Improving Throughput and Reliability of Distributed Scientific Workflows for Streaming Data Processing

With the advent of next-generation scientific applications, the workflow-based computing technology has become an indispensable research method for managing and streamlining large-scale distributed data processing. This paper investigates a problem of mapping distributed workflows for streaming data processing in faulty networks where nodes and links are subject to probabilistic failures. We formulate this problem as a bi-objective optimization problem in terms of both throughput and reliability, and propose a decentralized layer-oriented method to achieve high throughput for smooth data flow while satisfying a prespecified overall failure rate bound for a guaranteed level of reliability. The superiority of the proposed mapping solution is illustrated by both extensive simulation-based performance comparisons with existing algorithms and experimental results from a real-life scientific workflow deployed in wide-area networks.