Dynamic Client Task Allocation Based on Resource Detection
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Solution Overview
Problem
Conventional client/server distributed applications do not account for varying client resource capabilities, leading to inefficient resource utilization and increased server load, particularly in devices with limited resources like handheld devices, which can result in poor performance and unnecessary resource consumption.
Innovation Solution
A system and method for dynamically allocating application tasks between a client and a server based on available client resources, using a load analyzer to detect computing resources and a load distributor to selectively allocate tasks, allowing for automatic task distribution and configuration settings to optimize workload balancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a one size fits all paradigm is used in client/server applications, then implementation simplicity is maintained, but client responsiveness deteriorates due to ignoring varying client processing power and resources
Solution Approach 1:
The system dynamically adjusts task allocation strategies based on real-time client resource assessment. The server continuously monitors client capabilities (CPU, memory, bandwidth) and adapts task distribution accordingly, transitioning from static one-size-fits-all to dynamic resource-aware allocation that optimizes responsiveness for each client's current state
Solution Approach 2:
The invention changes the parameter of task allocation from uniform to variable based on client resource parameters. By monitoring and incorporating client-specific parameters (processing power, memory availability, network bandwidth) into the allocation decision, the system optimizes performance for each client while maintaining manageable implementation through automated parameter collection and evaluation
2Loss of energy
If client resources are not accessed or utilized, then server resources are consumed unnecessarily, but implementing resource-aware allocation increases system complexity
Solution Approach 1:
The system implements self-service through automated client resource reporting and server-side task allocation algorithms. Clients automatically report their resource status without manual intervention, and the server autonomously makes allocation decisions based on collected data, reducing the need for complex manual configuration while optimizing resource utilization
Solution Approach 2:
The architecture incorporates feedback loops where clients periodically report resource availability to the server, which then adjusts task allocation based on this feedback. This continuous feedback mechanism enables adaptive resource distribution that reduces server workload while implementing complexity through standardized feedback protocols rather than complex decision logic
3Power
If speech processing tasks are executed locally on client devices with limited resources, then server load is reduced, but task execution reliability deteriorates due to insufficient client processing power
Solution Approach 1:
The system applies local quality by tailoring task allocation decisions to each client's specific capabilities rather than applying uniform treatment. Clients with sufficient processing power receive locally-executable tasks, while clients with limited resources are assigned server-based tasks, optimizing both server load reduction and task execution reliability for each individual client
Solution Approach 2:
The task allocation strategy dynamically adapts to changing client resource conditions. The system continuously assesses client capabilities and adjusts task distribution in real-time, ensuring that speech processing tasks are allocated to clients only when they have sufficient resources available, thereby maintaining reliability while reducing server load
Data Source
AI summary
A software method for allocating application tasks between a client and a server can include the step of detecting client-based computing resources for executing at least one application task. At least one indicator of the detected client-based computing resources can be conveyed to a remotely located application server, the application server can determine whether to allocate at least one application task to the client or to a server component based upon at least one indicator.