, and be more proactive in dealing with issues.

AI and automation can improve network capacity planning

For federal IT pros to address these growing challenges to network capacity planning, they need strong data. Data shines a light on the network’s current performance, helps analyze bandwidth utilization and traffic patterns, and informs future bandwidth demand.

Until recently, the data needed for these insights was limited to static reporting on bandwidth utilization. By pairing AI and automation, IT can drive smart, agile network planning actions across the entire network infrastructure.

The first step towards this desired state is traffic monitoring. As the network grows, changes, and more applications and users are added, AI enables network planners to capture large volumes of data from different sources to more accurately and precisely measure network utilization down to the application level. This enables federal IT pros to correlate past performance with future trends, and in real-time for quick remediation.

The next step is using this data to model network behavior based on how applications perform in different scenarios, such as peak and off-peak periods, so the network can be appropriately right sized.

Depending on the risk appetite of the agency, these predictive insights can be combined with automated network configuration practices like software defined networking (SDN) to scale networks more efficiently, even across hybrid IT deployments. Instead of a single network engineer controlling a couple of hundred switches, they could potentially manage thousands from a single pane of glass.

In doing so, IT can elastically scale the network infrastructure as and when it’s needed to make smarter use of existing resources and budgets. If an increase in traffic is predicted for a critical app, SDN can quickly increase the pipe or change the way the traffic is being routed in an automated way, no human involvement required.

Reaping the rewards of AI and automation

Together, AI and SDN allow federal IT teams to garner a more precise assessment of network capacity and performance utilization, so they can make real-time, automated decisions about capacity and avoid service degradation. Above all, with AI making the basic decisions about capacity and SDN applying a more automated approach to network provisioning, federal government IT managers will spend less time on the more mundane tasks of running agency networks and more time supporting strategic initiatives.

Jim Hansen is the vice president of Products, Application Management at SolarWinds

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