Container Utility for Dynamic Resource Allocation in Pod Hosting
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Solution Overview
Problem
Legacy applications designed for traditional operating systems are not optimized for resource utilization in container-based hosting environments, leading to overprovisioning and underprovisioning of resources, which results in inefficient scalability and performance degradation.
Innovation Solution
A container utility is injected into the pod to collect operational statistics, which are processed by a rule execution engine to identify suboptimal resource usage, allowing for automatic or recommended adjustments to resource allocations and limits, enabling efficient resource management and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If applications are designed to execute within traditional operating systems with unrestricted resource access, then ease of operation is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent introduces a container execution environment as an intermediary layer between the application and the host operating system. This container acts as a mediator that provides restricted resource access to legacy applications, enabling them to run in containerized environments with controlled resource allocation while maintaining their original design assumptions of unrestricted access. The container utility injected into the pod serves as another intermediary that monitors and manages resource consumption.
2Reliability
If resource allocations are increased to ensure adequate performance, then reliability is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation where resource limits and requests are not fixed but can be adjusted based on actual application behavior. The system continuously monitors operational statistics and uses rule execution engines to dynamically modify resource allocations, scaling resources up or down according to actual demand rather than static overprovisioning, thereby maintaining performance while improving utilization efficiency.
Solution Approach 2:
The patent establishes a feedback loop where the container utility collects operational statistics about application resource consumption, feeds this information to rule execution engines, which then generate recommendations or automatically adjust resource allocations. This closed-loop feedback mechanism ensures resources are allocated based on actual usage patterns rather than estimates, optimizing the balance between performance and utilization efficiency.
3Loss of energy
If resource allocations are decreased to improve utilization efficiency, then resource utilization efficiency is improved, but reliability deteriorates
Solution Approach 1:
The patent implements preliminary actions by establishing resource requests and limits in advance, and by proactively monitoring operational statistics to detect trends before performance degradation occurs. The system prepares remedial actions in advance and can automatically adjust resources before reliability issues manifest, preventing performance problems rather than reacting to them after they occur.
Solution Approach 2:
The system dynamically adjusts resource allocations based on real-time monitoring of operational statistics, allowing resource limits to be flexibly modified according to actual application needs. This dynamic approach prevents both overprovisioning and underprovisioning by continuously adapting resource allocation to match actual usage patterns, thereby maintaining reliability while improving utilization efficiency.
4Productivity
If automated resource management is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service automation where the system automatically monitors its own resource consumption, detects suboptimal configurations, and applies remedial actions without requiring manual intervention. The container utility and rule execution engines work autonomously to optimize resource allocation, reducing the need for manual resource management while improving productivity. The system essentially manages itself through automated feedback loops and self-correcting mechanisms.
Data Source
AI summary
One or more computing devices, systems, and/or methods for application deployment, monitoring, and management within a container hosting environment are provided. A service collector acquires operational statistics of an application hosted within a container managed by a pod of the container hosting environment. A rule execution engine executes a set of rules to process the operational statistics. In response to the set of rules identifying suboptimal operation of the application, a remedial action is created to address the suboptimal operation of the application. The remedial action is either automatically executed to address the suboptimal operation or is used to generate a recommendation for how to address the suboptimal operation.