Application Cache Allocation Using Dynamic Storage Limits
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for managing local storage allocation for applications on computing devices are inefficient, often requiring significant user intervention and leading to wasted network and computational resources, and do not account for user behavior or device conditions.
Innovation Solution
A storage management system that calculates a dynamic cache limit for applications based on usage frequency, minimum data retention requirements, available device storage, and user behavior, automatically adjusting the limit to optimize storage allocation without user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static storage thresholds are used for all applications, then device storage management is simple, but storage allocation is inefficient and does not account for application usage frequency or device conditions
Solution Approach 1:
The patent implements dynamic storage thresholds that automatically adjust based on application usage frequency, recency of use, and device storage conditions. The system calculates a dynamic cache limit for each application that changes over time, replacing static thresholds with adaptive values that optimize storage allocation efficiency while maintaining manageable system complexity through automated calculations.
Solution Approach 2:
The system changes the storage threshold parameter from a fixed value to a dynamically calculated value based on multiple factors including usage frequency, recency, and available storage. This parameter transformation enables efficient storage allocation by adjusting thresholds according to actual application needs and device conditions, resolving the contradiction between simplicity and efficiency.
2Productivity
If dynamic cache limits are calculated based on multiple parameters, then storage allocation is optimized, but calculation complexity and processing time increase
Solution Approach 1:
The system performs preliminary calculations of dynamic cache limits during application installation and periodically updates them based on usage patterns. By pre-calculating and periodically refreshing storage allocations rather than continuously recalculating, the system achieves storage optimization while minimizing processing time and computational overhead during normal operation.
Solution Approach 2:
The system uses feedback from actual application usage data to refine storage allocation decisions. By monitoring usage patterns and adjusting cache limits based on observed behavior, the system achieves optimization over time without requiring complex real-time calculations, as the feedback mechanism guides incremental adjustments rather than demanding intensive processing.
3Ease of operation
If user intervention is required for storage management, then allocation decisions can be customized, but user burden and system latency increase
Solution Approach 1:
The system implements self-service storage management by automatically calculating and enforcing dynamic cache limits without requiring user intervention. The system autonomously monitors application usage, calculates appropriate storage allocations, and enforces limits by evicting data when necessary. This eliminates user burden and associated time delays while maintaining optimized storage allocation through automated decision-making.
Solution Approach 2:
The system uses feedback loops to automatically adjust storage allocations based on monitored usage patterns and device conditions. This closed-loop control enables the system to make customized allocation decisions autonomously, replacing manual user control with automated feedback-driven adjustments that reduce latency and user burden while preserving allocation customization.
4Speed
If more storage is allocated to frequently used applications, then application performance is improved, but other applications may suffer from storage shortage
Solution Approach 1:
The system applies local quality by allocating storage resources differently to different applications based on their individual usage characteristics. Each application receives a customized cache limit tailored to its specific usage frequency and recency, rather than applying a uniform allocation rule. This enables high-performance allocation for frequently used applications while ensuring that less frequently used applications retain sufficient storage, achieving both performance optimization and fairness through differentiated local adjustments.
Solution Approach 2:
The system dynamically changes storage allocation parameters for each application based on usage patterns. By transforming static, equal allocation into dynamic, usage-based allocation, the system improves performance for active applications while maintaining fairness across the application ecosystem through adaptive parameter adjustment that responds to actual usage conditions.
Data Source
AI summary
A storage management system is described that calculates a dynamic cache limit for a computing device application on an application-specific basis. The storage management system calculates the dynamic cache limit automatically and independent of user input, based on usage of the application at a computing device, conditions of the computing device, or a combination thereof. Example parameters considered in computing the dynamic cache limit include usage frequency of the application, minimum data retention requirements for the application, available computing device storage, a storage consumption rate of the application, storage consumption by the application relative to at least one other application, size of digital content created or consumed by the application, data download frequency, frequently used digital content, and so forth. The dynamic cache limit is periodically modified and enforced to prevent an application from consuming more computing device storage than permitted by the dynamic cache limit.