iMasterNCE-FabricInsight
Harness the power of Artificial Intelligence (AI) to proactively identify risks on networks before they impact services.
ผลิตภัณฑ์ โซลูชั่น และบริการสำหรับองค์กรธุรกิจ
Harness the power of Artificial Intelligence (AI) to proactively identify risks on networks before they impact services.
Huawei iMaster NCE-FabricInsight
Huawei iMaster NCE-FabricInsight is an intelligent analysis platform oriented to data center networks. Based on big data analysis technologies, it provides users with ubiquitous network application analysis and visualized presentation to eliminate the boundary between applications and networks.
iMaster NCE-FabricInsight collects all-scenario network data through Telemetry, displays network quality in real time, collects full flows at key network locations, and collects ERSPAN flows within fabrics, implementing correlation analysis of applications and networks, diagnosing faults in one-click mode, and safeguarding data center applications.
Network health evaluation
"1-3-5" fault locating
Application view
Specifications
| Feature | Description | |
| Network health evaluation | Comprehensive network health evaluation | Performs network-wide modeling based on the knowledge graph, provides 24/7 network quality assurance, visualizes network-wide resource status, and analyzes performance metrics such as traffic, bandwidth, and packet loss. Dynamically detects and proactively reports abnormal key metrics. Pushes health evaluation reports in real time or periodically, helping O&M personnel identify networks and improving O&M efficiency and service experience quality. |
| Telemetry-based network visualization in all scenarios | Collects statistics on metrics such as devices, boards, queues, interfaces, and entries based on gRPC and displays the dynamic baseline range of each metric based on the machine learning algorithm. In this way, the time when the baseline exception occurs can be quickly located and problems can be proactively identified before services are interrupted. Automatically associates the exception time point with the affected service flows, allowing users to view the flow behavior data that passes through the device and has connection setup failures at the exception time point. | |
| IP 360 | Provides IP 360 analysis to quickly learn about the number of online VMs and the distribution of top N switches connected to VMs, helping network administrators effectively plan resources in advance. Supports full lifecycle management of VMs on the entire network, displays VM logout, migration, and login records in real time, and provides network-wide IP snapshot analysis. It also compares all IP address changes before and after network changes, and checks whether exceptions such as VM logout occur. | |
| Network change visibility | Provides network snapshot management and supports automatic and manual synchronization of snapshots from 17 dimensions, including device configuration, entry, topology, capacity, and performance. Automatically analyzes differences before and after changes, and clearly displays detection results. | |
| "1-3-5" troubleshooting | "1-3-5" troubleshooting | Leverages Telemetry to collect data on the management, forwarding, and data planes of the entire network in all scenarios, and detects exceptions within 1 minute. Uses the knowledge graph to automatically identify the root causes of faults and potential risks within 3 minutes and provides effective rectification suggestions. Cooperates with iMaster NCE-Fabric to recommend fault handling plans, enabling typical faults to be quickly rectified within 5 minutes. |
| Intelligent source tracing of massive logs | Provides quick inference and aggregation capabilities for logs, quickly identifies root causes based on knowledge graph and AI algorithms, and displays fault propagation paths, as well as identifies the impact scope. This has little dependency on the experience of experts and greatly improves analysis efficiency. | |
| Risk evaluation | Performs inference and analysis on the reliability, consistency, performance load, capacity, and stability of data center networks based on systematic network modeling and provides network risk evaluation and predictive maintenance suggestions. During network health inspection, the product allows O&M personnel to use the risk evaluation function to identify network risks in advance and handle the risks before services are affected, ensuring network service quality. | |
| Intelligent analysis of network-wide logs | Visualizes network-wide log events, including multi-dimensional trends, distribution statistics, and details from Layer 0 to Layer 6. Supports noise reduction and convergence of abnormal logs. Specifically, it has 200+ preset default aggregation and clearance rules, as well as user-defined rules, improving log analysis efficiency. | |
| Application view | Emergent diagnosis | FabricInsight performs full flow mirroring on key network nodes and ERSPAN packet mirroring on all network nodes to search for forwarding paths of actual service flows in the entire DC based on source and destination IP addresses. It also identifies the status of objects such as devices, interfaces, and links on the paths, implementing correlation analysis of applications and networks as well as one-click diagnosis of application connectivity and poor-quality faults. It provides network path state and actual packets exchanged between applications as evidence, ensuring reliable and traceable data. |
| Service assurance | FabricInsight implements integrated application and network monitoring based on xFlow (intelligent full-flow analysis) and network monitoring capabilities. It monitors more than 140 performance metrics (70+ device-centric network metrics and 70+ flow-centric application metrics) in real time to proactively evaluate application experience and network quality of a single system. It also pre-defines key service views for key services and clarifies call chains during service interaction, such as payment and personal loan services, implementing real-time service quality monitoring from applications to networks. |
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