These tools provide multidimensional visibility into operational bottlenecks and error conditions as they occur, in a manner that is aggregated across hundreds of VMs. 4) and extraction wherein the logs are stored in a queryable search index 9. Application-level monitoring is facilitated via systematic log collection (Supplementary Fig. A set of graphical dashboards reports system health to users while supporting advanced querying capabilities for in-depth troubleshooting (Supplementary Fig. These metrics are aggregated and stored in a time-series database within Butler’s monitoring server. Host-level operational management is facilitated via a health metrics system that collects system measurements at regular intervals from all deployed virtual machines (VMs). The toolkit functions at two levels of granularity: host level and application level. In contrast to previous tools, Butler provides an operational management toolkit that quickly discovers and resolves expected and unexpected failures (Fig. Delays in recognizing and resolving these failures can notably affect data processing rate and increase project duration and costs.
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Sequencing library artifacts, sample contamination and nonuniform sequencing coverage 8 can cause data and software anomalies that challenge current workflows.
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Butler, in contrast, provides full support for operation on OpenStack-based commercial and academic clouds, Amazon Web Services, Microsoft Azure and Google Compute Platform, and can thus enable international collaborations involving the analysis of hundreds of thousands of samples where distributed cloud-based computation is pursued in different jurisdictions 5, 6, 7.Ī key lesson learned from large-scale projects including the PCAWG project 7, which has pursued a study of 2,658 cancer genomes sequenced by the International Cancer Genome Consortium and the Cancer Genome Atlas, is that analysis of biological data of heterogeneous quality, generated at multiple locations with varying standard operating procedures, frequently suffers from artifacts that lead to many failures of computational jobs and that can considerably limit a project’s progress. This limits their use in studies that require multi-cloud operation due to practical and regulatory requirements 5, 6. The recently developed cloud-based scientific workflow frameworks Nextflow 2, Toil 3 and GenomeVIP 4 focus their support largely on individual commercial cloud computing environments-mostly Amazon Web Services-and lack complete functionality for other major providers. When employed in global projects, such systems must be flexible in their ability to operate in different environments, including academic clouds, to allow researchers to bring their computational pipelines to the data, especially in cases where the raw data themselves cannot be moved.
To take advantage, large biological datasets are increasingly analyzed on various cloud computing platforms, using public, private and hybrid clouds 1 with the aid of workflow systems. Cloud computing offers easy and economical access to computational capacity at a scale that had previously been available to only the largest research institutions.
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