TL;DR:
- AI and ML are transforming network management in four key areas: traffic management, performance monitoring, capacity planning, and security monitoring.
- Traffic management techniques optimize physical performance and prioritize interactive applications.
- D-Link leads in onboard traffic management with AI-based systems integrated into their switches.
- Cisco utilizes AI in network monitoring software, while Cisco DNA Assurance offers comprehensive AI-driven analytics.
- AI enhances traditional network performance monitoring and refines root cause analysis.
- AI and ML significantly assist traffic analysis and enable adjustments to routes and traffic flow.
- D-Link’s autonomous traffic management on switches provides a quicker solution compared to Cisco’s external processes.
- AI-powered capacity planning tools expedite forecasting and adaptability in network management.
- SIEM tools leverage AI to detect suspicious activities and enhance security monitoring.
- UEBA offers precise user and endpoint activity tracking and is crucial in IPSs and NGAV systems.
- AI for network management is a game-changing tool that optimizes efficiency and productivity.
Main AI News:
The world of network management is fast-paced and demanding, requiring swift solutions to pressing issues. Thanks to the power of Artificial Intelligence (AI) and Machine Learning (ML), managing networks has taken a revolutionary turn, providing effective answers to urgent problems. In this article, we will explore four key areas where AI and ML are making a significant impact: traffic management, performance monitoring, capacity planning, and security monitoring. Let’s delve into how these groundbreaking technologies are transforming the network management landscape.
Traffic Management: Maximizing Efficiency and Performance
Traffic management plays a crucial role in network operations, and its close connection to performance monitoring cannot be overstated. By analyzing packet traffic across network devices, traffic management services enable efficient operations for performance systems, which rely on real-time updates from these devices.
To optimize physical performance, network managers can employ traffic-shaping techniques. This approach allows IT departments with budget constraints to plan capacity below peak throughput while ensuring priority access for interactive applications like VoIP, surveillance cameras, and video conferencing systems. One established technique is the use of a dedicated VLAN for voice traffic, allowing voice signals to be transmitted over the same wires used for data.
While AI technology is not yet widely adopted by switch manufacturers for onboard traffic management functions, D-Link has taken the lead in this area with its Auto Surveillance VLAN (ASV) and Auto Voice VLAN (AVV) systems integrated into their intelligent switches. ASV ensures uninterrupted security camera video feeds, while AVV manages voice traffic exclusively. With AI-powered recognition and prioritization capabilities, these switches eliminate the need for manual intervention in tagging VLAN packets or defining switch behavior.
Cisco Systems, a global leader in network switch production, offers AI products but does not implement artificial intelligence directly on its switches. Instead, Cisco shifts data to network monitoring and management software hosted on servers.
Performance Monitoring: Cisco’s AI-Driven DNA Assurance
Cisco has merged hardware and software products to develop a comprehensive AI-driven solution called “Cisco DNA Assurance.” This innovative package offers enhanced efficiency compared to traditional methods, highlighting the synergy between AI and ML alongside non-AI components. The Catalyst 9000 line of switches forms the physical foundation of this series, contributing critical data to the AI service.
The AI-powered addition to the package, known as Cisco AI Network Analytics, unleashes unprecedented analytics capabilities. Leveraging data from multiple sources, including switch metrics sampling, this state-of-the-art system detects and resolves network performance issues with unmatched precision.
AI-driven services do not directly influence network monitoring data collection. However, by examining linked data from multiple sources, these systems excel at diagnosing the root causes of issues. Leveraging the power of AI, businesses can streamline their services and achieve unparalleled productivity. It’s a paradigm shift that is transforming the way businesses operate.
Traditional Network Performance Monitoring Enhanced with AI Techniques
Network performance monitors have long been crucial in providing clear, insightful visuals of switch status on network management consoles. These monitors commonly rely on Simple Network Management Protocol (SNMP) services for their operation. SNMP-based monitoring remains an essential process that doesn’t require substitution by AI procedures. However, AI can refine root cause analysis for even more efficient results.
Cisco’s approach to network monitoring incorporates AI techniques into a scrutinized module, improving the detection and resolution of performance issues. This integration of AI and traditional network monitoring empowers businesses with more accurate insights into network performance.
