Automation is the key to maintaining optimal network performance and availability during tumultuous times. A resilient, automated network keeps functioning even if administrators can’t physically access the infrastructure or when a recession forces companies to reduce their IT workforce. A network automation framework includes all the tools, technologies, and practices required to build a resilient and fully automated enterprise network infrastructure.

The four building blocks of a resilient network automation framework include:

1. IT/OT production infrastructure
2. Automation infrastructure
3. Orchestration infrastructure
4. AIOps

In previous blogs, we focused on the building blocks that enable network automation and orchestration. In this blog, we’ll discuss how AIOps and machine learning help teams manage their automation and orchestration—and the massive amounts of data produced by their automated systems—more efficiently.

What is AIOps?

AIOps—artificial intelligence for IT operations—was originally introduced by Gartner in 2017. It uses AI technologies like machine learning (ML) and natural language processing (NLP) to analyze IT operations data. This data is pulled in from many different sources, including monitoring and visibility platforms, environmental monitoring sensors, event logs, and firewalls. AIOps utilizes that data to automate tasks like event correlation, anomaly detection, and root cause analysis (RCA) as well as to predict future outcomes and provide valuable business insights.

What’s the difference between AI and machine learning?

Before we delve any deeper into the specific uses for and benefits of AIOps, it’s important to clarify what we mean when we talk about technologies like AI and machine learning.

AI stands for artificial intelligence, which is defined as a computer’s ability to display human-like intelligence through behaviors like learning from new data, drawing conclusions based on that data, and coming up with solutions to problems.

Machine learning, on the other hand, describes a computer’s ability to process large quantities of data and learn from it. Learning is a major requirement for AI, which means that all machine learning applications could be considered AI. However, not all AI is machine learning—artificial intelligence uses additional technology to make decisions, solve problems, and perform other automated functions.

Essentially, AI describes a broad range of technologies, whereas machine learning is a more specific subset of technologies included in the AI umbrella. In the context of AIOps, however, machine learning is often the only artificial intelligence technology in use.

Using AIOps and machine learning to manage automated network infrastructure

In an automated enterprise network, AIOps and machine learning use advanced algorithms to provide in-depth analysis of all the data collected from production infrastructure, automation components, and orchestration systems. AIOps solutions can even take things a step further by making decisions and solving problems based on the results of that data analysis.

Some examples of how AIOps and machine learning can be used to manage automated network infrastructure include:

Security

Cyberattacks and data breaches are major threats to the reliability and performance of network infrastructure. In addition to the financial losses caused by sensitive data exfiltration and reputation loss, security breaches are also a leading cause of downtime, which directly impacts business revenue. According to the ITIC’s 2022 Global Server Hardware Security survey, 76% of enterprises cited security breaches as the top cause of downtime. That means network security is paramount to the resilience of an automated infrastructure.

For many years, network security relied on signature-based detection for jobs like intrusion prevention, antivirus, and spam filtering. Signature-based detection involves comparing an incoming request to a database of known threats to see if it matches—if not, it’s assumed to be safe and allowed into the network. This approach only works if the database is kept up to date and if all incoming threats have been identified in the past. Signature-based detection often fails to catch zero-day exploits or novel malware that it hasn’t seen before, plus it tends to generate a lot of false positives.

AIOps security solutions overcome this problem by learning from past experiences. Machine learning is able to extract information from past threats and then develop algorithms to recognize, predict, and categorize a new threat that it’s never seen before. This makes AIOps adept at preventing new threats as well as detecting ones already on the network.

You can also use AIOps to analyze data from infrastructure logs and other security solutions to spot the more subtle signs of a breach that’s already happened or that’s currently taking place. For example, AIOps and machine learning may detect an unusually large amount of data leaving the network, which could indicate that a malicious actor is exfiltrating sensitive information. Another security use for AI is called User and Entity Behavior Analytics (UEBA), which inspects account activity on a network and reports anomalous behavior that could indicate an account has been compromised.

AIOps improves upon automated network security solutions by using adaptive learning and predictive analysis to detect new and unusual threats with a greater degree of accuracy. It also takes advantage of the massive amounts of data produced by security appliances and network infrastructure to identify the subtle clues left behind by sophisticated cybercriminals. This makes AIOps a valuable tool for maintaining the security and availability of an automated network infrastructure.

