In large-scale distributed systems, failures are routine events whose occurrences increase with the number of computational tasks and execution locations. The advantage of representing an application as a workflow is the possibility of exploiting Workflow Management System (WMS) features such as portability, scalability, and, crucially, reliability. Among these, reliability is essential for ensuring robust execution in dynamic and failure-prone environments. In recent years, the emergence of hybrid workflows has posed new and intriguing challenges by increasing the possibility of distributing computations involving heterogeneous and independent environments. Consequently, the number of possible points of failure during the execution increased, creating a need for sophisticated fault tolerance mechanisms capable of addressing the specific requirements of hybrid systems. This work introduces a formal framework for a fault tolerance mechanism in hybrid workflows, enabling failure recovery through a rollback approach. The framework is rigorously defined by adapting and extending an existing workflow semantics tailored for hybrid execution. Our method leverages provenance data from workflow execution up to the point of failure, and creates a recovery workflow that spans multiple infrastructures. The rollback approach provides a robust and reliable strategy to ensure resilience against step failures and potential data loss. We then implement this mechanism in the StreamFlow WMS, and evaluate it using two case studies: the 1000 Genomes workflow and a synthetic workflow featuring iterative patterns. Experiments showcase the conceptual validity of our approach and assess the overhead introduced by the mechanism, including data availability checks.

Abstract In the ever-evolving landscape of scientific computing, properly supporting the modularity and complexity of modern scientific applications requires new approaches to workflow execution, like seamless interoperability between different workflow systems, distributed-by-design workflow models, and automatic optimisation of data movements. In order to address this need, this article introduces SWIRL, an intermediate representation language for scientific workflows. In contrast with other product-agnostic workflow languages, SWIRL is not designed for human interaction but to serve as a low-level compilation target for distributed workflow execution plans. The main advantages of SWIRL semantics are low-level primitives based on the send/receive programming model and a formal framework ensuring the consistency of the semantics and the specification of translating workflow models represented by Directed Acyclic Graphs (DAGs) into SWIRL workflow descriptions. Additionally, SWIRL offers rewriting rules designed to optimise execution traces, accompanied by corresponding equivalence. An open-source SWIRL compiler toolchain has been developed using the ANTLR Python3 bindings.

In recent years we have understood the importance of analyzing and sequencing human genetic variation. A relevant aspect that emerged from the Covid-19 pandemic was the need to obtain results very quickly; this involved using High-Performance Computing (HPC) environments to execute the Next Generation Sequencing (NGS) pipeline. However, HPC is not always the most suitable environment for the entire execution of a pipeline, especially when it involves many heterogeneous tools. The ability to execute parts of the pipeline on different environments can lead to higher performance but also cheaper executions. This work shows the design and optimization process that led us to a state-of-the-art Variant Calling hybrid workflow based on the StreamFlow Workflow Management System (WfMS). We also compare StreamFlow with Snakemake, an established WfMS targeting HPC facilities, observing comparable performance on single environments and satisfactory improvements with a hybrid cloud-HPC configuration.