Performance Monitoring for GCP, IBM, Kubernetes & Azure SQL Quest advanced features of Foglight
With now presented Foglight products Quest Software wants to eat the Performance Monitoring for Hybrid environments verb. Evolve 9.3 supports GCP, as well as Workload and cost management for Kubernetes; the Performance Investigator provides an analysis Toolset for Azure SQL Single Database and Elastic Pools.
The Cost-Director of Foglight Evolve takes into account the GCP and help to identify cost hybrid environments better than before, to manage and predict.
Quest Software (Quest) extends the functionality of Foglight Evolve and Foglight Performance Investigator. Users of hybrid infrastructures should go databases, as well as the consumption of resources so deep than in the past to analyze and, as a result determine which Workloads can migrate to the Cloud.
According to the manufacturer, Foglight Evolve 9.3 now fully supports the Google Cloud Platform (GCP). This includes performance Monitoring, and Cost Management. In addition, the manufacturer refers to a comprehensive and easy-to-use Workload and cost management Tool for Kubernetes environments. In order to take account of the growing use of Infrastructure as Code (IaC) account.
Automated Zombie hunting
It also offers a new Migration Assessment Tool for the IBM Cloud, as well as various automation capabilities. By Policy-Based Automation, organizations, virtual machines (VM) to start on Amazon Web Services (AWS) or Microsoft Azure in a time-controlled or can stop. The Cloud Optimizier Automation to set meanwhile VM Resizing and take care of potentially forgotten VMs, causing unnecessary costs (Zombie VMs)
Foglight for Databases
For now generally available Foglight for Databases 220.127.116.11 a new Performance Investigator (PI) for Azure SQL DB belongs to. Thus, the analysis Toolset for database professionals deliver far-reaching insights into the Performance of complex databases. The help, Workload data to break down and get a clear view of any potential Performance bottlenecks.
Users could compare in addition to actual Workloads with automated Foglight Baseline area; a Machine Learning (ML) based on the forecast of the Database load.