Enterprise Cloud Garbage Collector for Unproductive Resource Reclamation
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Solution Overview
Problem
Current cloud technologies focus on server and resource management during provisioning and steady-state operations, neglecting the recycling and reclamation of unused resources, leading to significant inefficiencies and costs due to unproductive servers and resources that continue to incur charges despite not being in use.
Innovation Solution
A system and method for identifying unproductive cloud resources by collecting data, selecting analytics models to classify resources as productive or unproductive, and determining action plans based on confidence levels, which includes notifying resource owners, hibernation, or reclamation of resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If cloud resources are easily provisioned and created, then resource availability and provisioning speed are improved, but resource tracking and management become difficult leading to forgotten unused resources
Solution Approach 1:
The system implements continuous feedback mechanisms by monitoring resource utilization metrics, performance data, and access patterns. The garbage collector periodically collects data from cloud resources and analyzes it to identify unproductive resources, creating a closed-loop feedback system that automatically detects and reports forgotten resources without requiring manual tracking.
Solution Approach 2:
The cloud garbage collector enables resources to self-identify their productivity status by automatically collecting and analyzing their own operational data. Resources that are unproductive automatically flag themselves through the analysis process, eliminating the need for external manual auditing and enabling self-managed resource lifecycle management.
2Measurement precision
If analytics models use multiple data sources and complex analysis, then classification accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The system segments the analytics process into distinct modular components: data collection module, data processing module, classification module, and action planning module. Each module handles a specific aspect of the analysis, processing different types of data (utilization metrics, performance data, access patterns) independently and passing results through defined interfaces, which reduces overall system complexity while maintaining comprehensive analysis capabilities.
3Ease of operation
If cloud resources continue to operate without utilization monitoring, then operational simplicity is maintained, but energy waste and unnecessary costs increase
Solution Approach 1:
The monitoring system operates autonomously by automatically collecting utilization data from cloud resources, analyzing the data through analytics models, and executing actions to reclaim unproductive resources. The system self-manages the entire process from data collection to resource reclamation without requiring manual intervention, maintaining operational simplicity while eliminating energy waste through automated detection and remediation of unproductive resources.
Data Source
AI summary
Various embodiments collect unproductive resources in a network infrastructure. In one embodiment, data relating to resources of a network infrastructure is collected. An analytics model is selected based on a type of the collected data. The selected analytics model is executed to classify a resource unproductive or productive, and to assign a corresponding confidence level. An action plan for each confidence level is determined and the action plan is executed for the resource. The collected data may include resource utilization information, hypervisor information, cloud related meta-data, user knowledge and system knowledge. When data is only resource data, a resource mining model is selected. When the data includes reference data, a reference mining model is selected. When the data comprises reference data and resource data, a combined mining model is selected.