scholarly journals Scientific Workflow Systems for 21st Century, New Bottle or New Wine?

2008 ◽  
Author(s):  
Yong Zhao ◽  
Ioan Raicu ◽  
Ian Foster
Keyword(s):  
21St Century ◽  
Scientific Workflow ◽  
Workflow Systems

scholarly journals A taxonomy of scientific workflow systems for grid computing

2005 ◽  
Vol 34 (3) ◽  
pp. 44-49 ◽  
Author(s):  
Jia Yu ◽  
Rajkumar Buyya
Keyword(s):  
Grid Computing ◽  
Scientific Workflow ◽  
Workflow Systems

Data Provenance in Scientific Workflows

2011 ◽  
pp. 46-59
Author(s):  
Khalid Belhajjame ◽  
Paolo Missier ◽  
Carole Goble
Keyword(s):  
Real World ◽  
Scientific Workflow ◽  
Scientific Workflows ◽  
Data Provenance ◽  
Workflow Systems ◽  
Scientific Experiments

Data provenance is key to understanding and interpreting the results of scientific experiments. This chapter introduces and characterises data provenance in scientific workflows using illustrative examples taken from real-world workflows. The characterisation takes the form of a taxonomy that is used for comparing and analysing provenance capabilities supplied by existing scientific workflow systems.

Makespan–Cost–Reliability-Optimized Workflow Scheduling Using Evolutionary Techniques in Clouds

2019 ◽  
Vol 29 (10) ◽  
pp. 2050167
Author(s):  
Xiumin Zhou ◽  
Gongxuan Zhang ◽  
Tian Wang ◽  
Mingyue Zhang ◽  
Xiji Wang ◽  
...  
Keyword(s):  
Quality Of Service ◽  
Energy Consumption ◽  
System Reliability ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Workflow Systems ◽  
Multi Objective ◽  
Qos Parameters ◽  
Crossover And Mutation

Most popular scientific workflow systems can now support the deployment of tasks to the cloud. The execution of workflow on cloud has become a multi-objective scheduling in order to meet the needs of users in many aspects. Cost and makespan are considered to be the two most important objects. In addition to these, there are some other Quality-of-Service (QoS) parameters including system reliability, energy consumption and so on. Here, we focus on three objectives: cost, makespan and system reliability. In this paper, we propose a Multi-objective Evolutionary Algorithm on the Cloud (MEAC). In the algorithm, we design some novel schemes including problem-specific encoding and also evolutionary operations, such as crossover and mutation. Simulations on real-world and random workflows are conducted and the results show that MEAC can get on average about 5% higher hypervolume value than some other workflow scheduling algorithms.

Extending BPEL2.0 for Grid-Based Scientific Workflow Systems

2008 ◽  
Author(s):  
Huajian Zhang ◽  
Xiaoliang Fan ◽  
Ruisheng Zhang ◽  
Jiazao Lin ◽  
Zhili Zhao ◽  
...  
Keyword(s):  
Scientific Workflow ◽  
Workflow Systems ◽  
Grid Based

Do we need to handle every temporal violation in scientific workflow systems?

2014 ◽  
Vol 23 (1) ◽  
pp. 1-34 ◽  
Author(s):  
Xiao Liu ◽  
Yun Yang ◽  
Dong Yuan ◽  
Jinjun Chen
Keyword(s):  
Scientific Workflow ◽  
Workflow Systems

scholarly journals YesWorkflow: A User-Oriented, Language-Independent Tool for Recovering Workflow Information from Scripts

2015 ◽  
Vol 10 (1) ◽  
pp. 298-313 ◽  
Author(s):  
Timothy McPhillips ◽  
Tianhong Song ◽  
Tyler Kolisnik ◽  
Steve Aulenbach ◽  
Khalid Belhajjame ◽  
...  
Keyword(s):  
Workflow Management ◽  
Building Blocks ◽  
Scientific Workflow ◽  
Workflow Engine ◽  
Workflow Model ◽  
Workflow Systems ◽  
Scripting Languages ◽  
High Productivity ◽  
Data Products ◽  
Future Version

Scientific workflow management systems offer features for composing complex computational pipelines from modular building blocks, executing the resulting automated workflows, and recording the provenance of data products resulting from workflow runs. Despite the advantages such features provide, many automated workflows continue to be implemented and executed outside of scientific workflow systems due to the convenience and familiarity of scripting languages (such as Perl, Python, R, and MATLAB), and to the high productivity many scientists experience when using these languages. YesWorkflow is a set of software tools that aim to provide such users of scripting languages with many of the benefits of scientific workflow systems. YesWorkflow requires neither the use of a workflow engine nor the overhead of adapting code to run effectively in such a system. Instead, YesWorkflow enables scientists to annotate existing scripts with special comments that reveal the computational modules and dataflows otherwise implicit in these scripts. YesWorkflow tools extract and analyze these comments, represent the scripts in terms of entities based on the typical scientific workflow model, and provide graphical renderings of this workflow-like view of the scripts. Future version of YesWorkflow will also allow the prospective provenance of the data products of these scripts to be queried in ways similar to those available to users of scientific workflow systems.

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