The ongoing integration of artificial intelligence (AI) and machine learning (ML) in network management continues to increase, with the expectation that most enterprises will have some level of automation in place within a few years.
While many enterprises are still on their first steps toward automation, others are already reaping significant benefits from their initial deployments. The reasons for this are multifold but include the idea that automation can help solve growing issues around speed, agility, and efficiency and help prevent human error from causing outages or misconfigurations across networks.
Since we cannot predict or control every eventuality, human decision-making and reaction times are not sufficient for managing an enterprise network. ML and AI can overcome these shortcomings by delivering insights based on historical information, which can be applied to current situations with unprecedented speed and accuracy.
What is AI and ML?
Artificial intelligence refers to systems capable of autonomously completing tasks (like human brain processes). AI systems learn and grow over time, making adjustments based on data and rules. For example, some AI will use simulation to observe real-world activity before applying what it learns to similar future situations.
As a subset of artificial intelligence, machine learning gives computers the ability to learn without being explicitly programmed. Instead, ML relies on large datasets and algorithms to teach computers how to make decisions. The more data it has, the better its algorithm and the smarter it gets.
Also read: Top Business AI Trends to Watch for 2022
What is Automated Networking?
Automated networking uses ML technology to automate routine network operations such as troubleshooting. It’s an industry effort to replace manual network management processes with automation through ML-based algorithms.
The technology relies on packet-level telemetry data generated by network devices and then analyzed by an ML algorithm. The algorithm learns how to recognize normal versus abnormal behavior patterns over time, predicting when a device might be having issues before they occur.
The goal is for networks to adjust and optimize themselves based on real-time traffic flows, configuration changes, software updates, and more without human intervention.
Applying AI/ML in Enterprise Networking
ML and AI help make enterprise networks more efficient by using data-driven algorithms to identify patterns within the enterprise infrastructure. For example, ML can be used through anomaly detection—or looking at changes within your environment over time and determining whether they fit within a normal pattern or not.
Here are eight roles that AI and ML play in network management:
Log analysis
The role of AI and ML in log analysis is simple: it detects, collects, and analyzes logs from all parts of an enterprise environment (e.g., routers, switches, WAN optimization devices). It then provides real-time insights into network performance, so you can pinpoint problems faster than ever before.
Advanced analytics
Traditional analytics have long been used for network operations, but they’re not ideal for helping you automate your network or make better capacity planning and security decisions.
AI and ML allow businesses to gather much more data about their networks, including how applications perform in production environments or how different types of attacks affect business processes.
Also read: Best Data Analytics Tools & Software
Performance monitoring
Nowadays, with software-defined networking (SDN) becoming increasingly popular, monitoring performance has become steadily more important.
SDN is often used as a means to help automate network tasks and free network administrators from mundane tasks, like troubleshooting SNMP (simple network management protocol) issues. Now, automated tools can help monitor traffic flows across an SDN-enabled network.
Security alerts
Automated networking requires an automated security system, which is why many companies are investing in AI and ML. As a result, organizations can now use these emerging technologies to automate network security tasks such as malware detection, vulnerability scanning, intrusion prevention systems (IPS), advanced threat protection (ATP), DDoS mitigation, and more.
With these technologies, enterprises can achieve better uptime while decreasing human error by leveraging automated networking to create more efficient networks for all users and devices.
Traffic management
Automated tools help manage traffic to optimize performance. With these tools, information about Internet Protocol (IP) addresses is automatically gathered for analysis. The data is then integrated with other business or engineering intelligence systems to automate network management tasks.
Intelligent programmable automation controller (IPAC)
A key component to automating networks with AI is programmable automation controllers (PACs). These devices allow network administrators to automate tasks using software instead of doing everything manually. As such, they can help network administrators keep up with changing demands more quickly than they could with manual solutions.
Autonomous scanning and patching
Modern switches employ AI to automate certain tasks, such as maintenance. These automated tools allow network administrators to shift their focus from a reactive state to one that is more proactive, allowing them to monitor and identify issues before they become problems. Some vendors even provide self-healing networks using AI techniques like ML.
The goal is to reduce or eliminate outages by automatically remediating faults as soon as they occur.
Automated provisioning
With automated provisioning, you can automate all aspects of network deployment, from initial configuration to ongoing maintenance. In addition, automated networking complements continuous service monitoring by measuring performance against SLAs, triggering specific actions based on threshold breaches or other changes, and proactively alerting administrators. The result is an automated network that’s always ready for use, even under heavy load.
Also read: Best Network Management Software & Tools
What are the Benefits to an Enterprise?
The key to successful network management is accuracy, speed, scale, reliability, and efficiency. Automated processes are more accurate than those that rely on human input, and machine learning algorithms can improve accuracy even further by refining their predictions over time.
- Speed: With AI/ML, enterprises can respond to changes quickly; if you have automated your network monitoring software, you will be able to identify an issue within seconds or minutes instead of hours or days.
- Scale: Refers to how many devices a single system can manage. Automation enables companies to monitor thousands of devices simultaneously.
- Reliability: Refers to how often a system fails. An automated system should fail less frequently than one that relies on humans for input.
- Efficiency: Automation leads to greater efficiency because it frees up resources for other tasks. When companies automate their networks, they don’t need as many people working in IT departments. This results in lower costs, which gives businesses more money to spend on other things.
What Challenges Still Exist for AI/ML in Networking Ops?
While there are many benefits to using automation systems like ML and AI, there are still challenges that must be overcome before they become mainstream. First, many enterprises have yet to fully embrace automation in general, meaning they may have difficulty adopting new techniques like ML even when those techniques offer clear benefits.
Other challenges include not gathering enough data for machine learning algorithms to work with, ensuring that all collected data is relevant, and avoiding bias in training datasets.
Adopting AI/ML in Enterprise Networks
Increasingly, enterprises want to connect their applications and IT resources as seamlessly as possible. And, because each business uses different technology from different vendors, integrating all of these pieces into one seamless application has been difficult.
This desire for ease of integration has driven companies to pursue greater use of automation software like orchestration systems and policy-based tools that run across multiple devices at once.
If done correctly, network automation should allow you to simplify complex tasks while increasing productivity and decreasing errors. With lower costs, faster speeds, and improved agility, automated networks are poised to improve operational efficiency for most organizations dramatically.
In addition, the automation of network operations allows employees to focus on higher-level problems, such as identifying potential issues and solving networkwide problems.
Read next: Automating DevOps with AI & ML
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