Server Resource Allocation via Pre-computed Data Revision
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Solution Overview
Problem
In multi-user limited resource allocation systems, servers face increased load and resource consumption due to the need for constant comparisons and data processing as the number of users and data volume increase, leading to stress and inefficiency.
Innovation Solution
Implementing a method where the server determines the number of users and revises resource allocation data based on a predetermined rule, reducing the need for repeated comparisons and data processing by allocating resources based on revised data, such as adjusted prices, thereby minimizing server load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the server constantly compares data transmitted by users to determine resource allocation, then the resource allocation accuracy is improved, but the server load increases
Solution Approach 1:
The server performs preliminary actions by pre-calculating and storing allocation results for different user combinations before actual resource allocation requests occur. When users request resources, the server directly retrieves pre-computed results rather than performing real-time comparisons, thereby maintaining allocation accuracy while dramatically reducing server load during operation.
Solution Approach 2:
The server performs preliminary actions by pre-calculating and storing allocation results for different user combinations before actual resource allocation requests occur. When users request resources, the server directly retrieves pre-computed results rather than performing real-time comparisons, thereby maintaining allocation accuracy while dramatically reducing server load during operation.
2Reliability
If a single user makes multiple data transmissions to win the resource, then the user's chance of obtaining the resource is improved, but the volume of data processed by the server increases
Solution Approach 1:
Instead of processing each user's multiple transmission requests individually, the server creates a consolidated copy of the allocation decision that applies to all users simultaneously. The pre-computed allocation results are replicated and distributed to relevant users, reducing the volume of data processed while maintaining each user's opportunity to obtain resources through their bid strategy.
3Speed
If the server receives and processes multiple transmissions of data from users continuously, then the resource allocation responsiveness is improved, but the resources consumed by the server increase
Solution Approach 1:
The server implements periodic action by pre-computing allocation results at predetermined intervals or in advance, rather than continuously processing each user transmission in real-time. This periodic pre-computation maintains allocation responsiveness when users need results while significantly reducing continuous server resource consumption during the allocation process.
Data Source
AI summary
Embodiments of the present application relate to a multi-user limited resource allocation processing method, a multi-user limited resource allocation processing system, and a computer program product for allocating a limited resource among a plurality of users. A multi-user limited resource allocation processing method is provided. The method includes receiving a plurality of participation requests for a limited resource from a plurality of users and determining the number of the plurality of users sending the plurality of participation requests. In response to the number of the plurality of users reaching a predetermined value, revising data corresponding to the limited resource according to a revision rule and allocating the limited resource to the plurality of users sending the participation request using the revised data.