Pod Resource Limit Adjustment Using ML Workload Prediction
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Solution Overview
Problem
Existing container orchestration platforms face challenges in dynamically adjusting pod resource limits, leading to inefficient resource allocation, increased costs, and environmental impact due to unpredictable resource demands.
Innovation Solution
A computer-implemented method that utilizes a trained machine learning model to monitor and predict resource usage, dynamically adjusting pod resource limits based on runtime and upcoming needs, enhancing resource utilization and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static resource limits are allocated to pods, then resource allocation is simple and predictable, but resource utilization efficiency deteriorates due to inability to adapt to changing workload demands
Solution Approach 1:
The patent implements dynamic resource limit adjustment by continuously monitoring pod resource usage and automatically modifying resource limits based on observed patterns. The system transitions from static pre-defined limits to dynamic adaptive limits that respond to actual workload demands, resolving the contradiction between adaptability and complexity through automated feedback mechanisms.
Solution Approach 2:
The system employs feedback loops where resource usage metrics are continuously collected, analyzed, and used to adjust resource limits. This closed-loop control enables the system to adapt to changing conditions while maintaining manageable complexity through automated decision-making based on predefined policies and machine learning models.
2Reliability
If resource limits are increased to ensure sufficient capacity, then service reliability is improved, but resource waste increases due to over-provisioning during low-demand periods
Solution Approach 1:
The system dynamically adjusts resource limits to match actual demand, preventing both over-provisioning and under-provisioning. By continuously adapting resource allocation to workload conditions, the system maintains service reliability while minimizing energy waste during low-demand periods.
Solution Approach 2:
The patent changes the resource limit parameters based on monitored usage patterns and predictions. Instead of fixed parameters, the system adjusts CPU, memory, and other resource limits dynamically, enabling reliable service delivery with optimized energy consumption by matching resource allocation to actual needs.
3Measurement precision
If machine learning models are used to predict resource usage, then resource allocation accuracy is improved, but system complexity and computational overhead increase
Solution Approach 1:
The system performs preliminary training of machine learning models using historical data before deployment. This pre-computation phase separates the complex model training from runtime operations, allowing accurate predictions during execution without adding significant operational complexity. The models are trained in advance to capture resource usage patterns.
Solution Approach 2:
The patent introduces machine learning models as intermediary components that translate historical resource usage data into future predictions. These models act as mediators between past observations and future resource allocation decisions, improving accuracy while encapsulating complexity within the model layer rather than the core resource management system.
4Productivity
If dynamic resource adjustment is implemented, then resource utilization efficiency is improved, but response time increases due to monitoring and adjustment overhead
Solution Approach 1:
The system implements periodic monitoring and adjustment cycles rather than continuous real-time changes. By sampling resource usage at regular intervals and adjusting limits periodically, the system maintains high utilization efficiency while minimizing the overhead impact on response time. The periodic approach balances responsiveness with computational efficiency.
Data Source
AI summary
Dynamic adjusting of pod resource limits is provided at runtime of a container orchestration platform pod. The process includes deploying a container orchestration platform pod with one or more pod resources in a computing environment, where the pod resources have one or more associated pod resource limits. Further, the process includes monitoring a runtime resource usage of the container orchestration platform pod, and predicting, by a trained machine learning model, upcoming resource usage of the container orchestration platform pod. The predicting uses, at least in part, the monitored runtime resource usage. Further, the process includes dynamically adjusting a pod resource limit of the one or more pod resource limits of the container orchestration platform pod in the computing environment. The dynamically adjusting is based on the monitored runtime resource usage, and the predicted upcoming resource usage.