Monitoring

An automated network infrastructure generates a massive quantity of logs that can be used to assess health and performance as well as to identify potential issues before they cause any outages or downtime. However, humans aren’t very good at sifting through large amounts of data to figure out what’s relevant and what isn’t.

Many monitoring solutions use basic automation to help weed out important data, for example by letting admins set performance thresholds that generate automatic alerts when devices fall out of the optimal operating range. However, this kind of automation creates a lot of false positives, which are tedious to sort through and could lead to admin neglect or complacency. It can also only detect specific symptoms and issues that fall within the scope of the monitoring thresholds programmed by a sysadmin, which means it can’t adapt to changing circumstances or predict new problems that weren’t anticipated by the admin in advance.

An AIOps monitoring solution collects all the logs produced by automated infrastructure and analyzes them in real time. Sysadmins can still set performance thresholds and program automatic alerts, but AIOps also uses machine learning to “think outside the box” by recognizing patterns and detecting anomalies it wasn’t programmed to look for. That means issues are identified faster, potentially before they cause any noticeable problems for end-users.

Machine learning also gives AIOps monitoring solutions the ability to track performance over time and predict future outcomes based on historical data. For example, organizations can use AIOps analysis to plan infrastructure upgrade schedules based on when device performance is predicted to start degrading, or in advance of a predicted spike in demand for a particular location. This gives CIOs and IT managers the ability to make smarter decisions about where and when to invest money and how to prioritize new initiatives.

AIOps monitoring solutions work well with data lakes, which are large repositories for unstructured data. Data lakes are an efficient way to process large quantities of data, such as monitoring and security logs. This enables the data to be used by AIOps and other big data tools.

AIOps transforms the flood of logs generated by complex, automated network infrastructures into actionable data. Enterprises can use AIOps and machine learning to catch subtle issues before they turn into major problems, improving the performance and availability of network resources. AIOps also provides valuable business intelligence that organizations can use to make smarter and more cost-effective decisions during recessions and other tumultuous events.

Root cause analysis (RCA)

When there’s an outage or other business interruption, the main priority is fixing whatever is preventing systems from operating normally so that systems can get back online. Often, this means fixing the symptoms of some deeper underlying problem. If that core problem isn’t addressed, it’s likely to cause another outage in the future. That means administrators must perform a root cause analysis (RCA) to discover the source, come up with a fix, and document everything for future reference.

Root cause analysis involves digging through devices, applications, and service logs, which human engineers can’t do as efficiently as AI solutions. AIOps can comb through all the relevant logs to determine the most likely cause of the problem as well as recommend the best solution to fix it. Incidents are automatically generated, prioritized, and assigned to the correct team for resolution, ensuring the core problem is quickly and thoroughly fixed to prevent future outages.

Some AIOps solutions can even automatically resolve some issues without waiting for a human engineer to receive an alert, log in to the system, identify the problem, and implement a solution. This can significantly reduce the mean time to resolution (MTTR) and minimize expensive business interruptions.

Sorting through data is what AIOps does best, which makes it the perfect tool for RCA. AIOps can determine the root cause of automated infrastructure failures much faster than human admins, making it easier to fix these underlying problems before they cause future downtime. AI can even proactively implement fixes while issues are ongoing, allowing businesses to recover faster and reduce the cost of outages.

Implementing AIOps and machine learning in a resilient network automation framework

AIOps is the final layer of the network automation framework because it reduces the management complexity involved in monitoring, troubleshooting, and optimizing automated network infrastructure. Because AIOps needs to collect logs from every single component of the network automation framework, it must be a vendor-neutral solution that has access to your orchestration platform as well as all your management hardware and software. This will be much easier if your orchestration, automation infrastructure, and IT/OT management infrastructure are also vendor-neutral.

For example, the Nodegrid platform from ZPE Systems includes management devices like Gen 3 OOB serial consoles and integrated network edge routers that can bring your entire mixed-vendor environment under a single management umbrella. Nodegrid hardware is truly vendor-neutral, which means it can directly host your AIOps applications to help consolidate devices in your rack. The ZPE Cloud infrastructure orchestration platform also supports integrations with third-party and cloud-based AIOps solutions. Either way, you get network infrastructure management, monitoring, automation, orchestration, and AIOps in a single platform.

