Glossary

AIOps stands for Artificial Intelligence for IT Operations. It combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination. This approach enhances the efficiency and effectiveness of IT operations, making them more proactive and predictive.

Autonomous anomaly detection is an advanced technique that uses artificial intelligence and machine learning algorithms to automatically identify and flag irregularities or atypical patterns in data. These anomalies, which significantly deviate from established norms, are detected without direct human oversight. This method is crucial in various fields for preemptively recognizing and responding to potential issues or threats.

Automation & remediation in the context of NetOp Cloud refers to the process of automatically detecting network issues and executing predefined solutions. This system minimizes manual intervention, ensuring swift and efficient resolution of network problems, thus maintaining optimal network performance and reliability in cloud-managed environments.

Bandwidth monitoring involves the tracking and analysis of the amount of data transmitted over a network. It is crucial for ensuring optimal network performance, preventing bottlenecks, and managing network resources effectively, making it an essential component in the administration of network systems.

Cloud automation refers to the use of technology to automate manual processes in cloud computing environments. It encompasses various tasks like deploying servers, managing storage, and configuring networks. This automation leads to improved efficiency, reduced errors, and significant time savings in managing cloud resources.

Cloud-native Architecture refers to a method of building and running applications that fully exploit the advantages of the cloud computing model. It focuses on creating scalable and flexible applications that are resilient, manageable, and observable, designed specifically for cloud environments rather than traditional data centers.

Cloud infrastructure management is the process of overseeing and controlling the hardware and software resources in cloud environments. This management ensures optimized resource usage, performance, security, and compliance with standards and policies, making it a vital aspect of cloud computing operations.

Cloud monitoring is the practice of continuously overseeing and evaluating the operational status and performance of cloud-based services and infrastructures. It helps in detecting and addressing issues promptly, ensuring the smooth functioning of cloud environments and the reliability of cloud services.

Cloud networking is a method of configuring and managing network resources and services in the cloud. It offers scalable, dynamic, and efficient networking solutions, enabling easier management and deployment. This approach is particularly beneficial for handling complex networks and provides cost-effective, flexible networking capabilities suitable for various organizational needs.

Configuration drift is the phenomenon where network configurations unintentionally deviate from their standard or intended settings over time. It’s a common challenge in network management, often resulting from manual changes, patch updates, or differences in environment setups. Regular monitoring and automated reconciliation are essential to maintain consistency, security, and compliance.

Correlation in network management refers to the process of analyzing and linking related data points or events. This is essential for identifying trends, diagnosing issues, and optimizing network performance. It involves using advanced analytics to discern patterns that might indicate underlying problems or opportunities for improvement in network operations.

Container monitoring involves overseeing the performance, health, and security of software containers in cloud-based environments. This process includes tracking resource usage, application performance, and security vulnerabilities. It’s crucial for ensuring the containers operate efficiently and securely, especially in complex, dynamic environments where containerized applications play a pivotal role.

Dashboard reporting provides a comprehensive and interactive view of network performance and health through a graphical user interface. It aggregates key metrics and data points, enabling network administrators to quickly assess the network’s status, identify issues, and make informed decisions. Dashboards are customizable and often feature real-time data updates.

Data haystack refers to the massive volumes of data generated within network environments. Managing this data effectively involves filtering, sorting, and analyzing to extract meaningful insights. The challenge lies in identifying relevant information (the ‘needle’) amidst the vast and often unstructured data (‘haystack’).

Event correlation in network management is the process of analyzing and linking related network events to determine cause-and-effect relationships. It helps in quickly identifying and resolving network issues, improving overall network performance and security. This approach is vital for efficient problem resolution and proactive network management.

Fault management entails identifying, diagnosing, and resolving faults or issues within a network. It’s a critical component of network management, ensuring minimal disruption and maintaining high service quality. Fault management involves detecting anomalies, diagnosing their root cause, and implementing corrective actions to restore normal operations.

Flow analysis examines network traffic flows to understand usage patterns, detect anomalies, and optimize network performance. It involves analyzing the data packets moving through the network, providing insights into bandwidth usage, traffic trends, and potential security threats. This analysis is key for proactive network management.

High availability in network management refers to designing systems and infrastructures that ensure continuous operational performance with minimal downtime. It involves implementing redundant systems and failover mechanisms to provide uninterrupted service, even during hardware failures, maintenance, or other disruptions.

Hybrid cloud is a computing environment that combines public and private cloud infrastructures, allowing data and applications to move between them. This model offers flexibility, scalability, and optimized cost-efficiency, enabling organizations to leverage the best of both cloud worlds according to their specific needs.

Infrastructure as Code (IaC) is a practice where network and IT infrastructure are managed and provisioned through code, rather than through manual processes. This approach allows for more efficient, consistent, and repeatable deployment of resources, enhancing overall network management efficiency.

Infrastructure as a Service (IaaS) is a form of cloud computing that provides virtualized computing resources over the internet. In network management, IaaS offers a flexible, scalable, and cost-effective way to manage IT resources, including servers, storage, and networking components.

Log management involves collecting, storing, analyzing, and managing computer-generated logs. It’s crucial for network troubleshooting, security monitoring, and compliance with regulatory requirements, providing insights into the operational status of network components and applications.

