Container Pod Thresholding for Stable VM Resource Scaling
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Solution Overview
Problem
Existing information handling systems face inefficiencies in processor and memory utilization due to uncontrolled pod scaling, leading to bottlenecks and potential system crashes when handling increased loads, particularly in resource-heavy applications.
Innovation Solution
A method and system for determining an optimal number of pods in a container-based deployment using a calibration mode to set a threshold for pod creation and queuing additional requests, preventing over-allocation of resources and ensuring efficient resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If uncontrolled pod scaling is implemented to handle increased loads, then system capacity and adaptability are improved, but processor and memory utilization become inefficient leading to bottlenecks and system crashes
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors resource utilization metrics (CPU usage, memory consumption, pod count) and automatically adjusts pod scaling decisions based on this feedback. The threshold-based control system receives information about current system state and modifies scaling behavior accordingly, preventing both over-provisioning and under-provisioning of resources.
Solution Approach 2:
The system transitions from static pod scaling configurations to dynamic scaling that adapts in real-time based on workload conditions. The threshold values for pod creation and removal are dynamically adjusted based on observed resource utilization patterns, allowing the system to optimize its capacity dynamically rather than relying on fixed scaling parameters.
2Productivity
If more pods are deployed to handle increased loads, then processing capacity is improved, but resource allocation becomes inefficient and system crashes occur
Solution Approach 1:
The system performs preliminary actions by establishing threshold values for resource utilization before actual scaling decisions are made. These pre-defined thresholds act as control parameters that guide pod scaling operations, ensuring that pods are created or removed at appropriate moments based on predicted resource needs rather than reactive responses to resource exhaustion.
Solution Approach 2:
The patent utilizes parameter changes by modifying the number of pods based on changing resource utilization parameters. When CPU usage, memory consumption, or other performance metrics cross defined thresholds, the system adjusts the pod count parameter accordingly, optimizing the balance between processing capacity and resource efficiency.
3Adaptability or versatility
If pod scaling is allowed without thresholds, then system flexibility is improved, but bottlenecks and system crashes occur due to over-allocation
Solution Approach 1:
The patent introduces an intermediary control mechanism that mediates between the desire for flexible pod scaling and the need for resource management control. This intermediary layer consists of threshold-based decision logic that automatically determines when scaling operations should occur, eliminating the need for complex manual resource management while maintaining scaling flexibility.
Data Source
AI summary
In one or more embodiments, one or more systems, one or more methods, and/or one or more processes may: execute a virtual machine; deploy a first calibration multiple pods associated with an application on the virtual machine; for each set of inputs of multiple sets of inputs: determine a processor utilization of the first calibration multiple pods and all additional calibration multiples of pods; and determine a memory utilization of the first calibration multiple pods and all additional calibration multiples pods; and determine a threshold number of pods associated with the application that can be utilized without exceeding a threshold amount of memory based at least on the processor utilization of the first calibration multiple pods and all additional calibration multiples of pods and based at least on the amount of memory allocated to the first calibration multiple pods and all additional calibration multiples of pods.