Dynamic Bot Swarm Configuration for High-Volume Processing
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Solution Overview
Problem
Statically configured computing systems often operate at sub-optimal efficiencies due to varying request volumes, fluctuating resource responsiveness, and unpredictable data complexity, with existing middleware solutions being expensive and inefficient at scale.
Innovation Solution
A computer-implemented method that dynamically configures a swarm of processing bots using a communications fabric, allowing bots to autonomously adjust their operations based on chatter-processing rules, enabling elastic scaling and rapid task-processing adaptations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a statically configured computing system is used, then system structure is simple and easy to manage, but processing efficiency deteriorates due to varying request volumes and unpredictable data complexity
Solution Approach 1:
The patent implements dynamic configuration of processing bots that can be created, modified, and terminated based on real-time request characteristics. The system transitions from static to dynamic by allowing the processing architecture to adapt its structure and resources according to varying workload demands, data complexity, and external resource responsiveness.
Solution Approach 2:
The system segments processing tasks into independent bot units that can operate autonomously. Each bot handles specific tasks or sub-tasks, allowing the system to scale processing capacity by adding or removing individual bot instances rather than reconfiguring the entire system, thus improving productivity without proportionally increasing management complexity.
2Adaptability or versatility
If middleware is introduced to manage and coordinate resources, then resource coordination capability is improved, but system cost and operational efficiency deteriorate due to synchronization delays and lock management overhead
Solution Approach 1:
Processing bots autonomously coordinate their own operations and resource usage without requiring centralized middleware control. Each bot independently manages its task execution, state transitions, and resource allocation decisions, eliminating synchronization delays and lock management overhead while maintaining effective resource coordination through decentralized self-organization.
Solution Approach 2:
The patent extracts the coordination function from centralized middleware and distributes it to individual processing bots. By removing the middleware layer and embedding coordination logic directly within each bot, the system eliminates the time losses associated with centralized synchronization while preserving resource coordination capabilities through autonomous bot behavior.
3Adaptability or versatility
If the system operates with fixed configuration, then system stability is maintained, but adaptability to changing conditions deteriorates due to varying request volumes and external resource responsiveness
Solution Approach 1:
The system achieves adaptability to changing conditions through dynamic bot configuration. Processing bots can be instantiated, modified, or terminated based on real-time monitoring of request volumes, data complexity, and external resource responsiveness, allowing the system to adapt its composition dynamically while maintaining operational stability through controlled lifecycle management.
4Productivity
If more processing resources are allocated to handle high-volume requests, then processing capacity is improved, but system cost and complexity increase
Solution Approach 1:
The system segments processing capacity into discrete, independently deployable bot units. This allows processing resources to be allocated in granular increments matching actual workload requirements, improving processing capacity without proportionally increasing overall system resources. Bots can be added or removed based on demand, optimizing the ratio of processing capacity to resource consumption.
Solution Approach 2:
The system dynamically changes operational parameters such as bot instance count, task allocation patterns, and processing priorities based on workload characteristics. By adjusting these parameters rather than permanently allocating additional resources, the system achieves variable processing capacity that responds to demand fluctuations without permanently increasing resource consumption or system complexity.
Data Source
AI summary
Embodiments relate to dynamically configuring a swarm of processing bots to autonomously execute tasks corresponding to a request. A communications fabric enables broadcasts of processing and status data from individual bots to other bots, which can locally determine whether and/or how the communications are to affect its processing.