Subtopic Deep Dive

Scientific Workflow Management in Grids
Research Guide

What is Scientific Workflow Management in Grids?

Scientific Workflow Management in Grids orchestrates complex, data-intensive computational pipelines across distributed grid resources using engines like Pegasus and Taverna.

Systems like Pegasus map abstract workflows to grid sites for execution (Deelman et al., 2005, 1213 citations). Taverna enables bioinformatics workflow composition via Web services (Oinn et al., 2004, 1617 citations). Yu and Buyya (2005, 829 citations) classify grid workflow systems by architecture and features.

15
Curated Papers
3
Key Challenges

Why It Matters

Pegasus supports reproducible astronomy pipelines on grids, reducing setup time by abstracting resource details (Deelman et al., 2005). Taverna accelerates bioinformatics discoveries by integrating tools for in silico experiments (Oinn et al., 2004). These systems enable e-science scalability, as in hybrid grid-cloud transitions modeled by CloudSim (Calheiros et al., 2010, 4861 citations).

Key Research Challenges

Resource Heterogeneity Handling

Grids feature diverse compute nodes and networks, complicating workflow mapping. Pegasus addresses this via abstract representations (Deelman et al., 2005). Adaptive scheduling remains difficult across failures (Yu and Buyya, 2005).

Provenance and Reproducibility

Tracking data lineage in distributed executions ensures scientific validity. Taverna logs workflow enactments for bioinformatics (Oinn et al., 2005, 653 citations). Best practices highlight versioning needs (Wilson et al., 2014, 698 citations).

Deadline-Constrained Scheduling

Grid workflows must meet time bounds amid variability. Abrishami et al. (2012, 648 citations) propose algorithms for Iaas clouds adaptable to grids. Timing anomalies exacerbate issues (Graham, 1969, 2338 citations).

Essential Papers

1.

CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov et al. · 2010 · Software Practice and Experience · 4.9K citations

Abstract Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as ‘services’ to end‐users under a usage‐based payment model. It can leverage virtualized se...

2.

A break in the clouds

Luis M. Vaquero, Luis Rodero‐Merino, Juan Cáceres et al. · 2008 · ACM SIGCOMM Computer Communication Review · 2.6K citations

This paper discusses the concept of Cloud Computing to achieve a complete definition of what a Cloud is, using the main characteristics typically associated with this paradigm in the literature. Mo...

3.

Bounds on Multiprocessing Timing Anomalies

Ron Graham · 1969 · SIAM Journal on Applied Mathematics · 2.3K citations

Previous article Next article Bounds on Multiprocessing Timing AnomaliesR. L. GrahamR. L. Grahamhttps://doi.org/10.1137/0117039PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsE...

4.

Taverna: a tool for the composition and enactment of bioinformatics workflows

Tom Oinn, Matthew Addis, Justin Ferris et al. · 2004 · Bioinformatics · 1.6K citations

Abstract Motivation: In silico experiments in bioinformatics involve the co-ordinated use of computational tools and information repositories. A growing number of these resources are being made ava...

5.

Large-scale cluster management at Google with Borg

Abhishek Verma, Luis Pedrosa, Madhukar Korupolu et al. · 2015 · 1.3K citations

Google's Borg system is a cluster manager that runs hundreds of thousands of jobs, from many thousands of different applications, across a number of clusters each with up to tens of thousands of ma...

6.

Pegasus: A Framework for Mapping Complex Scientific Workflows onto Distributed Systems

Ewa Deelman, Gurmeet Singh, Mei-Hui Su et al. · 2005 · Scientific Programming · 1.2K citations

This paper describes the Pegasus framework that can be used to map complex scientific workflows onto distributed resources. Pegasus enables users to represent the workflows at an abstract level wit...

7.

A Taxonomy of Workflow Management Systems for Grid Computing

Jia Yu, Rajkumar Buyya · 2005 · Journal of Grid Computing · 829 citations

Reading Guide

Foundational Papers

Start with Deelman et al. (2005) for Pegasus mapping framework, then Oinn et al. (2004) for Taverna enactment, followed by Yu and Buyya (2005) taxonomy to contextualize systems.

Recent Advances

Study Verma et al. (2015, Borg, 1289 citations) for large-scale management insights applicable to grids; Abrishami et al. (2012) for deadline scheduling.

Core Methods

Abstract-to-concrete mapping (Pegasus), service-oriented composition (Taverna), simulation-based evaluation (CloudSim, Calheiros et al., 2010).

How PapersFlow Helps You Research Scientific Workflow Management in Grids

Discover & Search

Research Agent uses searchPapers with 'Pegasus workflow grid' to find Deelman et al. (2005), then citationGraph reveals 1200+ downstream works on hybrid mapping. exaSearch uncovers Taverna extensions; findSimilarPapers links to Yu and Buyya (2005) taxonomy.

Analyze & Verify

Analysis Agent runs readPaperContent on Pegasus paper, verifying claims with CoVe against Oinn et al. (2004). runPythonAnalysis parses CloudSim simulation data for workflow performance stats (Calheiros et al., 2010). GRADE scores evidence strength for reproducibility claims.

Synthesize & Write

Synthesis Agent detects gaps in grid-cloud interoperability via contradiction flagging between Vaquero et al. (2008) and Deelman et al. (2005). Writing Agent uses latexSyncCitations for 20-paper review, latexCompile for workflow diagrams, exportMermaid for Pegasus execution graphs.

Use Cases

"Compare Pegasus and Taverna performance metrics in grid bioinformatics workflows"

Research Agent → searchPapers + findSimilarPapers → Analysis Agent → readPaperContent (Deelman 2005, Oinn 2004) → runPythonAnalysis (extract timing data, plot with matplotlib) → CSV export of benchmarks.

"Draft LaTeX section on grid workflow taxonomies with citations"

Synthesis Agent → gap detection (Yu 2005 gaps) → Writing Agent → latexEditText (taxonomy table) → latexSyncCitations (add Buyya papers) → latexCompile → PDF with synced refs.

"Find GitHub repos implementing grid workflow schedulers from papers"

Research Agent → citationGraph (Pegasus) → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect (Abrishami 2012 code) → verified scheduler implementations.

Automated Workflows

Deep Research scans 50+ papers via searchPapers on 'grid workflow management', outputs structured report with Pegasus/Taverna comparison (Deelman 2005, Oinn 2004). DeepScan applies 7-step CoVe to verify scheduling claims (Abrishami 2012). Theorizer generates hybrid grid-cloud models from Calheiros (2010) simulations.

Frequently Asked Questions

What defines Scientific Workflow Management in Grids?

It involves engines like Pegasus and Taverna orchestrating pipelines across distributed grid resources (Deelman et al., 2005; Oinn et al., 2004).

What are core methods in grid workflow systems?

Abstract workflow mapping (Pegasus), Web service composition (Taverna), and taxonomic classification (Yu and Buyya, 2005).

What are key papers?

Deelman et al. (2005, Pegasus, 1213 citations), Oinn et al. (2004, Taverna, 1617 citations), Yu and Buyya (2005, taxonomy, 829 citations).

What open problems exist?

Hybrid grid-cloud scheduling under deadlines (Abrishami et al., 2012), provenance in heterogeneous environments (Oinn et al., 2005), timing anomaly mitigation (Graham, 1969).

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