Latency measurement in network management involves calculating the time it takes for data to travel from one point in the network to another. It’s a critical metric for assessing network performance, especially in applications requiring real-time data transfer.

Network Data visualization in network management is the practice of representing complex network data in a graphical or visual format. This approach makes it easier to interpret large volumes of data, identify trends, and communicate findings. Effective visualization aids in quick decision-making and simplifies the understanding of complex network scenarios.

Network Data aggregation in network management involves collecting, compiling, and analyzing data from various network sources. This process is vital for creating a comprehensive overview of network performance, identifying trends, and making data-driven decisions. Aggregated data helps in understanding network behavior, resource utilization, and potential areas for optimization.

Network monitoring is the continuous observation of a network’s performance and operation. It involves tracking metrics like traffic, bandwidth usage, and uptime to ensure the network functions efficiently and to identify and address issues promptly.

Network mapping involves creating a visual representation of the network’s structure and components. This is crucial for understanding the layout of the network, identifying relationships between different network devices, and aiding in troubleshooting and network planning.

Network KPI predictions in the context of NetOp Cloud refers to the forecasting of key performance indicators (KPIs) for network systems using predictive analytics. This approach utilizes historical data to anticipate future network performance, aiding in proactive decision-making and optimization of network efficiency and reliability.

Network discovery is the process of identifying devices and services on a network. This includes mapping network resources, understanding network structure, and documenting the various components. It’s essential for effective network management, security, and planning.

Network Traffic Analysis is the process of intercepting, recording, and analyzing network traffic to understand network behavior, optimize performance, and detect security threats. It’s crucial for maintaining efficient and secure network operations.

Network observability (NetOp) in the context of NetOp Cloud refers to the comprehensive monitoring and analysis of network data. It encompasses collecting, visualizing, and utilizing network metrics, logs, and traces to ensure optimal network performance, enabling proactive issue detection and efficient troubleshooting in cloud-managed networks.

Network Virtualization involves creating a virtual version of network resources. This technique allows for the simulation and testing of network configurations, leading to greater flexibility, scalability, and efficient resource utilization.

Network operations (NetOps) involves the activities, methodologies, and tools used to deploy, operate, and maintain network infrastructure. It’s focused on ensuring the network is reliable, efficient, and aligned with business objectives.

Network topology refers to the arrangement of elements in a communication network. It dictates how network devices are interconnected and can significantly impact the network’s performance and scalability. Common topologies include star, mesh, and ring.

Observability in network management refers to the ability to understand the internal states of the network by examining its outputs. It’s crucial for detecting and diagnosing issues, ensuring system reliability, and improving performance.

Packet sniffing involves capturing and analyzing network packets. It’s used in network management for monitoring network traffic, diagnosing problems, and ensuring security. Packet sniffers capture data as it travels across the network, providing insights into network health and performance.

Performance baseline is a set of metrics representing the standard performance of a network under normal conditions. It serves as a benchmark for comparing current network performance, aiding in identifying deviations and potential issues. Establishing a baseline is crucial for effective network management and troubleshooting.

Predictive network analytics in the context of NetOp Cloud involves using advanced analytics to forecast network behavior and potential issues. This predictive approach utilizes data trends and patterns to anticipate and mitigate network problems, enhancing operational efficiency and preventing downtime in cloud-managed network environments.

Proactive machine learning detection in the context of NetOp Cloud refers to the use of advanced machine learning algorithms to predictively identify and address potential network issues. This proactive approach ensures network reliability and performance by detecting anomalies and irregularities before they escalate into major problems.

Real-time Monitoring is the continuous tracking of network performance and activities as they occur. It’s essential for promptly detecting and responding to issues, maintaining network health, and ensuring the smooth operation of IT services.

Root cause analysis in network management is the process of identifying the fundamental cause of a problem. It involves a deep investigation of incidents to determine what happened, why it happened, and how to prevent it from happening again.

SNMP (Simple Network Management Protocol) is a protocol used for managing devices on IP networks. It’s used to monitor network-attached devices for conditions that warrant administrative attention. SNMP provides a standardized framework for network access to manageable devices.

Software-Defined Networking (SDN) is an approach to networking that uses software-based controllers or application programming interfaces (APIs) to direct traffic on the network and communicate with the underlying hardware infrastructure. This provides more flexibility and control over network resources.

SD-WAN is a specific application of SDN technology applied to WAN connections, which are used to connect enterprise networks over large geographic distances. It simplifies the management and operation of a WAN by decoupling the networking hardware from its control mechanism.

Shift left refers to the practice of integrating security and testing early in the software development lifecycle. It aims to improve quality, reduce long-term costs, and minimize risks by addressing issues early in the process.

Shift right complements Shift Left by focusing on testing and security in the later stages of development, including post-release. It emphasizes continuous testing, monitoring, and feedback to improve the quality and security of applications in production environments.

Syslog is a standard for message logging. It allows for the collection and storage of logs from various network devices, providing insights into network activity and aiding in troubleshooting and security analysis.

Telemetry in network management involves the automatic collection and transmission of data from remote or inaccessible sources. It’s used for monitoring network conditions, performance, and diagnostics, providing valuable insights for network optimization and management.

In network management, a threshold is a predefined value that triggers an alert or action when exceeded. Setting thresholds helps in proactive monitoring and managing network performance, ensuring prompt responses to potential issues.