VMwareのSDDCソリューションであるNSXとJuniperの連動について、ジュニパーネットワークスのSEがProof of Concept 及び、デザイン検討を行った資料です。
Juniperの最新アーキテクチャ、Clos IP FabricとVMware NSXの連動によるSDDCの世界をご堪能ください。
VMwareのSDDCソリューションであるNSXとJuniperの連動について、ジュニパーネットワークスのSEがProof of Concept 及び、デザイン検討を行った資料です。
Juniperの最新アーキテクチャ、Clos IP FabricとVMware NSXの連動によるSDDCの世界をご堪能ください。
This document provides an example of the physical connection configuration of a PV-FC-180 device for network monitoring. The PV-FC-180 is connected to a monitoring network and Coreflow2 for data collection. It mirrors traffic from two campus side networks and the Internet across four ports to an ESXi server running ExtremeAnalytics software for traffic aggregation, collection, and analysis of FlowData accessible via HTTPS.
This document compares the specifications of Extreme Networks' VSP 8600, ERS 5900, VSP 7200, ERS 3600 switches to competing products from other vendors such as Brocade's BD 8K, BD 8810, Juniper's X670-G2, and Extreme's own X460-G2 and X440-G2 switches. Key specifications compared include switching capacity, port density of various speeds, MAC address table size, routing capabilities, airflow design, and support for features like MLAG and virtual routing. The VSP 8600 is highlighted as having much higher switching capacity and port density compared to the Brocade BD 8K.
The document discusses several case studies of organizations deploying Wi-Fi networks using Extreme Networks solutions in challenging environments. These include a retail chain deploying Wi-Fi in large warehouses, a seaport installing Wi-Fi across its facilities, a public transportation provider giving passengers Wi-Fi access on buses, and a mining company connecting equipment across remote mine sites. Extreme Networks solutions provided reliable and secure connectivity through customizable access points and network management tools in each scenario.
This document provides a tutorial on machine learning in Python. It covers 14 tutorials on topics like loading and preparing data, evaluating models, improving accuracy with techniques like hyperparameter tuning and ensemble learning. The tutorials also define key terms and provide references to machine learning algorithms and datasets. The overall workflow moves from loading and exploring data to developing and selecting models to finalizing and validating a model.