AI Resource Detection for Idle Data Center Infrastructure
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing monitoring tools fail to identify underutilized resources such as comatose, ghost, and zombie resources in data centers, leading to wasted resources, increased energy consumption, security risks, and inefficiencies.
Innovation Solution
A tool using artificial intelligence/machine learning (AI/ML) models to predict underutilized resources by analyzing historical and recent telemetry and trace log data from servers, hypervisors, and virtual machines, without relying on command prompts, to identify candidates for decommissioning or redeployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring tools focus on resource availability and performance, then resource availability is improved, but underutilized resources are overlooked
Solution Approach 1:
The monitoring approach is segmented into multiple detection dimensions: availability monitoring, utilization pattern analysis, and underutilization identification. This allows the system to separately optimize for resource availability while simultaneously detecting underutilized resources through dedicated analysis of usage patterns and comparison against baseline performance.
Solution Approach 2:
The system introduces an intermediary analysis layer that processes telemetry data and trace log data to identify underutilized resources. This intermediary layer compares actual resource usage against expected performance baselines and flags resources that appear available but are operating far below optimal capacity, thus resolving the contradiction between availability monitoring and underutilization detection.
2Measurement precision
If monitoring tools focus on virtual machines, then VM detection is improved, but broader resource detection is limited
Solution Approach 1:
The monitoring system is designed with universal applicability across multiple resource types including virtual machines, physical servers, storage systems, and networking devices. The same telemetry collection framework and underutilization detection algorithms work across all resource types, enabling the system to maintain precise VM detection while extending coverage to broader infrastructure resources.
Solution Approach 2:
The system adapts its monitoring parameters and detection thresholds based on resource type. By dynamically adjusting measurement criteria and baseline performance expectations for different resource categories, the system maintains high detection accuracy for VMs while simultaneously achieving comprehensive coverage across diverse infrastructure resources without requiring separate specialized monitoring systems.
3Ease of operation
If command prompts are used to obtain data, then data acquisition is simplified, but automation and scalability are reduced
Solution Approach 1:
The system replaces manual command prompt-based data collection with automated telemetry agents and trace log collectors that continuously gather resource data in the background. These automated mechanisms eliminate the need for manual intervention while maintaining comprehensive data collection capabilities, enabling scalable and automated underutilization detection across the entire infrastructure.
Solution Approach 2:
The monitoring system implements self-service data collection through embedded telemetry agents that automatically gather resource metrics, health status, and performance data without requiring external manual queries. The system self-manages its own monitoring needs by continuously collecting and analyzing its own operational data, thereby achieving full automation while maintaining ease of operation through centralized monitoring interfaces.
4Duration of action of stationary object
If resources operate for years without notice, then operational continuity is maintained, but security risks increase
Solution Approach 1:
The system performs preliminary detection of underutilized resources and security vulnerabilities before they become critical issues. By continuously monitoring resource usage patterns and identifying resources operating below optimal capacity for extended periods, the system can flag potential security risks in advance, allowing administrators to take preventive actions such as applying security patches or decommissioning unused resources before they become vulnerable entry points.
Solution Approach 2:
The system establishes continuous feedback loops that monitor resource operational status, usage patterns, and security posture over time. This feedback mechanism provides ongoing visibility into long-term resource behavior, enabling the system to detect when resources have been operational for extended periods without proper maintenance or security updates, thereby identifying security risks that arise from prolonged operation without intervention.
Data Source
AI summary
An apparatus may include a computer processor operating in a data center and running an AI/ML model. The apparatus may include a trace log agent and a telemetry agent. The computer processor may be configured to train and run the AI/ML model to determine if a resource in the data center is being utilized or is idle by using data provided by the trace log agent and a telemetry agent. The apparatus may include a status check engine, a discovery engine, and an analytics engine. The computer processor may be configured to run each of these engines to confirm a prediction by the AI/ML model that the resource is idle. The computer processor may be configured to notify an administrator of the data center if the AI/ML model predicts the resource is idle and the engines provide increased confidence to the prediction.