Abstract

Scientific workflows have become mainstream for conducting large-scale scientific research. As a result, many workflow applications and Workflow Management Systems (WMSs) have been developed as part of the cyberinfrastructure to allow scientists to execute their applications seamlessly on a range of distributed platforms. In spite of many success stories, a key challenge for running workflows in distributed systems is failure prediction, detection, and recovery. In this paper, we propose an approach to use control theory developed as part of autonomic computing to predict failures before they happen, and mitigated them when possible. The proposed approach applying the proportional-integralderivative controller (PID controller) control loop mechanism, which is widely used in industrial control systems, to mitigate faults by adjusting the inputs of the controller. The PID controller aims at detecting the possibility of a fault far enough in advance so that an action can be performed to prevent it from happening. To demonstrate the feasibility of the approach, we tackle two common execution faults of the Big Data era—data storage overload and memory overflow. We define, implement, and evaluate simple PID controllers to autonomously manage data and memory usage of a bioinformatics workflow that consumes/produces over 4.4 TB of data, and requires over 24TB of memory to run all tasks concurrently. Experimental results indicate that workflow executions may significantly benefit from PID controllers, in particular under online and unknown conditions. Simulation results show that nearly-optimal executions (slowdown of 1.01) can be attained …

Date

2017

Authors

Rafael Ferreira da Silva, Rosa Filgueira, Ewa Deelman, Erola Pairo-Castineira, Ian Overton, Malcolm Atkinson

Conference

11th Workshop on Workflows in Support of Large-Scale Science co-located with The International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2016)

Pages

15-24

Publisher

CEUR Workshop Proceedings (CEUR-WS. org)