Task Assignment Server Optimizing Throughput via Dynamic Resource Matching
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Solution Overview
Problem
Conventional load balancing systems fail to account for the variability among clients and resources, leading to inefficient task distribution and potential overloading, which can result in increased response times and reduced system throughput.
Innovation Solution
A computer-implemented system that uses a task assignment server to intelligently distribute tasks among worker resources based on availability, capability, and relationship information, including feedback from clients and resources, to optimize task assignment and ensure successful task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional load balancing is used to distribute tasks among resources, then task distribution is achieved, but it fails to account for client and resource variability leading to inefficient distribution and potential overloading
Solution Approach 1:
The patent changes the parameters used for load balancing from generic metrics to specific parameters including client attributes (preferences, location, device type) and resource attributes (capabilities, availability, performance history). This allows the system to match tasks to resources based on multiple dimensions of variability, improving both throughput and success rates by avoiding mismatches that cause failures or overloading
Solution Approach 2:
The system dynamically adjusts task distribution based on real-time feedback from clients and resources. The load balancer continuously learns from task outcomes, client preferences, and resource performance, adapting the distribution strategy to changing conditions. This dynamic approach prevents static overloading while maintaining high throughput through optimized routing
2Speed
If tasks are distributed without considering client and resource attributes, then distribution speed is maintained, but response time increases and system throughput is reduced
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing client attributes and resource capabilities in databases before task distribution is needed. The load balancer has pre-established knowledge of client preferences, resource availability, and performance characteristics, enabling rapid matching without real-time analysis delays. This preliminary preparation maintains fast assignment speed while improving response time through informed decision-making
3Device complexity
If uniform task distribution is applied to all resources, then simplicity is maintained, but resource overloading occurs and system efficiency decreases
Solution Approach 1:
The system implements feedback mechanisms where the load balancer receives outcome information from completed tasks, availability updates from resources, and preference data from clients. This feedback loop enables the system to learn from past performance and continuously improve distribution decisions. The feedback-driven approach achieves efficient, non-uniform distribution without requiring complex manual configuration, maintaining algorithmic simplicity while boosting throughput through adaptive optimization
Data Source
AI summary
Computer implemented systems and methods intelligently distribute tasks received from clients among worker resources. One or more databases store information about worker resources and information about clients. A task assignment server, communicatively coupled to the database(s), receives a plurality of tasks that are to be performed for the clients, accesses the stored information about the worker resources, accesses the stored information about the clients, and assigns each of a majority of the tasks to one of the plurality of worker resources, in dependence on the information about the plurality of worker resources and in dependence on the information about the plurality of clients, so that the plurality of tasks are distributed among two or more of the plurality of worker resources. The system can also include a plurality of queues adapted to store information about tasks assigned to the worker resources associated with the queues.