Intelligent Container Garbage Collector for Storage Optimization
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Solution Overview
Problem
The surge in demand for container storage has put a strain on existing infrastructure, as replicating container services across nodes consumes large amounts of storage space, leading to physical constraints such as limited expansion due to land, energy, and network topology limitations, necessitating a more efficient approach to manage storage.
Innovation Solution
Implementing an intelligent garbage collector system that uses a machine-learning function to analyze resource consumption parameters of containers, classify them into suspicious, malicious, or normal classes, and perform garbage collection actions such as generating alerts or reducing resource consumption, thereby optimizing storage efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If container services are replicated across nodes, then service availability and scalability are improved, but storage space consumption increases significantly
Solution Approach 1:
The patent implements a garbage collection mechanism that identifies and removes unused container images from the storage system. The system tracks which container images are currently in use and safely deletes those that are no longer needed, thereby recovering storage space while maintaining service scalability.
Solution Approach 2:
The garbage collection system operates autonomously to manage container image storage. It automatically monitors resource consumption parameters, identifies suspicious or malicious containers, and performs cleanup actions without requiring manual intervention, thus self-managing the storage optimization process.
2Reliability
If all container images are retained for safety, then system reliability is improved, but storage efficiency deteriorates
Solution Approach 1:
The system employs a feedback mechanism where the garbage collection function continuously monitors resource consumption parameters of containers and adjusts its cleanup actions accordingly. This feedback loop ensures that containers essential for system reliability are preserved while unnecessary containers are removed, optimizing storage efficiency without compromising reliability.
Solution Approach 2:
The patent changes the state of container management by introducing classification parameters (suspicious, malicious, normal) and using machine learning to dynamically determine which containers to retain or remove. This parameter-based approach enables intelligent differentiation between containers that must be kept for reliability and those that can be safely discarded to improve storage efficiency.
3Measurement precision
If manual container management is performed, then control precision is improved, but operational complexity increases
Solution Approach 1:
The patent replaces manual container management operations with an automated machine-learning-based garbage collection system. The machine learning function analyzes resource consumption parameters and automatically classifies containers, substituting human manual operations with an automated intelligent system that achieves high classification accuracy while reducing operational complexity.
Solution Approach 2:
The garbage collection function serves as an intermediary between container operations and storage management. It introduces a machine learning-based classification layer that automatically processes container data, determines appropriate actions (delete, retain, alert), and executes storage management tasks, thereby mediating between operational requirements and storage optimization without requiring direct manual intervention.
Data Source
AI summary
Methods, systems, and computer program products are included for the intelligent garbage collection of containers. An example method includes providing a garbage collection data structure, the garbage collection data structure including metadata and one or more resource consumption parameters corresponding to the container. The one or more resource consumption parameters are analyzed by a machine-learning function. Based on the analyzing, the container is classified into one or more classes, the one or more classes including at least one of a suspicious container class, a malicious container class, or a normal container class. Based on the classifying, one or more garbage collection actions are performed on the container, including at least one of generating an alert corresponding to the container or reducing the resource consumption of the container.