ZPE’s Network Automation Blueprint

AIOps works together with IT/OT production infrastructure, automation infrastructure, and orchestration to ensure network resiliency during uncertain times. The Network Automation Blueprint from ZPE Systems provides a reference architecture for achieving Gartner’s definition of hyperautomation as well as meeting the Open Networking User Group (ONUG) Orchestration and Automation recommendations.

Download the Network Automation Blueprint today and see how all these building blocks fit together to ensure network resiliency.

Ready to learn more about implementing AIOps and machine learning?

To learn more about implementing AIOps and machine learning with Nodegrid, contact ZPE Systems today.

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As the recession continues to affect businesses across all industries, enterprise network resilience has never been more critical. The typical outage costs at least $100,000—a price tag that most companies can’t easily absorb in the current economic climate. However, decreasing business revenues have caused many companies, especially in the tech industry, to lay off large portions of their key IT staff. That means there are fewer administrators to monitor and manage network infrastructure and fewer engineers available to respond to issues and recover from outages.

Network automation is the key to ensuring 24/7 availability and optimal performance with less human interaction. A network automation framework provides all the tools and guidance needed to create a fully-automated network infrastructure that’s resilient to failure.

The four building blocks of a resilient network automation framework include:

1. IT/OT production infrastructure
2. Automation infrastructure
3. Orchestration infrastructure
4. AIOps

In previous blogs we discussed the role of IT/OT production infrastructure in network automation and how an IT/OT convergence strategy accelerates network automation. We also described the automation infrastructure components that enable end-to-end network automation. In this post, we’ll explain how infrastructure orchestration and automation build upon the previous two layers to enable streamlined, hyperautomated network resiliency. Our final blog in the series will conclude with a guide to using AIOps and other machine learning technologies to complete the network automation framework.

What is infrastructure orchestration and automation?

The infrastructure orchestration and automation layer contains the tools and paradigms used to efficiently manage and control that automation. The core components of infrastructure orchestration and automation include:

Version control

The automation infrastructure layer uses infrastructure as code (IaC) to decouple device configurations from the underlying hardware so they can be written as scripts or definition files that automatically provision network resources. In addition, this layer uses software-defined networking (SDN) to create a virtual control plane that overlays the production network infrastructure, allowing network management and optimization tasks to be written as automated scripts.

The goal of IaC and SDN is to reduce human error, speed up device provisioning, and build a more streamlined and resilient network infrastructure. However, IaC and SDN programming can be very complex, and not all sysadmins and network administrators are expert coders. In addition, an automated enterprise network has hundreds or even thousands of these definition files and scripts to store, manage, and deploy.

This is why a network automation framework should include version control in the orchestration and automation layer. Version control is a very familiar concept to programmers, especially in DevOps environments, but not all network and infrastructure teams have used it before. Version control involves storing all code in a centralized repository and then tracking and managing changes to that code.

Let’s say one administrator is responsible for configuring and maintaining the IaC definition file used to provision a particular model of Meraki AP. Here are some examples of how that workflow could break down when that one admin is out of the office for an extended period of time due to COVID-19 or gets laid off due to cutbacks in the organization:

• Twenty new Meraki APs need to be deployed to a new site with identical configurations.
• The existing definition needs to be updated and pushed out ASAP to patch a security vulnerability.
• Someone discovers an error in the current version and they need to roll back to a previous configuration.

A version control system for IaC and SDN acts as the single source of truth for the entire automated infrastructure. All automation scripts and definition files are stored in one centralized location, so anyone with authorization can deploy identical devices with the push of a button. When an admin needs to change the code, those changes are tracked and can be rolled back at any time if a mistake is made. Version control systems even allow admins to leave notes explaining the reasoning or logic behind individual changes, so other team members can pick up where they left off, or in their absence, identify the root cause of issues.

Another key benefit of version control is that it facilitates the use of automated testing. QA and security analysts can run automated scans on code in the version control repository pre-production, so any misconfigurations or security vulnerabilities are identified and fixed before deployment. This reduces the risk of human error and improves the security and resiliency of the automated network infrastructure.

Version control is a core component of infrastructure orchestration and automation because it serves as the single source of truth for the entire automated network architecture.

