Conditional Container Deployment for Resource Optimization
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Solution Overview
Problem
In container computing environments, the rapid creation of many jobs can lead to a significant consumption of system resources, limiting available computing and storage resources for other users and potentially causing overload, as a large number of containers are created simultaneously, thereby impacting system performance.
Innovation Solution
A method is implemented to conditionally deploy reusable groups of containers based on the current availability of system resources, deploying non-reusable containers when resources are sufficient and reusable containers when resources are scarce, thereby optimizing resource utilization and preventing overload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a large number of jobs are created within a short time period, then the system can handle more user requests and improve productivity, but the amount of system resources consumed increases significantly and available resources for other users are limited
Solution Approach 1:
The system dynamically changes the container deployment strategy parameter based on current resource availability. When resource usage exceeds the threshold, the system switches from deploying non-reusable containers to deploying reusable containers, effectively changing the resource consumption parameter to maintain productivity while conserving resources.
Solution Approach 2:
The patent implements a dynamic resource management system that continuously monitors system resource usage and adjusts container deployment strategies in real-time. The system transitions between different deployment modes (reusable vs. non-reusable containers) based on current load conditions, making the resource allocation flexible and adaptive rather than static.
2Reliability
If non-reusable containers are deployed for each job, then resource isolation and job security are improved, but the amount of processing and storage resources required increases when the system is under load
Solution Approach 1:
The system changes the container reusability parameter dynamically based on resource conditions. When resources are abundant, non-reusable containers provide isolation; when resources are scarce, reusable containers reduce consumption. This parameter change allows the system to balance reliability and resource usage based on current state.
Solution Approach 2:
The patent makes the container deployment strategy dynamic by switching between reusable and non-reusable modes based on real-time resource monitoring. This dynamic approach allows the system to maintain job isolation when possible while reducing resource consumption under load by reusing containers across multiple jobs.
3Quantity of substance
If reusable containers are deployed to conserve resources, then resource efficiency is improved, but the complexity of container management and resource tracking increases
Solution Approach 1:
The system implements feedback mechanisms to monitor resource usage and automatically adjust container deployment strategies. The resource monitoring component provides continuous feedback on system state, enabling the management system to make informed decisions about when to reuse containers versus when to deploy new ones, managing complexity through automated feedback loops.
Solution Approach 2:
The container management system uses automated resource monitoring and threshold-based decision making to self-regulate container deployment. The system serves itself by automatically switching between deployment modes based on predefined resource thresholds, reducing the need for complex manual management while maintaining resource efficiency.
4Productivity
If the system monitors resource usage dynamically to make deployment decisions, then resource utilization is optimized, but the processing overhead for monitoring and decision-making increases
Solution Approach 1:
The system performs partial monitoring by focusing only on key resource metrics and using simple threshold comparisons rather than comprehensive analysis. This partial action approach provides sufficient information for deployment decisions without the full processing overhead of complete resource state analysis, optimizing the balance between utilization efficiency and processing cost.
Data Source
AI summary
A computer-implemented method according to one embodiment includes identifying a job creation request within a system; determining a current amount of available resources within the system; and conditionally deploying a reusable group of containers for the job, based on the current amount of available resources for the system.