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:
- IT/OT production infrastructure
- Automation infrastructure
- Orchestration infrastructure
- 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.