Grid Job Matching With User-Selected Compute Resource Sharing
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Solution Overview
Problem
Existing grid computing systems fail to motivate users to participate by allowing them to select jobs that utilize their arithmetic devices' calculation capabilities.
Innovation Solution
A management system that allows users to select jobs providing calculation capabilities and offers incentives, recommending jobs based on user preferences, thereby facilitating the provision of calculation resources to grid computing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the system automatically assigns jobs to arithmetic devices without user selection, then the system complexity is reduced, but user motivation to participate in grid computing decreases
Solution Approach 1:
The patent adds a new dimension of user choice and incentive mechanisms to the automatic job assignment system. Users can now select from multiple available jobs and receive incentives based on their participation, transforming the system from a purely automated resource allocation model to one that incorporates user agency and motivation through additional operational dimensions.
2Productivity
If the system provides multiple job selection options with incentives, then user motivation increases, but the system complexity increases
Solution Approach 1:
The management server is designed to perform multiple functions: it not only assigns jobs automatically but also presents multiple job options to users, calculates and provides incentives, and manages the interaction between users and the job selection interface. This multi-functional approach allows the system to handle both automated and user-driven job allocation within a single unified platform.
Solution Approach 2:
The patent introduces an intermediary interface between the job assignment system and users. This intermediary layer presents job information, collects user selections, and manages incentive distribution, thereby mediating the interaction between the automated system and human participants without requiring complete system redesign.
3Measurement precision
If the system monitors and predicts calculation capability availability, then job allocation accuracy improves, but the information processing requirements increase
Solution Approach 1:
The system performs preliminary monitoring and prediction of calculation capability availability before jobs are assigned. By proactively gathering information about arithmetic device states and predicting future availability, the system prepares allocation data in advance, reducing the need for intensive real-time information processing during actual job assignment.
Data Source
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AI summary
In prediction processing, the controller (505) predicts a temporal change in a calculation capability available in grid computing processing of each of a plurality of arithmetic devices (105). In selection processing, the controller (505) allows a user to select a job for which the calculation capability of the arithmetic device owned by the user is desired to be provided from jobs introduced by a job list. In the matching processing, on the basis of a result of the prediction processing and a result of the selection processing, the controller (505) allocates an arithmetic device 105 that is available in the grid computing processing and is owned by the user to job data corresponding to the job selected by the user in the selection processing among the plurality of jobs.