Virtual Container Scaling for Continuous Network Resource Allocation
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Solution Overview
Problem
Communication systems face disruptions when output demands increase, leading to power downs and resource allocation inefficiencies, resulting in incomplete data exchanges due to insufficient resource allocation.
Innovation Solution
A system and method for dynamically scaling and rescaling virtual containers in a containerized environment using machine learning algorithms to monitor and adjust resource allocation in real-time, allowing for vertical and horizontal scaling of processing and memory resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If communication systems power down to allocate additional resources, then resource allocation efficiency improves, but communication operations are disrupted and data exchanges are incomplete
Solution Approach 1:
The patent implements dynamic scaling of virtual containers based on real-time network demand. The system continuously monitors resource consumption and communication traffic patterns, then adjusts the number and size of virtual containers accordingly. This allows the system to transition from static resource allocation to dynamic adaptation, resolving the contradiction between resource efficiency and operational continuity by having resources scale up or down without disrupting active communications.
Solution Approach 2:
The system performs preliminary analysis of network traffic patterns and communication demands to predict future resource requirements. By analyzing historical data and traffic trends, the system can proactively scale resources before peak demand occurs, preventing disruptions before they happen rather than reacting after problems arise.
2Productivity
If static container configurations are used, then system simplicity is maintained, but processing speeds increase and resource efficiency are limited
Solution Approach 1:
The virtual container system performs self-adjustment based on monitored resource consumption and communication patterns. The system automatically determines when to scale containers up or down without requiring manual intervention or complex external control mechanisms. This self-service capability increases processing speed and resource efficiency while keeping the control logic contained within the system itself, avoiding the need for overly complex external management infrastructure.
3Reliability
If resources are allocated to meet peak demand, then communication operations complete successfully, but resource waste occurs during low-demand periods
Solution Approach 1:
The system dynamically adjusts resource allocation based on real-time demand patterns rather than maintaining fixed high-capacity resources. By continuously monitoring communication traffic and virtual container performance, the system scales resources up during peak demand periods to ensure data exchange completion and scales down during low-demand periods to minimize resource waste, achieving both reliability and energy efficiency.
Solution Approach 2:
The system implements continuous feedback loops that monitor resource consumption, communication success rates, and traffic patterns. This feedback information is used to automatically adjust resource allocation decisions, ensuring resources are allocated sufficient to meet actual demand while avoiding over-provisioning during low-demand periods, thus preventing resource waste while maintaining operational reliability.
Data Source
AI summary
An apparatus comprises a memory and a processor communicatively coupled to one another. The processor is configured to determine network resource availability information in a communication network and execute a machine learning algorithm to analyze the network resource availability information based at least in part upon one or more communication conditions, generate one or more analysis results in response to analyzing the network resource availability information; and generate one or more network assignment recommendations based at least in part upon the plurality of analysis results and historical data. Further, the processor is configured to assign a second plurality of resources in the containerized environment over the second time period and rescale one or more virtual containers in the containerized environment to use the second plurality of resources.