@article {1187,
	title = {Checkpointing Workflows for Fail-Stop Errors},
	journal = {IEEE Transactions on Computers},
	volume = {67},
	year = {2018},
	month = {2018-08},
	pages = {1105{\textendash}1120},
	abstract = {We consider the problem of orchestrating the execution of workflow applications structured as Directed Acyclic Graphs (DAGs) on parallel computing platforms that are subject to fail-stop failures. The objective is to minimize expected overall execution time, or makespan. A solution to this problem consists of a schedule of the workflow tasks on the available processors and of a decision of which application data to checkpoint to stable storage, so as to mitigate the impact of processor failures. To address this challenge, we consider a restricted class of graphs, Minimal Series-Parallel Graphs (M-SPGS), which is relevant to many real-world workflow applications. For this class of graphs, we propose a recursive list-scheduling algorithm that exploits the M-SPG structure to assign sub-graphs to individual processors, and uses dynamic programming to decide how to checkpoint these sub-graphs. We assess the performance of our algorithm for production workflow configurations, comparing it to an approach in which all application data is checkpointed and an approach in which no application data is checkpointed. Results demonstrate that our algorithm outperforms both the former approach, because of lower checkpointing overhead, and the latter approach, because of better resilience to failures.},
	keywords = {checkpoint, fail-stop error, resilience, workflow},
	url = {http://ieeexplore.ieee.org/document/8279499/},
	author = {Li Han and Louis-Claude Canon and Henri Casanova and Yves Robert and Frederic Vivien}
}
@article {1193,
	title = {Computing the Expected Makespan of Task Graphs in the Presence of Silent Errors},
	journal = {Parallel Computing},
	volume = {75},
	year = {2018},
	month = {2018-07},
	pages = {41{\textendash}60},
	abstract = {Applications structured as Directed Acyclic Graphs (DAGs) of tasks occur in many domains, including popular scientific workflows. DAG scheduling has thus received an enormous amount of attention. Many of the popular DAG scheduling heuristics make scheduling deci- sions based on path lengths. At large scale compute platforms are subject to various types of failures with non-negligible probabilities of occurrence. Failures that have recently re- ceived increased attention are {\textquotedblleft}silent errors,{\textquotedblright} which cause data corruption. Tolerating silent errors is done by checking the validity of computed results and possibly re-executing a task from scratch. The execution time of a task then becomes a random variable, and so do path lengths in a DAG. Unfortunately, computing the expected makespan of a DAG (and equivalently computing expected path lengths in a DAG) is a computationally dif- ficult problem. Consequently, designing effective scheduling heuristics in this context is challenging. In this work, we propose an algorithm that computes a first order approxi- mation of the expected makespan of a DAG when tasks are subject to silent errors. We find that our proposed approximation outperforms previously proposed approaches both in terms of approximation error and of speed.},
	keywords = {Expected makespan, fault-tolerance, scheduling, Scientific workflows, silent errors, Task graphs},
	doi = {https://doi.org/10.1016/j.parco.2018.03.004},
	author = {Henri Casanova and Julien Herrmann and Yves Robert}
}
@conference {1098,
	title = {Checkpointing Workflows for Fail-Stop Errors},
	booktitle = {IEEE Cluster},
	year = {2017},
	month = {2017-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Honolulu, Hawaii},
	abstract = {We consider the problem of orchestrating the execution of workflow applications structured as Directed Acyclic Graphs (DAGs) on parallel computing platforms that are subject to fail-stop failures. The objective is to minimize expected overall execution time, or makespan. A solution to this problem consists of a schedule of the workflow tasks on the available processors and of a decision of which application data to checkpoint to stable storage, so as to mitigate the impact of processor failures. For general DAGs this problem is hopelessly intractable. In fact, given a solution, computing its expected makespan is still a difficult problem. To address this challenge, we consider a restricted class
of graphs, Minimal Series-Parallel Graphs (M-SPGS). It turns out that many real-world workflow applications are naturally structured as M-SPGS. For this class of graphs, we propose a recursive list-scheduling algorithm that exploits the M-SPG structure to assign sub-graphs to individual processors, and uses dynamic programming to decide which tasks in these sub-graphs should be checkpointed. Furthermore, it is possible to efficiently compute the expected makespan for the solution produced by this algorithm, using a first-order approximation of task weights and existing evaluation algorithms for 2-state probabilistic DAGs. We assess the performance of our algorithm for production workflow configurations, comparing it to (i) an approach in which all application data is checkpointed, which corresponds to the standard way in which most production workflows are executed today; and (ii) an approach in which no application data is checkpointed. Our results demonstrate that our algorithm strikes a good compromise between these two approaches, leading to lower checkpointing overhead than the former and to better resilience to failure than the latter.},
	author = {Li Han and Louis-Claude Canon and Henri Casanova and Yves Robert and Frederic Vivien}
}
