Scheduling scientific workflows to meet soft deadlines in the absence of failure models

Abstract

Highly distributed systems such as Clouds and Grids are used to execute complex scientific workflow applications by researchers from various areas of science. While scientists rightfully expect efficient and reliable execution of their applications, current systems often cannot deliver the required Quality of Service. We propose a dynamic execution and scheduling heuristic able to schedule workflow applications with a high degree of fault tolerance, while taking into account soft deadlines. Experimental results show that our method meets soft deadlines in volatile highly distributed systems in the absence of historic failure trace data or complex failure models of the target system. © 2010 Springer-Verlag.