A Taxonomy of Workflow Management Systems for Grid Computing

By Jia Yu, Rajkumar Buyya. Journal of Grid Computing, 2005

967 citations recorded by the source corpus.

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.

Subfields: Computer Networks and Communications, Information Systems and Management, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/b6cd91e3dc2334238dee018e0a82bcdfb7d689db

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Citations in the corpus

Workflows and e-Science: An overview of workflow system features and capabilities (2009, background): “There have been other publications that have compared a subset of workflow systems for scientific applications (1) but such classifications potentially suffer from the ever-changing world of distributed computing and the evolution of new workflow solutions that are appearing at a rather frequent rat”
Pegasus, a workflow management system for science automation (2015, background): “Scientific workflows [30, 68, 69, 70] often access large amounts of input data that needs to be made available to the individual jobs when they execute.”
Computational models and heuristic methods for Grid scheduling problems (2010, neutral): “Such applications can take advantage of the power of Grid computing; however, the characteristics of the Grid environment make the coordination of its execution very complex [12,13].”
A taxonomy of scientific workflow systems for grid computing (2005, background): “A detailed taxonomy and in depth discussion on its mapping can be found in [23].”
Scheduling Algorithms for Grid Computing: State of the Art and Open Problems (2006, background): “A comprehensive survey of these systems is given in [121], while we continue our focus on their scheduling algorithm components in what follows.”
A market-oriented hierarchical scheduling strategy in cloud workflow systems (2011, methodology): “or language-based modelling tools such as XPDL (XML Process Definition Language) [48].”
Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms (2006, methodology): “workflow management systems [6,8,14,16,19,21,26, 30] with scheduling algorithms have been developed.”
ProvDB: A System for Lifecycle Management of Collaborative Analysis Workflows (2016, background): “systems [1, 2, 12, 13, 13, 14, 29, 33, 35, 36, 54] where the operations are typically treated as black boxes, the provenance can usually be captured only at the level of datasets.”
DAGuE: A Generic Distributed DAG Engine for High Performance Computing (2011, neutral): “[2], [3]”