Optimize network performance and availability with visibility and analytics across virtual and physical networks. Provide planning and recommendations for implementing micro-segmentation security, plus operational views to quickly and confidently manage and scale VMware NSX deployment.
Vrijdag, 23 september 2016
Donderdag, 22 september 2016
Best practices are described for optimizing Big Data applications running on VMware vSphere. Hardware, software, and vSphere configuration parameters are documented, as well as tuning parameters for the operating system,
Hadoop, and Spark. The Dell 12-server cluster (10 of which were dedicated to Hadoop worker processes) used in the test is described in detail, showing how the best practices were applied in its configuration. Test results are shown from two MapReduce and two Spark applications running on vSphere as well as directly on the hardware, and results from a reduced-size cluster of 5 worker servers.
The virtualized cluster outperformed the bare metal cluster by 5-10% for all MapReduce and Spark workloads with the exception of one Spark workload, which ran at parity. All workloads showed excellent scaling from 5 to 10 worker servers and from smaller to larger
Download the Technical White Paper: Big Data Performance on vSphere 6 Best Practices for Optimizing Virtualized Big Data Applications
A demo showing how a multi-site Cross-VC NSX can be used with third party security services; an example is demonstrated using Palo Alto Networks. Cross-VC NSX provide for consistent security across multiple vCenter domains and sites with Universal Distributed Firewall. Further, third party security services such as Palo Alto Networks can be leveraged for Application/L7-level security.