Micro-computation Decision Service Availability via Execution Flow Configurations
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Solution Overview
Problem
Decision services in production computing environments face low availability due to intermittent issues like network connectivity loss and task timeouts, leading to failed computations and errors, which conventional systems handle by simply returning an error without re-attempting the task.
Innovation Solution
An intelligent execution manager utilizes execution flow configurations, metadata, and service level agreements (SLAs) to dynamically determine whether to re-run failed computing tasks, considering execution times and historical success rates, to minimize impact on overall service availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems return an error without re-attempting failed tasks, then system complexity is reduced, but service availability deteriorates
Solution Approach 1:
The system dynamically determines whether to re-run failed tasks based on real-time conditions such as execution flow configurations, service level agreements, and current system state. The execution manager adjusts retry behavior adaptively rather than using fixed rules, allowing the system to balance complexity and availability dynamically based on operational context
Solution Approach 2:
An execution manager is introduced as an intermediary component between the task execution system and error handling. This mediator evaluates execution flow configurations, metadata, and SLAs to intelligently decide whether to re-run failed tasks, thereby managing the complexity of availability enhancement without requiring changes to the core execution system
2Reliability
If failed tasks are re-run dynamically, then service availability is improved, but processing time increases
Solution Approach 1:
Execution flow configurations and service level agreements are pre-defined before task execution. These configurations specify conditions under which tasks should be re-run and time constraints for such re-runs. By preparing these rules in advance, the system can quickly evaluate whether to re-attempt failed tasks without adding significant processing overhead during critical execution paths
Solution Approach 2:
The system applies re-run logic selectively rather than universally. The execution manager evaluates each failed task against predefined configurations and SLAs to determine if a re-run is appropriate, applying partial action only where beneficial. This avoids the excessive time cost of re-running all failed tasks while still improving availability where it matters
3Productivity
If execution flow configurations are used to manage re-runs, then task completion rate is improved, but system complexity increases
Solution Approach 1:
The execution flow configuration is segmented into distinct components: task definitions, execution paths, failure conditions, and service level agreements. Each segment can be independently configured and managed. This modular structure allows the system to handle complex re-run logic through organized, manageable configuration elements rather than monolithic complexity
Data Source
AI summary
There are provided systems and methods for increasing availability of a micro-computation decision service by utilizing execution flow configurations. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules and/or artificial intelligence models for decision-making of data including data in production computing environment. A decision service may normally be used for data processing and decision-making through an execution flow configuration and/or graph identifying a flow of task executions and other computing operations.