Tag Archives: Turbonomic

Planning Cloud Migration with Turbonomic 5.9

The latest version of Turbonomic includes enhanced public cloud support; cloud cost tracking and budget management, as well as cloud migration planning. In this post we’ll look at the forecasting costs, and savings, for migration to public cloud using Turbonomic v5.9. To upgrade to version 5.9 review the release notes here and follow the offline update steps outlined […]

Turbonomic Integration with vRealize Automation

This post will walk through integrating Turbonomic with vRealize Automation (vRA). Support for vRealize Automation has been included with Turbonomic since the release of version 5.5. Turbonomic makes intelligent real time placement decisions for virtual machines based on application workload demands and available resource. When provisioning machines using vRA we can invoke a custom workflow to […]

Configuring a Turbonomic Cloud Target

Quick post covering how to add a cloud target such as AWS or Azure to Turbonomic Operations Manager. The steps outlined below assume that you have an existing Turbonomic appliance installed, for assistance with installing Turbononic see the Turbonomic Install Guide. If you are testing or building a proof of concept you can deploy a […]

Installing Turbonomic AWS Cloud Manager

This post will cover the installation of Turbonomic Cloud Manager for Amazon Web Services. Turbonomic is an autonomous management platform able to continuously analyse your environment and provide recommendations for downsizing applications without sacrificing performance. This saves administrators time and effort and directly reduces infrastructure costs for pay as you go cloud services. Each Turbonomic […]

Turbonomic Install Guide

Turbonomic Operations Manager (formerly VMTurbo) is an autonomic platform for heterogeneous environments to deliver application performance in any cloud; by providing real time actionable and automated recommendations to improve performance and make better use of existing infrastructure. This self management creates and maintains a desired state in the sweet spot between QoS and resource utilisation. […]