Selective Resource Caching for Network-Resilient Offline Workflows
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Solution Overview
Problem
Computing devices face challenges in maintaining functionality during network instability due to the infeasibility of downloading or transferring entire platforms, which would overwhelm storage and processing resources, leading to compromised access to resources and tools.
Innovation Solution
A computing system that selectively caches relevant resources and functionalities from a remote server based on historical utilization, environmental conditions, and predicted tasks, ensuring access control levels are maintained, and queues modifications for upload upon reconnection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire platform is downloaded or transferred to computing devices, then access to resources and tools is improved, but storage and processing footprints become infeasible for typical computing devices
Solution Approach 1:
The platform is divided into discrete entities (data, logic, dependencies, libraries) that can be selectively cached on computing devices. This segmentation allows only necessary components to be stored locally rather than the entire platform, reducing storage footprint while maintaining access to required resources.
Solution Approach 2:
Specific entities are extracted from the remote server and cached locally on computing devices based on predicted tasks and historical utilization. This extraction enables access to frequently needed resources without storing the complete platform, balancing reliability with storage constraints.
2Productivity
If more resources are cached on computing devices, then functionality during network downtime is improved, but storage and processing limitations are exceeded
Solution Approach 1:
Entities are cached on computing devices in advance based on predicted tasks and historical utilization patterns before network disconnection occurs. This preliminary action ensures that necessary resources are already available locally, enabling continued functionality during network downtime without exceeding storage capacity by caching everything.
Solution Approach 2:
The system dynamically adjusts which entities to cache based on predicted task parameters, historical utilization metrics, and available storage capacity. This parameter-driven approach optimizes the balance between maintaining productivity during offline periods and respecting storage limitations.
3Quantity of substance
If selective caching is implemented based on historical utilization, then storage efficiency is improved, but system complexity increases due to tracking and determining entities
Solution Approach 1:
The system automatically tracks historical utilization of entities and determines which ones to cache based on this data, eliminating the need for manual configuration. This self-service approach manages the complexity of tracking and selection internally while presenting a simple interface to users.
Solution Approach 2:
The system uses historical utilization data as feedback to continuously optimize which entities are cached. By monitoring how entities are actually used and adjusting caching decisions based on this feedback, the system achieves high storage efficiency without requiring complex manual intervention.
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.