Distributed Search Subsystem Filtering and Context Management
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Solution Overview
Problem
In large distributed computing systems, managing and identifying entities with specific attributes is computationally intensive, leading to significant overheads and complexity, as existing management subsystems struggle to efficiently collect and process information from numerous entities and systems.
Innovation Solution
A distributed-search subsystem is implemented, which filters participants based on centrally stored attribute values, maintains a search context for active searches, and returns the most favorably scored responses, optimizing the search process by distributing information requests and continuously updating the search context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If centralized management subsystems collect and process information from all entities in distributed systems, then complete information gathering is achieved, but computational overhead and processing time increase significantly
Solution Approach 1:
The patent segments the centralized management subsystem into multiple distributed search engines and search agents deployed across different nodes in the distributed system. Each search agent handles local information collection and filtering, while search engines coordinate searches across the distributed network. This segmentation distributes the computational burden, reducing the processing overhead on any single node while maintaining complete information gathering through coordinated collaboration among distributed components.
2Loss of information
If distributed search distributes information requests to all participants, then comprehensive search coverage is achieved, but network overhead and response time increase
Solution Approach 1:
The patent implements preliminary action through pre-computed inverted indexes and cached attribute data stored at each participant node. When a search request arrives, the distributed search engine first queries these local indexes and caches to identify potential matches before distributing detailed information requests. This preliminary filtering reduces the number of participants that need to be contacted, maintaining comprehensive search coverage while significantly reducing network overhead and response time.
3Reliability
If continuous information collection is performed from all entities, then up-to-date information is maintained, but system resource consumption increases
Solution Approach 1:
The patent implements periodic action through scheduled information collection cycles and event-driven updates. Instead of continuous monitoring, search agents periodically collect information from local entities and update the distributed index. Additionally, the system triggers information collection events only when specific changes occur in the system state. This periodic and event-driven approach maintains up-to-date information while dramatically reducing computational resource consumption compared to continuous collection.
4Measurement precision
If complex filtering and scoring operations are performed centrally, then accurate search results are produced, but processing overhead increases
Solution Approach 1:
The patent applies local quality by enabling each distributed search agent to perform local filtering and preliminary scoring operations on information collected from local entities. Each node applies its own filtering criteria and scoring functions locally, then submits only the most relevant results to the central search engine for final ranking. This distribution of filtering and scoring operations maintains search result accuracy while reducing the processing overhead and complexity at any single node.
Data Source
AI summary
The current document is directed to efficient, distributed-search methods and subsystems within distributed computer systems, including computer systems distributed over multiple sets of geographically distributed data centers, each comprising multiple discrete computer systems, such as multi-processor servers. In one implementation, the distributed-search methods and subsystems are implemented locally within participating entities as well as within one or more distributed-search engines. Each search is directed, by a participant, to identify attribute-associated entities within, provided by, connected to, or otherwise accessible to a distributed computing system having attribute values specified or indicated by the search query. Certain attribute values are continuously collected and centrally stored by the one or more centralized distributed-search engines while other attribute values are obtained through information requests distributed among participating entities.