Distributed Processing Candidate Selection by Trust and Capacity
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Solution Overview
Problem
Users face challenges in accessing additional computing capacity on demand without acquiring permanent hardware, managing device discovery and linking processes, ensuring capability and trustworthiness of remote devices, and securing remote processing.
Innovation Solution
A method for autonomous, dynamic, peer-to-peer discovery and selection of computing entities based on their capabilities and trustworthiness, using feature vectors and Hall of Fame ratings to efficiently distribute processing tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If users access additional computing capacity from remote devices, then processing capability is improved, but security and trustworthiness assurance becomes more difficult
Solution Approach 1:
The patent introduces an intermediary trust assessment mechanism that mediates between the initiator device and recipient device. This intermediary system evaluates the trustworthiness of recipient devices through feature vector verification and Hall of Fame rating checks, allowing secure distributed processing without direct trust establishment between participating devices.
Solution Approach 2:
The system implements feedback through the Hall of Fame rating mechanism, where recipient devices are evaluated and rated based on their performance and trustworthiness. This feedback loop allows the network to learn from past interactions and make informed decisions about task distribution, improving security while maintaining processing capability.
2Reliability
If manual device discovery and linking processes are used to connect computing entities, then connection reliability is improved, but operational complexity and time consumption increase
Solution Approach 1:
The patent enables computing entities to autonomously discover and evaluate each other through automated feature vector exchange and trust assessment mechanisms. Devices self-register their capabilities and self-evaluate trustworthiness without manual intervention, eliminating the need for tedious pairing processes while maintaining connection reliability through systematic verification.
Solution Approach 2:
The system performs preliminary trust assessment and capability verification through feature vectors before actual task distribution. By pre-evaluating and pre-registering computing entities in the network, the system establishes reliable connections in advance, eliminating the need for manual pairing at the time of task execution.
3Reliability
If computing entities operate independently with isolated capabilities, then device security is improved, but distributed processing efficiency decreases
Solution Approach 1:
The patent segments computing capabilities into modular feature vectors that can be independently evaluated and exchanged. Each computing entity maintains its isolated security boundaries while exposing standardized capability descriptors, allowing secure task distribution across multiple independent devices without compromising individual device security.
Solution Approach 2:
The system creates a universal interface through standardized feature vectors that enable different computing entities with diverse capabilities to participate in distributed processing. This universal mechanism allows various device types and capabilities to be integrated into the distributed system while maintaining their individual security and operational independence.
4Reliability
If permanent hardware acquisition is used to ensure processing capability, then processing reliability is improved, but cost and flexibility decrease
Solution Approach 1:
The patent transforms the static model of permanent hardware acquisition into a dynamic model where processing capability is adaptively selected from the network based on task requirements. Computing entities can dynamically join and leave the network, and task distribution can be dynamically adjusted based on current device availability and capability matching, providing both reliability and flexibility.
Data Source
AI summary
A device and method, comprising: determining a quantum of data storage and processing capability/capacity to be offered for distributed processing; retrieving a current trustworthiness rating; constructing a features vector comprising quantum and rating; and entering a pool of candidates for selection by initiator by externalising the features vector. An initiator and method, comprising: determining a minimum quantum of storage and processing capability/capacity and minimum trustworthiness rating required for the processing task; constructing a requirement vector comprising quantum and rating; querying a network for a pool of candidates to perform the task; retrieving a features vector from a candidate; comparing features vector and requirement vector to determine which candidates meet the minimum quantum and rating required for the task; responsive to finding that a candidate meets the minima, selecting the candidate and dispatching task for processing at the candidate and rating the candidate on completion/non-completion/non-completion to standard required.