The role of machine learning in scientific workflows

Deelman, Ewa; Mandal, Anirban; Jiang, Ming; Sakellariou, Rizos

doi:10.1177/1094342019852127

Title: The role of machine learning in scientific workflows

Abstract

Machine learning (ML) is being applied in a number of everyday contexts from image recognition, to natural language processing, to autonomous vehicles, to product recommendation. In the science realm, ML is being used for medical diagnosis, new materials development, smart agriculture, DNA classification, and many others. In this article, we describe the opportunities of using ML in the area of scientific workflow management. Scientific workflows are key to today’s computational science, enabling the definition and execution of complex applications in heterogeneous and often distributed environments. We describe the challenges of composing and executing scientific workflows and identify opportunities for applying ML techniques to meet these challenges by enhancing the current workflow management system capabilities. We foresee that as the ML field progresses, the automation provided by workflow management systems will greatly increase and result in significant improvements in scientific productivity.

Authors:

^[1]; Mandal, Anirban ^[2]; Jiang, Ming ^[3]; Sakellariou, Rizos ^[4]

University of Southern California Information Sciences Institute, Marina Del Rey, CA, USA
Renaissance Computing Institute, Chapel Hill, NC, USA
Lawrence Livermore National Laboratory, Livermore, CA, USA
The University of Manchester, Manchester, UK

Publication Date:: Tue May 21 00:00:00 EDT 2019

Research Org.:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Univ. of Southern California, Marina Del Rey, CA (United States). Information Sciences Inst.

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA); National Science Foundation (NSF); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

OSTI Identifier:: 1523666

Alternate Identifier(s):: OSTI ID: 1548331; OSTI ID: 1600091

Report Number(s):: LLNL-JRNL-765200
Journal ID: ISSN 1094-3420

Grant/Contract Number:: SC0012636; AC52-07NA27344

Resource Type:: Published Article

Journal Name:: International Journal of High Performance Computing Applications

Additional Journal Information:: Journal Name: International Journal of High Performance Computing Applications Journal Volume: 33 Journal Issue: 6; Journal ID: ISSN 1094-3420

Publisher:: SAGE

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; Scientific workflows; machine learning; workflow systems; anomaly detection; workflow composition

Citation Formats


                    Deelman, Ewa, Mandal, Anirban, Jiang, Ming, and Sakellariou, Rizos. The role of machine learning in scientific workflows.  United States: N. p., 2019. 
Web.  doi:10.1177/1094342019852127.

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                    Deelman, Ewa, Mandal, Anirban, Jiang, Ming, & Sakellariou, Rizos. The role of machine learning in scientific workflows.  United States.  https://doi.org/10.1177/1094342019852127

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                    Deelman, Ewa, Mandal, Anirban, Jiang, Ming, and Sakellariou, Rizos. Tue .  
"The role of machine learning in scientific workflows".  United States.  https://doi.org/10.1177/1094342019852127.

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@article{osti_1523666,

  title        = {The role of machine learning in scientific workflows},

  author       = {Deelman, Ewa and Mandal, Anirban and Jiang, Ming and Sakellariou, Rizos},

  abstractNote = {Machine learning (ML) is being applied in a number of everyday contexts from image recognition, to natural language processing, to autonomous vehicles, to product recommendation. In the science realm, ML is being used for medical diagnosis, new materials development, smart agriculture, DNA classification, and many others. In this article, we describe the opportunities of using ML in the area of scientific workflow management. Scientific workflows are key to today’s computational science, enabling the definition and execution of complex applications in heterogeneous and often distributed environments. We describe the challenges of composing and executing scientific workflows and identify opportunities for applying ML techniques to meet these challenges by enhancing the current workflow management system capabilities. We foresee that as the ML field progresses, the automation provided by workflow management systems will greatly increase and result in significant improvements in scientific productivity.},

  doi          = {10.1177/1094342019852127},

  journal      = {International Journal of High Performance Computing Applications},

  number       = 6,

  volume       = 33,

  place        = {United States},

  year         = {Tue May 21 00:00:00 EDT 2019},

  month        = {Tue May 21 00:00:00 EDT 2019}

}

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Title: The role of machine learning in scientific workflows

Abstract

Citation Formats

TRIO: Burst Buffer Based I/O Orchestration conference, September 2015

ASKALON: a Grid application development and computing environment conference, January 2005

Energy-Aware Workflow Scheduling Using Frequency Scaling conference, September 2014

Detecting Abnormal Machine Characteristics in Cloud Infrastructures conference, December 2011

Failure prediction and localization in large scientific workflows conference, January 2011

Intelligent failure prediction models for scientific workflows journal, February 2015

What makes workflows work in an opportunistic environment? journal, January 2006

OSG-GEM: Gene Expression Matrix Construction Using the Open Science Grid journal, January 2016

PANORAMA: An approach to performance modeling and diagnosis of extreme-scale workflows journal, July 2016

Toward an End-to-End Framework for Modeling, Monitoring and Anomaly Detection for Scientific Workflows conference, May 2016

A Survey of Data-Intensive Scientific Workflow Management journal, March 2015

Data Elevator: Low-Contention Data Movement in Hierarchical Storage System conference, December 2016

A Machine Learning Approach for Performance Prediction and Scheduling on Heterogeneous CPUs conference, October 2017

Stacker: An Autonomic Data Movement Engine for Extreme-Scale Data Staging-Based In-Situ Workflows conference, November 2018

