VMware launched the latest version of its management platform, vRealize Cloud Management 8.2, with new self-driving capabilities and integration with the Kubernetes orchestration engine.
vRealize Cloud Management is an automation platform available as a SaaS offering as well as for on-premises installation. Customers can use the platform for self-service provisioning of infrastructure, DevOps automation, and network automation. It acts as a single pane of glass to manage the day two operations of virtualization infrastructure running within the data center and the cloud.
With vRealize Cloud Management 8.2, VMware is bringing new AI-driven capabilities to its flagship management platform. Below is a quick overview of the enhancements:
VMware Cloud Templates
Formerly known as vRealize Automation blueprints, VMware Cloud Templates simplify defining consistent and repeatable deployments. Based on the Infrastructure as Code (IaC) paradigm, customers can use a canvas to design infrastructure templates that translate to a YAML file, which can be added to a version control system such as GitHub.
Apart from defining and creating deployment templates for vSphere, VMware Cloud Foundation and NSX, the tool also supports templates targeting mainstream cloud platforms, including AWS, Azure, and Google Cloud. VMware Cloud Templates supports existing IaC tools such as ServiceNow, Terraform and Ansible.
vRealize Cloud Management 8.2 integrates with VMware Tanzu Kubernetes Grid and Red Hat OpenShift platforms for auto-discovery of Kubernetes clusters. It can monitor health, performance, capacity, cost, and configuration of various Kubernetes primitives.
vRealize Log Insight 8.2 and its SaaS counterpart VMware vRealize Log Insight Cloud introduce enhanced Kubernetes support, deeper integration with VMware Cloud on AWS, and overall usability enhancements.
Customers get a unified view of the infrastructure running traditional VMs as well as Kubernetes clusters through a common console.
This latest release of vRealize includes feature integration with mainstream APM tools such as AppDynamics, Datadog and Dynatrace. The platform can discover applications, infrastructure resources based on vSphere, vSAN, and NSX to provide end-to-end visibility into the stack. Through the integration with vRealize Network Insight, customers can predict, prevent, and remediate issues in the context of applications.
VMware Cloud Monitoring
With vRealize Cloud Management 8.2, VMware claims Improved metric-correlation and new near real-time monitoring that enables enterprise observability. This enhancement helps customers in detecting performance and availability issues up to 15x faster.
Enhancements to native AWS management will unlock capacity calculations for EC2 instances and automatically import metrics into the monitoring platform for faster troubleshooting.
Cost and Capacity Planning
With its improvements to the pricing and costing engine, including daily virtual machine (VM) cost granularity, enhanced metering capabilities, and pricing support for non-VMware vRealize Automation workloads, VMware claims that it will help customers reduce cloud infrastructure costs.
Acquired earlier this year, Nyansa helped VMware with an AI-enhanced network analytics platform. Branded as Voyance, the tool automates the collection of metrics from network devices to correlate and contextualize it. With Nyansa, VMware added advanced network logging and analytics capabilities to its management platform.
The next acquisition, Blue Medora, brought the essential self-driving and AI-based operations to the vRealize Suite. The True Visibility product from Blue Medora provided integration solutions for vRealize Operations via management packs, which have become an integral part of vRealize. Blue Medora and VMware worked closely for years before the acquisition, helping both the companies align with customer requirements and industry trends.
AIOps is becoming an essential part of IT operations. The rise of cloud native platforms based on Kubernetes and the adoption of multi-cloud platforms demand an intelligent approach to infrastructure management through AIOps.