Distributed Throughput Optimization for Large-Scale Scientific Workflows Under Fault-Tolerance Constraint

With the advent of next-generation scientific applications, the workflow approach that integrates various computing and networking technologies has provided a viable solution to managing and optimizing large-scale distributed data transfer, processing, and analysis. This paper investigates a problem of mapping distributed scientific workflows for maximum throughput in faulty networks where nodes and links are subject to probabilistic failures. We formulate this problem as a bi-objective optimization problem to maximize both throughput and reliability. By adapting and modifying a centralized fault-free workflow mapping scheme, we propose a new mapping algorithm to achieve high throughput for smooth data flow in a distributed manner while satisfying a pre-specified bound of the overall failure rate for a guaranteed level of reliability. The performance superiority of the proposed solution is illustrated by both extensive simulation-based comparisons with existing algorithms and experimental results from a real-life scientific workflow deployed in wide-area networks.