Network management is a challenge that keeps growing as networks increase in size and constant, stumble-free uptime becomes a non-negotiable expectation. IT teams are getting stressed-out and overworked, and need all the help they can get from new tools and time-saving platforms.
Consequently, the world of network management is delighted by the arrival of artificial intelligence (AI) and machine learning (ML) network management applications. These platforms are still relatively new, and enterprises are only just beginning to implement them, but farsighted IT teams see AI and ML as the new supertools for network management and operations.
Use cases for AI and ML network management cover a broad field, including predictive maintenance; anomaly detection; automated configuration, policy management, and network monitoring; network optimization; and security incident alerts and remediation, as well as self-healing networks. With the help of AI and ML, network management and network operations teams can lower costs, boost efficiency, raise network resilience and performance; and improve security standards.
According to Ron Howell, managing network architect for cloud infrastructure services at Capgemini North America, AI and ML for network management “helps network managers achieve greater security and be more compliant. With the power of AI, network management can more easily achieve greater efficiency, compliance, and security.”
Predictive maintenance can be a game changer. With predictive maintenance, network managers can proactively replace hardware before it fails, and address nascent issues while they are still relatively minor and can be resolved more easily and quickly. This helps IT teams reduce downtime, improve network performance, and reduce the costs and disruptions associated with unexpected failures.
Additionally, predictive maintenance models estimate the probability of device failure within a given timeframe, allowing IT teams to optimize maintenance scheduling or replacements. Routine maintenance can take place during planned downtime, enabling teams to work proactively to enhance network resilience rather than reacting to unexpected failures and constantly rushing to put out fires.
Predictive maintenance involves gathering masses of data from network devices such as routers, switches, access points and servers, processing and analyzing it, and recognizing patterns and anomalies that may indicate impending failures. It’s impossible without AI and ML. The volume of data is far too great for manual analysis to handle. Only ML algorithms can process all the data points, analyze them, and identify trends and outliers that indicate potential outages, failures, or bottlenecks before they occur.
ML-powered network management goes beyond crisis situations and predicting failures. ML algorithms also assist IT teams to optimize networks for improved performance. The models analyze traffic patterns and detect and resolve bottlenecks, latency, and congestion, thereby ensuring that network resources are used efficiently and can scale with the needs of the organization.
AI analytics tools can deliver real-time insights into network performance and usage, directing network managers to those areas where capacity needs to be increased or routing should be adjusted. ML-powered bandwidth management is able to identify and prioritize network traffic based on its importance.
Many networks are running with incorrect traffic shaping and Quality of Service (QoS) configurations, which throw traffic limitations and prioritizations out of balance and cause performance issues. AI tools can monitor these metrics and adjust settings, helping prevent the issues from arising.
Load balancing is another area where AI makes a vital contribution. Load balancing is highly complex, requiring real time knowledge of traffic levels, deep understanding of typical traffic patterns, and the ability to consider numerous devices at the same time. It’s extremely difficult to do this successfully using manual processes, but AI algorithms can easily balance traffic across multiple servers, helping prevent slowdowns and performance issues.
In a similar way, ML and AI can manage dynamic routing algorithms which automatically select the best path that network traffic should take through a complicated system. This can help reduce latency and ensure that traffic is delivered efficiently.
One of the most exciting elements of AI and ML network management is that IT teams can use it to automate routine yet vital tasks, like configuration management, network policy management, device management, and network monitoring, freeing employees to focus on higher-level issues and strategic planning.
Automating network monitoring for KPIs like bandwidth usage, latency, and congestion saves time and boosts overall network performance. AI tools can set performance thresholds and generate alerts when performance degrades, allowing IT teams to improve maintenance processes and reduce issues that lead to unexpected downtime. Device inventory, firmware updates, and device health monitoring can also be handled by AI platforms, which automatically reach out to vendors when bug fixes or updates are released or when hardware approaches the end of its life.
Network management teams are now able to automate the configuration of network devices like firewalls, routers and access points, and automate setup for VLANs, routing tables, and access control lists. Automation helps reduce the risk of manual and operational errors, which often require a lot of time and money to resolve, while revealing opportunities to save resources and increase efficiency.
Policy management can also be automated, including tasks like creating and applying network policies, monitoring policy compliance, and making changes to security policies, Quality of Service (QoS) policies, traffic shaping policies, and more.
Self-healing networks have long been a dream for network management teams, but AI makes them into a reality. AI network monitoring can detect and diagnose network traffic issues like packet loss, latency, and congestion in real time, and instantly apply automated fault resolution. This involves corrective actions such as rerouting traffic, applying QoS, allocating additional bandwidth, or switching to redundant network paths.
Over time, AI-powered self-healing networks are able to learn from past performance issues, recognize the earliest signs of a potential fault, and proactively adjust network configuration settings, routing decisions, or bandwidth allocations to prevent the fault from actualizing. Self-healing networks also apply automated monitoring and network optimization to ensure that the network is always operating at peak performance levels.
AI and ML offer the capabilities to significantly raise network security levels. AI algorithms analyze traffic patterns and compare them to historical data, enabling them to detect the anomalies that could indicate a security threat. The platform immediately notifies security teams to investigate, thereby preventing potential security incidents and resolving data breaches before they cause too much damage.
ML network monitoring can also spot suspicious behavior, like unusual data transfers or attempts to access sensitive data, or atypical changes in employee behavior, helping limit the risks of an internal data breach.
What’s more, ML algorithms can be trained to respond to security threats in real time, drastically speeding up response times and hardening the overall security profile. If the system spots a possible breach, it could automatically isolate those parts of the system that could be affected. Risky IP addresses can be blocked instantly to lower the risks of a cyber attack.
Overall, AI and machine learning are transforming network management by providing IT teams with new tools and capabilities to monitor, analyze, and optimize network performance, leading to more efficient and reliable networks. By using AI and ML algorithms for task automation, predictive management, and network monitoring, network management teams can raise efficiency, lower costs, improve network performance, harden the security profile, and increase resilience across their networks.
To learn more about how AI and machine learning are impacting network management, speak to us at NetOp.
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