Traffic Analysis: Unleashing the Power of AI and ML
Investigating the root cause of performance difficulties often requires analyzing traffic throughput calculations. Failure to address the issue comprehensively can result in problems migrating from one network node to another, causing further disruptions.
AI and ML play a significant role in enhancing traffic analysis for network management. Through continuous ML processes, real-time statistics and results can be collected, enabling minute adjustments to routes and traffic flow optimization. Techniques such as queuing and application prioritization become more effective with the assistance of AI and ML technologies.
Autonomous Traffic Management: D-Link’s Efficient Solution
D-Link takes a unique approach to traffic management by performing these tasks directly on the switch itself, providing a quicker and simpler solution compared to Cisco’s external AI-based traffic management processes. This immediacy delivers instant results, making it easier to achieve optimal performance.
Autonomous decision-making by network devices, bolstered by AI services, allows all devices on a network to operate with maximum efficiency. The D-Link strategy is particularly effective when all switches belong to the same AI-enhanced series.
Capacity Planning: AI-Driven Efficiency and Swift Adaptability
Capacity planning is a complex task that requires extensive data input, especially during unexpected equipment failures when immediate re-routing is necessary. Juggling long-term planning and rapid response to failures can put immense pressure on network staff. To alleviate this burden, automated analysis tools powered by AI and ML are essential.
Cisco Systems and Juniper Networks have integrated additional capabilities into their AI products to enhance capacity planning and traffic shaping. For instance, Enterprise Intelligence’s Service Delivery Intelligence (SDI) leverages AI to accelerate performance examination data searches, enabling quick problem identification and invaluable solutions. This AI-assisted capacity planning tool ensures maximum efficiency and accuracy.
Security Monitoring: AI and ML Unleash Advanced Protection
AI-driven triage in data examination, combined with the exceptional performance metrics of ML, is instrumental in advanced security monitoring. Two powerful tools for ensuring organizational security are Security Information and Event Management (SIEM) and User and Entity Behavior Analytics (UEBA).
SIEM Tools: Comprehensive Security Solutions
SIEM comprises two crucial components, Security Information Management (SIM) and Security Event Management (SEM), working together to create a comprehensive security solution. Security Event Management relies on network traffic analysis, while Security Information Management benefits from AI systems. These systems detect suspicious activity by carefully analyzing log files, providing a comprehensive overview of an organization’s security posture. AI techniques significantly decrease the time required to identify malicious activities, allowing for swift preventive actions.
UEBA: Precision User and Endpoint Activity Tracking
User and Entity Behavior Analytics (UEBA) play a vital role in monitoring user and endpoint activity with precision. By collecting log data from user accounts, IP addresses, and external sources, UEBA utilizes machine learning algorithms to detect behavioral changes and trigger alerts when necessary. UEBA is an integral part of modern cybersecurity systems, including Intrusion Detection Systems (IDS) and Next-Generation Antivirus Systems (NGAV), providing invaluable recognition capabilities.
The Rise of IPSs and NGAV Systems
UEBA’s relevance is paramount in Intrusion Prevention Systems (IPSs) and Next-Generation Antivirus Systems (NGAV), where automated mitigation measures are integrated with identity detection systems. IPS developers have incorporated UEBA routines to establish baselines, eliminating false-positive reporting and enhancing overall security. Next-generation antivirus systems leverage UEBA to detect viruses at the moment of infiltration, preventing potential damage and ensuring proactive protection.
AI for Network Management: A Game-Changing Tool
Artificial Intelligence is a game-changing tool in the field of network management. Leading network monitoring systems are leveraging AI and ML processes, realizing their immense value and benefits. As these technologies become more widespread, businesses can expect to reap the maximum advantages they offer.
Conclusion:
The integration of AI and ML in network management is reshaping the market landscape. Businesses that embrace these technologies gain a competitive edge by optimizing performance, enhancing security, and streamlining operations. AI-powered solutions enable more efficient traffic management, accurate performance monitoring, swift capacity planning, and advanced security measures. The market demands innovative network management systems that harness the power of AI and ML to deliver exceptional results and drive businesses toward success.
