Distributed Control Agents for Scalable Task Execution
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Solution Overview
Problem
Existing centralized data center systems face scalability limitations, lag issues, high costs due to data replication, and single point of failure risks when managing and processing data from a large number of customer nodes.
Innovation Solution
A method and system that delegate the management and execution of tasks to customer nodes by deploying control agents, allowing tasks to be executed locally on customer computers, thereby reducing reliance on the centralized data center and enhancing scalability, speed, and fault tolerance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If data is periodically pushed to centralized backend, then data storage is simplified, but lag issues occur
Solution Approach 1:
The patent implements real-time data streaming from customer nodes to the backend, eliminating the periodic push mechanism. This preliminary action of continuous data transmission prevents lag by ensuring data is available immediately upon generation, rather than waiting for periodic intervals.
2Ease of operation
If all data is replicated in centralized data center, then data access is simplified, but costs increase due to vast storage capacity requirements
Solution Approach 1:
The patent implements a distributed data storage architecture where data is stored locally at customer nodes rather than being replicated centrally. This allows data access to occur locally without requiring vast centralized storage capacity, reducing costs while maintaining ease of access through the portal that queries distributed data sources.
3Extent of automation
If backend processes all data, then centralized control is maintained, but scalability is limited
Solution Approach 1:
The patent segments the data processing workload by deploying control agents at customer nodes that perform local data processing and filtering. This segmentation allows the backend to focus on high-level coordination and query management, enabling the system to scale to large numbers of customer nodes without overwhelming the centralized backend.
4Device complexity
If centralized backend processes all tasks, then system coordination is simplified, but single point of failure risk increases
Solution Approach 1:
The patent deploys control agents at customer nodes that are pre-configured to perform local data processing and task execution. These agents operate autonomously based on policies pushed from the backend, eliminating the single point of failure by distributing processing capabilities while maintaining simplified coordination through policy-based management.
Data Source
AI summary
Described are systems and methods for scaling performance across a large number of customer nodes by delegating management of execution of one or more tasks to the customer nodes. An example method may commence with ascertaining a set of the customer nodes eligible for delegation of the one or more tasks. The method may continue with deploying one or more control agents to the eligible set of the customer nodes. The one or more control agents may be configured to coordinate and execute the one or more tasks on the eligible set of customer nodes and selectively take one or more actions based on results of the execution of the one or more tasks.