Towards an autonomous decentralized orchestration system

Orchestrating workflows needed for modern scientific data analysis presents a significant research challenge: they are typically executed in a centralized manner such that all data pass through a single compute server known as the engine, which causes unnecessary network traffic that leads to a performance bottleneck. This paper presents a scalable decentralized orchestration system that relies on a functional, high-level data coordination language for executing workflows. This system consists of distributed execution engines, each of which is responsible for executing part of the overall workflow. It exploits parallelism in the workflow by partitioning it into smaller sub-workflows and determines the most appropriate engines to execute them using network resource monitoring and placement analysis. This permits the computation logic of the workflow to be moved towards the services providing the data, which improves the overall execution time. The system supports data-driven execution that allows each sub-workflow to be executed as soon as the data needed for its execution become available from other sources. Therefore, a scheduling mechanism is not required to manage the order in which the sub-workflows are orchestrated. This paper provides an evaluation of the proposed system, which demonstrates that decentralized orchestration provides scalability over centralized orchestration. Copyright (C) 2015 John Wiley & Sons, Ltd.