Configurable Event-Based Automation Architecture
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Solution Overview
Problem
Current automation architectures for collaboration environments are not scalable and do not allow for user customization of jobs in response to specific actions, limiting their effectiveness in distributed computing environments.
Innovation Solution
A configurable event-based automation architecture that uses a rule-based engine to translate user-specified rules into job requests, enabling customizable job generation and execution in a scalable and distributed manner, with components like a rule manager and job manager to manage and execute these jobs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If automation jobs are scheduled at the front-end (web or application servers), then jobs can be executed in response to user actions, but the architecture becomes difficult to scale and lacks user customization capability
Solution Approach 1:
The system segments the automation architecture into distinct modular components: event sources that generate events, a rule engine that processes events and evaluates conditions, job definitions that specify actions, and an execution engine that carries out jobs. This segmentation enables independent configuration and scaling of each component, providing user customization capability while maintaining architectural clarity and manageability
Solution Approach 2:
The rule engine serves as a universal component that handles multiple types of events from different sources and applies user-defined rules to generate various job types. This multi-functional design allows the same core engine to support diverse automation scenarios without requiring separate specialized systems, thereby improving adaptability without proportionally increasing complexity
2Productivity
If automation jobs are scheduled at the front-end, then immediate execution is possible, but the system lacks scalability in distributed computing environments
Solution Approach 1:
The system introduces an event stream as an intermediary component that decouples event sources from the rule engine. Events are published to the stream and can be consumed by multiple rule engine instances simultaneously, enabling scalable distribution across multiple servers while maintaining efficient job execution. This intermediary layer allows the system to scale horizontally without losing execution efficiency
Data Source
AI summary
Scalable architectures, systems, and services are provided herein for generating jobs by applying user-specified rules to events in an action log framework (ALF) stream is disclosed. More specifically, collaboration environments can include features or mechanisms that allow end-users or collaborators to perform a variety of actions such as, for example, file uploads and previews, or collaboration operations such as, for example, task assignment and comments. These actions can be captured or logged by an action log framework and, as described herein, automatically translated into one or more jobs by applying customizable user-specified rules.