Orchestrator

Automation is meant to make life easier, but it can be very complicated to manage on a large scale. Modern enterprise network architectures include thousands of moving parts in locations around the world and in the cloud. Automating each of these workflows means writing, testing, deploying, managing, and troubleshooting many different definition files and automation scripts. Doing all of that manually adds more work to overloaded and under-resourced network infrastructure teams, which increases the risk of something going wrong. Simply put, organizations need a way to automate their automation.

An orchestrator is a tool used to control all of the automated workflows on an enterprise network, just like a conductor orchestrates many different instruments and musicians into one cohesive symphony. An orchestrator uses management devices, like Gen 3 OOB serial consoles and SD-WAN gateway routers, to gain control over the physical and virtual network infrastructure. Administrators program the orchestrator to automatically deploy definition files or networking scripts (which it pulls from the version control system) in response to certain triggers. That means admins could potentially automate every step in every workflow, removing the need for human intervention and reducing the chance of errors.

Plus, an orchestrator can react to events much faster than even the best administrator. For example, if a spike in demand is overloading resources at one regional data center, the orchestrator can instantly deploy automated load-balancing workflows to reroute traffic before end-users notice any performance issues. This allows enterprises to maintain 24/7 network availability and performance even with reduced IT staff.

As part of a resilient network automation framework, the orchestrator should be vendor-agnostic (vendor-neutral). It needs to be compatible with all of the automation infrastructure components, as well as the production IT/OT solutions. It also needs to support all of the major third-party automation vendors, such as Ansible and Gluware, to give infrastructure teams the flexibility to use the tools they’re most comfortable with and that work best in their enterprise’s unique environment. Finally, the orchestrator needs to integrate with other tools within the orchestration and automation layer, including the version control system and the monitoring and analytics platform.

The orchestrator is what gives the “orchestration and automation” layer its name. It provides admins with the ability to automatically manage all the automated workflows that make up a resilient network infrastructure. An orchestrator reduces the risk of outages caused by human error and can automatically respond to and prevent potential issues.

Visibility & insights

It’s tempting to think of infrastructure orchestration and automation as a “set it and forget it” solution that can perfectly manage an enterprise network without any human oversight, but the technology isn’t quite there yet. Administrators need a way to monitor all the automated workflows, identify problems the orchestrator may have missed, and analyze the health and performance of the network infrastructure.

A visibility and insights platform collects logs from all the various components of the automated network infrastructure and aggregates the data in one centralized location. It provides visualizations of current device health and network performance, and may even include predictive analysis to power business insights. This gives administrators a big-picture overview of distributed, complex, and automated network architectures so they can ensure continuous availability and optimal performance.

As with the version control system and the orchestrator, the visibility and insights solution needs to be vendor-agnostic so it can dig into every single hardware and software solution in the automated network infrastructure. In a resilient network automation framework, the vendor-neutral version control, orchestrator, and visibility solutions are all combined in a single platform.

Infrastructure orchestration and automation with a single platform

A unified infrastructure orchestration and automation platform like ZPE Cloud simplifies the control and management of a fully-automated enterprise network. ZPE Cloud uses Nodegrid hardware—such as Gen 3 OOB serial consoles and integrated network edge routers—to deliver orchestration and automation to large, distributed, multi-vendor network infrastructures. The ZPE Cloud management app supports integrations with your choice of third-party version control and infrastructure automation solutions, or you can use Nodegrid hardware to directly host your automation software.

With ZPE Cloud, you also get comprehensive monitoring data on all connected infrastructure, plus, you can use Nodegrid environmental monitor sensors to gain insights on conditions in remote data centers and network closets.

ZPE’s Network Automation Blueprint

Infrastructure orchestration and automation works together with IT/OT production infrastructure, automation infrastructure, and AIOps to ensure network resiliency during uncertain times. The Network Automation Blueprint from ZPE Systems provides a reference architecture for achieving Gartner’s definition of hyperautomation as well as meeting the Open Networking User Group (ONUG) Orchestration and Automation recommendations.

In a future blog post, we’ll discuss the remaining building block of the Network Automation Blueprint in depth. In the meantime, you can read about IT/OT production infrastructure and automation infrastructure, or click here to get a sneak peek of the blueprint, which includes a 10-step checklist to get started with automation now.

Ready to learn more about infrastructure orchestration and automation?

To learn more about infrastructure orchestration and automation with ZPE Cloud and Nodegrid, contact ZPE Systems today.

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