Operating System Container Grouping for Application Switching
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Solution Overview
Problem
Current computer systems face performance optimization challenges in processing large volumes of data for medical imaging applications, where complex tasks require efficient loading and processing of applications without perceptible delays, and existing methods like server farming do not comprehensively address resource optimization and stability within the computer.
Innovation Solution
An operating method that pre-specifies a pre-start level for the computer to iteratively pre-start units, creating containers and loading applications based on their maturity levels, allowing for suspension and resumption, and determining the degree of grouping to minimize container groups while ensuring applications can be suspended without conflicts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If applications are loaded into separate containers individually, then each application can be managed independently and stability is improved, but the number of containers increases and resource optimization deteriorates
Solution Approach 1:
The patent combines multiple applications that can be suspended into a single container, reducing the total number of containers while maintaining system stability. This is achieved by grouping applications based on their suspension capability, allowing them to share container resources without conflicting with each other.
Solution Approach 2:
The patent segments applications into different groups based on their suspension capability. Applications that can be suspended are grouped together in shared containers, while applications that cannot be suspended are placed in dedicated containers. This segmentation strategy optimizes resource utilization while maintaining system reliability.
2Productivity
If more containers are created to process complex tasks in parallel, then processing speed and productivity are improved, but resource allocation increases and resource optimization deteriorates
Solution Approach 1:
The patent merges multiple applications into shared containers, allowing parallel processing of complex tasks while reducing the total number of containers. This enables efficient resource allocation by having multiple applications share the same container resources, thereby improving productivity without proportionally increasing resource consumption.
Solution Approach 2:
The patent creates universal containers that can host multiple applications simultaneously. These shared containers serve multiple functions by accommodating different applications that can be suspended and resumed, thereby reducing the overall number of containers needed and optimizing resource utilization while maintaining high processing speed.
3Loss of time
If applications are pre-started before user request, then response time is reduced and productivity is improved, but resource allocation increases and resource optimization deteriorates
Solution Approach 1:
The patent implements preliminary action by pre-starting applications that can be suspended before user requests arrive. These pre-started applications are kept in a suspended state within shared containers, ready to be resumed quickly when needed. This approach reduces response time while optimizing resource allocation by not fully executing pre-started applications until actually needed.
Solution Approach 2:
The patent applies the discarding and recovering principle by suspending pre-started applications instead of keeping them fully active. When resources are needed, the system recovers these suspended applications by resuming them from their suspended state, rather than starting them from scratch or maintaining them in a fully active resource-consuming state.
4Quantity of substance
If the number of containers is reduced to optimize resources, then resource allocation is improved, but system complexity increases and managing application interactions becomes more difficult
Solution Approach 1:
The patent applies local quality by differentiating container management based on application characteristics. Shared containers are used for applications that can be suspended, while dedicated containers are used for applications that cannot be suspended. This localized approach simplifies management by creating clear rules for when to use shared versus dedicated containers, reducing overall system complexity despite having fewer containers total.
Solution Approach 2:
The patent changes the parameter of container sharing based on application suspension capability. By using suspension capability as a key parameter, the system automatically determines whether applications should share containers or have dedicated containers. This parameter-based approach simplifies container management complexity by providing a clear, automated decision criterion.
Data Source
AI summary
In at least one embodiment, if the pre-start level has the value empty container, the computer creates a container within the framework of the pre-start but does not load any application into the container. If the pre-start level has the value application, the computer creates a respective container within the framework of the pre-start for each application. If the pre-start level has a higher value, the computer determines within the framework of the pre-start a degree of grouping for the applications assigned to the respective pre-started unit, and groups the applications in accordance with the degree of grouping determined into at least one container group. Within the framework of the processing of the complex tasks, the computer terminates on switching from one application to another application, the application still being executed only if the application involves an application not able to be suspended.