Dynamic API Execution Plan Generation and Adaptation
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Solution Overview
Problem
Existing API execution environments face challenges in efficiently managing and processing concurrent API requests, leading to suboptimal resource utilization and increased latency due to static execution plans that do not adapt to changing conditions.
Innovation Solution
An API execution service that dynamically maps dependency graphs to generate execution plans, allowing for optimal ordering and parallel execution of actions, and modifies these plans based on performance monitoring data to ensure efficient resource usage and reduced latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static execution plans are used to process API requests, then the system structure is simple and easy to manage, but resource utilization is suboptimal and latency increases due to inability to adapt to changing conditions
Solution Approach 1:
The patent implements dynamic execution plans that automatically adapt to changing system conditions such as resource availability, request patterns, and service performance. The execution plan generator continuously monitors system state and reorders or selects actions based on current conditions, transforming static plans into dynamic, self-adjusting execution strategies that optimize resource utilization without manual intervention.
Solution Approach 2:
The system incorporates feedback mechanisms where performance data from executed actions is fed back into the execution plan generator. This feedback loop enables the system to learn from past performance, identify bottlenecks, and modify future execution plans accordingly, creating a continuous improvement cycle that enhances adaptability while maintaining manageable complexity through automation.
2Productivity
If concurrent API requests are processed without dynamic adjustment, then processing speed is maintained at acceptable levels, but resource utilization efficiency decreases and latency increases
Solution Approach 1:
The execution plan generator performs preliminary analysis of dependency graphs and system conditions before executing API requests. By pre-determining optimal action orderings and identifying parallelizable tasks based on current system state, the system prepares execution strategies in advance that minimize runtime delays and optimize resource allocation, reducing both latency and improving overall processing efficiency.
Solution Approach 2:
The patent segments API request processing into discrete actions with defined dependencies, allowing independent optimization of each action's execution timing. This segmentation enables the system to process actions in parallel where possible while maintaining correct ordering where dependencies require sequential execution, thereby improving productivity without increasing latency through optimized concurrency management.
Data Source
AI summary
The systems and methods are provided for the dynamic processing of API requests received by an API execution service. The API execution service may map out a dependency graph based on dependency relationships between all the actions involved in processing the API requests, some of which can be determined from the API definitions and configurations. From the dependency graph, an execution plan can be generated that represents a request processing pipeline conveying the optimal order and arrangement to perform the actions (e.g., serially, in parallel). The execution plan can be followed to process API requests and its performance monitored. The API execution service may dynamically modify this execution plan used to process API requests as it becomes apparent that the execution plan is no longer optimal.