Virtual Machine Resource Optimization in the Retail Sector
How Xautomata’s Digital Twin of an Organization detected “Zombie” and “Top Talker” VMs and optimized datacenter resources for a national retailer.
At a glance
350
Servers evaluated
50
Sites in scope
The client
A national Italian retailer
Xautomata worked with a well-known Italian retail company, applying its DTO (Digital Twin of an Organization) platform to a specific datacenter challenge: optimizing virtual-machine (VM) resources by identifying and managing underperforming or overutilized VMs – referred to respectively as “Zombie” and “Top Talker” VMs.
Objective
Detect and manage Zombie and Top Talker VMs
The primary objective was to analyze the resources required by VMs in the datacenter in order to efficiently detect and manage Zombie and Top Talker VMs – evaluating resources across 350 servers and 50 sites.
Approach
A multi-step strategy
First capability
Short description of this part of the approach.
Second capability
Short description of this part of the approach.
Third capability
Short description of this part of the approach.
First step
VM Assessment and Classification
Metrics Collection
Use of an open-source monitoring system to gather data on CPU, RAM, and network resource consumption.
Machine Learning Algorithm
A dedicated algorithm categorized VMs based on their performance and resource usage.
Assessment Reports
Detailed reports identified Zombie and Top Talker VMs.
Second step
Application Map Construction for Top Talker VMs
Correlation Identification
Cisco AppDynamics APM identified correlations between different applications, building an application map focused on Top Talker VMs.
Impact
What the engagement delivered
Optimized resources
Identifying and categorizing VMs let the company optimize resource allocation and usage, ensuring efficient operations.
Targeted analysis
Detailed, focused analysis concentrated only on the areas that required attention.
Application mapping
Application maps, built where needed, provided insight into application interactions and dependencies.
First step
VM Assessment and Classification
Metrics Collection
Use of an open-source monitoring system to gather data on CPU, RAM, and network resource consumption.
Machine Learning Algorithm
A dedicated algorithm categorized VMs based on their performance and resource usage.
Assessment Reports
Detailed reports identified Zombie and Top Talker VMs.
Second step
Application Map Construction for Top Talker VMs
Correlation Identification
Cisco AppDynamics APM identified correlations between different applications, building an application map focused on Top Talker VMs.
Conclusion
Xautomata’s Digital Twin of an Organization platform successfully addressed the critical needs of the retail sector – optimizing VM resources and delivering valuable insights into application performance, while improving operational efficiency and ensuring a robust, scalable IT infrastructure.