Application Component Memory Placement for Faster Startup
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Solution Overview
Problem
Existing computing systems face inefficiencies in managing initial data distribution for application processes, particularly in mobile devices, leading to suboptimal performance and resource utilization.
Innovation Solution
Implementing a system where an operating system or hypervisor scores and strategically places application components in different types of memory based on criticality, using machine learning and expectation-maximization algorithms to optimize memory usage and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If all application components are placed in high-performance memory, then application performance is improved, but memory cost and resource consumption increase
Solution Approach 1:
The patent applies local quality by differentiating memory allocation based on component criticality. Critical components (those essential for application startup and core functionality) are placed in high-performance memory, while non-critical components are placed in lower-performance memory. This selective approach ensures that performance is optimized only where necessary, rather than uniformly across all components, thus resolving the contradiction between performance and resource consumption.
Solution Approach 2:
The patent segments application components into different categories based on their criticality levels. By dividing components into critical and non-critical groups, the system can apply different memory allocation strategies to each segment. This segmentation allows the system to prioritize memory resources for components that matter most while reducing overall memory consumption, directly addressing the technical contradiction.
2Loss of time
If memory allocation is optimized for speed, then application startup time is reduced, but memory management complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-analyzing and determining the criticality of each application component before the application actually runs. During the installation or initialization phase, the system performs static analysis to identify which components are critical for startup and core functionality. This advance preparation allows the system to automatically make optimal memory placement decisions without requiring complex real-time analysis, thus reducing startup time while keeping memory management complexity manageable.
Solution Approach 2:
The system applies self-service by enabling applications to effectively manage their own memory allocation based on pre-determined criticality information. Each application's components are automatically placed in appropriate memory regions based on their criticality scores, without requiring manual intervention or complex external management. This automated self-organization reduces the complexity of memory management while achieving optimal performance.
3Productivity
If critical components are identified and prioritized, then memory efficiency is improved, but analysis and scoring overhead increases
Solution Approach 1:
The patent performs the energy-intensive analysis and scoring of component criticality in advance, during installation or system initialization, rather than continuously during runtime. By completing the static analysis and criticality determination beforehand, the system creates a reusable criticality profile for each application. This preliminary action eliminates the need for ongoing analysis overhead, achieving both memory efficiency and low energy consumption during actual application execution.
Data Source
AI summary
In a mobile device, processes of an application can be monitored and scored for initial data distribution. Specifically, a method can include monitoring processes of an application, and scoring objects or components used by the processes to determine placement of the objects or components in memory during initiation of the application. The method can also include, during initiation of the application, loading, into a first portion of the memory, at least partially, the objects or components scored at a first level. The method can also include, during initiation of the application, loading, into a second portion of the memory, at least partially, the objects or components scored at a second level. The objects or components scored at the second level can be less critical to the application than the objects or components scored at the first level.