AI Operations (AIOps) harnesses the transformative power of Artificial Intelligence (AI) to revolutionize IT and network operations. This umbrella term encompasses various solutions that combine big data with AI-driven analytics and machine learning (ML) to automate and enhance manual processes. In IT and network management, AIOps processes vast amounts of diagnostic data and events in near real-time, identifying patterns and anomalies in system behaviour. This capability enables faster issue detection and resolution – often even before problems arise – complementing and enhancing traditional IT Service Management (ITSM) and Network Service Assurance solutions.
Key Benefits of AIOps in IT and Network Operations
AIOps can improve network and IT menagement performance through:
- Improved Operational Efficiency: By automating or replacing several manual processes the efficiency of IT and network management teams is increased.
- Faster Problem Resolution: Significantly reduced Mean Time to Repair (MTTR) by identifying and diagnosing critical issues almost instantly.
- Lower Operational Costs: Automating routine tasks like network monitoring allows organizations to manage complex networks with smaller teams.
- Reduced Manual Workloads: AIOps allows IT and network infrastructure to grow without a need for increase in team size and manual oversight.
- Improved User Experience: Reduced MTTR and predictive problem solving ensures high service availability and thus good experience for end-users.
Core AIOps Functions in IT and Networking
AIOps brings proactive approach that transforms traditional reactive IT and network management through several key AI/ML-driven capabilities:
- Automated Root Cause Analysis (RCA): AIOps swiftly identifies the root cause of network problems, eliminating the need for tedious manual analysis.
- Predictive Maintenance: By analysing historical data patterns, AIOps can foresee potential service disruptions and equipment failures before they occur.
- Alert and Ticket Correlation: AIOps analyses and correlates thousands of alerts, grouping related events into a single incident to alleviate ‘alert fatigue’.
- Self-Healing Capabilities: For known problems, AIOps can initiate automated fixes—such as restarting a failed device – without human intervention.
- Enhanced Security: It establishes a baseline of normal network behaviour and swiftly flags unusual activity like unauthorized access or abnormal data flows.
Using ‘Cloud Sourcing’ to Enhance AIOps for Wireless Network
Like any AI-driven solution, the effectiveness of AIOps hinges on the quality of the data it processes and analyses. This holds true for its application in wireless network operations. Vendors like Cisco, Huawei, and Aruba (HP) provide valuable network diagnostic data. However, their monitoring solutions often focus solely on the network infrastructure – covering access points, routers, switches, firewalls, and more. To fully leverage the power of AIOps in industrial WiFi network management, a holistic view is essential, including diagnostics from the end-user’s perspective.
The user network experience and overall network performance are closely linked yet fundamentally different. For instance, what an end-user perceives as a device malfunction may not stem from poor WiFi performance. Instead, it could be attributed to a delayed response from a DNS server, temporary signal disruption due to physical obstacles, or disabled fast roaming support on their device.
By deploying a network experience monitoring application on each end-user device, combined with specialized probes that continuously test devices, access points, interfering networks, and external internet connections, organizations can gather substantial amounts of valuable diagnostic data. This approach represents a form of ‘Cloud Sourcing,’ where hundreds of devices and probes report diagnostics from the individual user (or rather device) perspective, enhancing overall network visibility.
This wealth of diagnostic data can serve two critical functions: it can train AI-based analytics and help identify anomalies, disruptions, and malfunctions before they impact employees or customers. Furthermore, AI-driven analytics can recommend necessary actions – such as network and device configuration changes – to IT and network management staff and automate these tasks to facilitate ongoing improvements in network performance and user experience.
Conclusions
AIOps is rapidly becoming mainstream in IT and network operations across numerous industries. However, to effectively apply AIOps within wireless network operations, a comprehensive understanding of network experience from both infrastructure and end-user perspectives is essential. For large enterprises and industrial organizations managing complex wireless networks, implementing continuous monitoring solutions that deliver millions of data points to AI-driven analytics is not just beneficial – it’s imperative for ensuring optimal performance and outstanding user experiences.
No responses yet