🤖 AI Summary
This work addresses the limitations of traditional workflow platforms, which rely on static, pre-defined processes and struggle to accommodate the dynamic data integration demands of distributed systems. To overcome this, the authors propose a configuration-driven runtime orchestration framework that dynamically constructs execution graphs at request time through dependency-aware scheduling and parallel task execution, thereby circumventing the constraints of fixed workflows. This approach enables rapid adaptation to evolving integration scenarios without requiring system redeployment, significantly reducing latency. Empirical evaluation in a real-world Customer 360 enterprise use case demonstrates that the framework offers substantial advantages in flexibility, scalability, and efficient data aggregation compared to conventional solutions.
📝 Abstract
Modern enterprise platforms increasingly depend on distributed microservices, analytical data platforms, and external APIs to construct composite responses for applications. Orchestrating data retrieval across these heterogeneous systems is challenging because many workflow platforms rely on predefined workflows or state-machine definitions. Systems such as Apache Airflow, AWS Step Functions, and Temporal provide powerful orchestration capabilities but typically assume workflows are defined prior to execution. This paper presents a configuration-driven runtime orchestration framework for dynamic data retrieval in distributed systems. The framework generates execution graphs dynamically from configuration at request time, enabling low-latency orchestration without redeploying workflow code when integrations evolve. The execution planner performs dependency-aware scheduling and parallel execution of independent tasks, allowing efficient aggregation across distributed services. The paper describes the architecture, execution model, and operational tradeoffs of this framework, and presents a representative enterprise case study for Customer 360 retrieval. The approach demonstrates how runtime configuration can enable flexible and scalable orchestration in rapidly evolving integration environments.