Runtime Agent Request Processing with Dynamic Data Merging
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Solution Overview
Problem
Cloud computing systems face delays in processing requests due to data updates and require storage of multiple data versions for personalization, leading to inefficiencies in resource management and access control.
Innovation Solution
A runtime agent is introduced that processes requests independently of the cloud platform, generating an application package with hierarchical folders and access paths, allowing for dynamic data merging and caching to enhance performance and availability, while managing user access and personalization without code enhancements or software updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is hosted in the cloud for centralized distribution, then resource sharing and accessibility are improved, but request processing delays occur during data updates
Solution Approach 1:
The patent segments the centralized cloud data into distributed edge caches across multiple runtime agents. Each runtime agent maintains a local cache of data packages, allowing systems to access data from nearby edge nodes rather than always querying the central cloud. This segmentation enables parallel data retrieval from multiple distributed sources, reducing request processing delays while maintaining data accessibility.
Solution Approach 2:
The patent implements preliminary action by pre-fetching and caching data packages at edge runtime agents before they are actually needed. The system proactively retrieves data from the cloud and stores it in local caches, so when requests arrive, the data is already available locally. This anticipatory caching eliminates wait times during data retrieval while maintaining up-to-date data availability.
2Adaptability or versatility
If multiple versions of data are stored in the cloud for personalization, then system personalization capability is improved, but cloud storage complexity and resource requirements increase
Solution Approach 1:
The patent applies local quality by allowing each runtime agent to maintain customized local caches tailored to specific system requirements and user preferences. Different runtime agents can cache different data versions and personalization parameters locally, rather than the cloud storing all possible versions for all systems. This enables personalized data delivery where each edge node optimizes its cache for local consumption patterns, reducing overall cloud storage complexity.
Solution Approach 2:
The patent uses copying by creating local replicas of data packages at edge runtime agents instead of storing all master copies in the cloud. Each runtime agent maintains copies of frequently accessed data and personalization parameters locally. This copying approach allows multiple systems to access personalized data from their local caches without requiring the cloud to manage complex version control for every possible personalization scenario.
3Speed
If data is cached at runtime agents for faster access, then request processing speed is improved, but data consistency and update propagation become more complex
Solution Approach 1:
The patent implements periodic action by establishing scheduled data synchronization cycles between the cloud and edge runtime agents. Runtime agents periodically check for and retrieve updated data packages from the cloud, ensuring cached data remains current without requiring continuous real-time synchronization. This periodic refresh approach maintains data consistency while avoiding the complexity of continuous update propagation mechanisms.
Solution Approach 2:
The patent uses feedback mechanisms where runtime agents report cache status and data freshness metrics back to the cloud platform. The cloud receives feedback about which data packages are cached where and when they were last updated. This feedback loop enables intelligent update propagation, where the cloud can selectively push updates only to runtime agents that need them, maintaining data consistency across the distributed cache network without complex manual coordination.
Data Source
AI summary
Various embodiment of systems and methods to send a response for a request received at a runtime agent has been described. Initially a request including an access path of plurality of files is received at the runtime agent. Based on the received request, one or more files from the plurality of files that have a dynamic property are determined. Next the one or more files that have the dynamic property are parsed to identify an include statement in the one or more files, wherein the include statement has an access path for a to-be-included file. Next the data from the one or more files and the to-be-included file is retrieved by the runtime agent. The data retrieved from the one or more files and the to-be-included files to obtain a merged data are merged. Finally the response including the merged data is sent to the client by the runtime agent.