Selective Caching Framework for Network-Resilient Computing
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Solution Overview
Problem
Computing devices face challenges in maintaining functionality during network instability due to storage and processing limitations, making it infeasible to download entire platforms, and caching entire remote servers can overwhelm the device.
Innovation Solution
A computing system selectively caches resources and functionalities based on historical utilization, current job, and environmental conditions, setting access control levels and queuing modifications for upload upon reconnection, ensuring situational awareness during network downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire remote server is cached locally, then availability during network disruption is improved, but storage requirements and device overload increase
Solution Approach 1:
The patent segments the remote server into multiple individual entities (data, logic, dependencies, libraries) and selectively caches only those relevant to current workflows. This allows the system to maintain availability during network disruptions while avoiding the storage burden of caching the entire server.
Solution Approach 2:
Instead of caching the complete remote server (excessive action), the system caches only the necessary subset of entities based on current workflows and historical usage patterns (partial action). This partial caching approach provides sufficient availability during disruptions without overwhelming storage capacity.
2Reliability
If more entities are cached to maintain functionality during network instability, then reliability is improved, but processing overhead and device resource consumption increase
Solution Approach 1:
The system performs preliminary determination of which entities to cache based on current workflows and historical utilization patterns before network disruptions occur. This proactive selection ensures that only necessary entities are cached, reducing processing overhead while maintaining reliability during instability.
Solution Approach 2:
The system automatically determines and manages entity caching based on historical usage data and current workflows without requiring manual intervention. This self-service approach optimizes the balance between reliability and processing overhead by leveraging past patterns to predict future needs.
3Volume of stationary object
If selective caching is implemented, then storage requirements are reduced, but the complexity of determining which entities to cache increases
Solution Approach 1:
The system uses historical utilization patterns as feedback to automatically determine which entities should be cached. By analyzing past usage data, the system learns which entities are most valuable and pre-selects them for caching, reducing the complexity of manual determination while optimizing storage usage.
Solution Approach 2:
The system changes the parameter of caching selection from manual/complete to automatic/selective based on historical utilization patterns. This parameter change simplifies the determination logic by using automated patterns recognition rather than complex manual decision-making, while still achieving optimal storage efficiency.
Data Source
AI summary
Computing systems methods, and non-transitory storage media are provided for obtaining a request or query indicative of a resource, tool, task, or workflow, determining any entities including data, logic, dependencies and libraries, within a remote server, corresponding to the resource, tool, task, or workflow, and selectively provisioning or caching, from the remote server, the entities at the computing system.