How AI for IT operations improves VDI management
Enterprise AI tools help IT pros improve VDI management by providing accurate estimates of resource requirements, root cause analysis of issues and more.
Machine learning and AI for IT operations continue to gain traction, so it's no surprise that both technologies are working their way into VDI management.
IT can use AI and machine learning to improve security, resource utilization, capacity planning, storage management and a number of other VDI-related operations -- especially in large or complex deployments. It may take time for organizations to fully integrate AI as part of their VDI management processes, but vendors are now including AI and machine learning in their software for organizations to experiment with.
VDI management could undergo a significant transformation in the near future thanks to the rise of enterprise AI and machine learning.
AI for IT operations
Advancements around AI in the enterprise are giving rise to AI for IT operations (AIOps), a methodology that incorporates big data analytics, machine learning and other AI technologies to streamline operations.
AIOps can help with capacity planning and resource allocation by mapping workloads to the right compute, network and storage resources while also providing predictive intelligence to automatically scale those resources. AIOps makes it possible to more accurately detect and respond to security threats, service disruptions, storage failures and other issues while automating other routine processes.
How are AI and machine learning different?
People sometimes use the terms AI and machine learning interchangeably, but they have distinct meanings.
AI simulates human intelligence processes in different types of machines; computing systems are the most common platform to run AI. Its processes use learning and reasoning to examine data and draw conclusions with ranging levels of definitiveness. AI processes are also self-correcting, which enables them to continuously improve as they analyze more data.
A machine learning program makes it possible for a computer to execute operations without explicit programming. The software's machine learning processes search through the collected data to uncover patterns and adjust the programmed actions accordingly. As new data becomes available, the software continues to learn, develop and modify its actions.
AI for IT operations innovations can also help IT pros alleviate some pain points of VDI management. VDI platforms such as Citrix XenDesktop and VMware Horizon come with many of their own tools for managing VDI deployments, but connecting the dots across different systems can be difficult -- especially in siloed organizations where multiple groups of IT pros are focused on their own areas of responsibility.
For example, one group might manage the VDI servers while another group manages the storage platforms. An effective AI for IT operations program can correlate events across systems, leading to faster resolutions and fewer user disruptions.
AI moves into VDI software
As AI in the enterprise makes inroads in VDI, software to handle different components of VDI management is emerging. For example, Moogsoft Inc.'s AIOps offers an AI platform that can correlate events from inside and outside of the VDI deployment and gather data from applications, networks, system logs, orchestration tools and other sources. After collecting this mix of data, Moogsoft AIOps can correlate events, find problems and fix them wherever they reside across these systems.
Citrix also incorporated AI and machine learning technologies into Citrix Analytics, and VMware implemented these technologies into Workspace One.
Citrix Analytics monitors user, device and application behavior across Citrix products, including XenApp, XenDesktop, XenMobile and ShareFile. Using the data it collects from these systems, Citrix Analytics applies advanced machine learning algorithms to analyze behavior and identify potential threats coming from inside and outside of the organization.
VMware's Workspace One Intelligence aggregates, analyzes and correlates device and application data to provide deep insights and app analytics across the entire digital workspace. It uses AI and machine learning to make recommendations and predictions that enable data-driven decisions, while also automating workflows across the deployment.
Systems outside of the immediate VDI platform can also benefit from machine learning and AI in the enterprise. For example, CylanceProtect is integrated threat prevention software that uses AI to protect VMs and other endpoints from malware infections.
Hewlett Packard Enterprise (HPE) is adding an AI-based recommendation engine to its InfoSight platform so IT can better manage HPE's 3PAR flash storage, as well. The new engine improves IT's ability to resolve performance problems and identify the root causes of issues between storage and VMs.