Principal component analysis: a review and recent developments journal, April 2016

Lambda architecture for cost-effective batch and speed big data processing conference, October 2015

Optimizing Workflow Data Footprint journal, January 2007

The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud journal, May 2013

Harnessing Data Movement in Virtual Clusters for In-Situ Execution journal, March 2019

Anomaly detection: A survey journal, July 2009

ROSS: A high-performance, low-memory, modular Time Warp system journal, November 2002

A Performance Model to Estimate Execution Time of Scientific Workflows on the Cloud conference, November 2014

A Job Sizing Strategy for High-Throughput Scientific Workflows journal, February 2018

MOHEFT: A multi-objective list-based method for workflow scheduling conference, December 2012

Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences journal, January 2010

Analysis of application heartbeats: Learning structural and temporal features in time series data for identification of performance problems conference, November 2008

Local convergence of the fuzzy c-Means algorithms journal, January 1986

Makeflow: a portable abstraction for data intensive computing on clusters, clouds, and grids conference, January 2012

On the Use of Machine Learning to Predict the Time and Resources Consumed by Applications conference, May 2010

Pegasus, a workflow management system for science automation journal, May 2015

Comparing machine learning classifiers in potential distribution modelling journal, May 2011

A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data journal, April 2016

Data Access for LIGO on the OSG conference, January 2017

A Declarative Optimization Engine for Resource Provisioning of Scientific Workflows in IaaS Clouds conference, January 2015

Discovering cluster-based local outliers journal, June 2003

Algorithms for cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds journal, July 2015

Wings: Intelligent Workflow-Based Design of Computational Experiments journal, January 2011

Performing statistical analyses on quantitative data in Taverna workflows: An example using R and maxdBrowse to identify differentially-expressed genes from microarray data journal, August 2008

Predicting application performance using supervised learning on communication features conference, January 2013

A Pareto-based approach for CPU provisioning of scientific workflows on clouds journal, May 2019

Resource-efficient workflow scheduling in clouds journal, May 2015

In Situ Visualization at Extreme Scale: Challenges and Opportunities journal, November 2009

The future of scientific workflows journal, April 2017

Scheduling Multilevel Deadline-Constrained Scientific Workflows on Clouds Based on Cost Optimization journal, January 2015

Workload-aware anomaly detection for Web applications journal, March 2014

Performance Anomaly Detection and Bottleneck Identification journal, July 2015

Aspen: A domain specific language for performance modeling conference, November 2012

A survey of data provenance in e-science journal, September 2005

How to Track Your Data: The Case for Cloud Computing Provenance conference, November 2011

Sequential Competitive Learning and the Fuzzy c-Means Clustering Algorithms journal, July 1996

TRIO: Burst Buffer Based I/O Orchestration
conference, September 2015

ASKALON: a Grid application development and computing environment
conference, January 2005

Energy-Aware Workflow Scheduling Using Frequency Scaling
conference, September 2014

Detecting Abnormal Machine Characteristics in Cloud Infrastructures
conference, December 2011

Failure prediction and localization in large scientific workflows
conference, January 2011

Intelligent failure prediction models for scientific workflows
journal, February 2015

What makes workflows work in an opportunistic environment?
journal, January 2006

OSG-GEM: Gene Expression Matrix Construction Using the Open Science Grid
journal, January 2016

PANORAMA: An approach to performance modeling and diagnosis of extreme-scale workflows
journal, July 2016

Toward an End-to-End Framework for Modeling, Monitoring and Anomaly Detection for Scientific Workflows
conference, May 2016

A Survey of Data-Intensive Scientific Workflow Management
journal, March 2015

Data Elevator: Low-Contention Data Movement in Hierarchical Storage System
conference, December 2016

A Machine Learning Approach for Performance Prediction and Scheduling on Heterogeneous CPUs
conference, October 2017

Stacker: An Autonomic Data Movement Engine for Extreme-Scale Data Staging-Based In-Situ Workflows
conference, November 2018

Principal component analysis: a review and recent developments
journal, April 2016

Lambda architecture for cost-effective batch and speed big data processing
conference, October 2015

Optimizing Workflow Data Footprint
journal, January 2007

The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud
journal, May 2013

Harnessing Data Movement in Virtual Clusters for In-Situ Execution
journal, March 2019

Anomaly detection: A survey
journal, July 2009

ROSS: A high-performance, low-memory, modular Time Warp system
journal, November 2002

A Performance Model to Estimate Execution Time of Scientific Workflows on the Cloud
conference, November 2014

A Job Sizing Strategy for High-Throughput Scientific Workflows
journal, February 2018

MOHEFT: A multi-objective list-based method for workflow scheduling
conference, December 2012

Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences
journal, January 2010

Analysis of application heartbeats: Learning structural and temporal features in time series data for identification of performance problems
conference, November 2008

Local convergence of the fuzzy c-Means algorithms
journal, January 1986

Makeflow: a portable abstraction for data intensive computing on clusters, clouds, and grids
conference, January 2012

On the Use of Machine Learning to Predict the Time and Resources Consumed by Applications
conference, May 2010

Pegasus, a workflow management system for science automation
journal, May 2015

Comparing machine learning classifiers in potential distribution modelling
journal, May 2011

A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data
journal, April 2016

Data Access for LIGO on the OSG
conference, January 2017

A Declarative Optimization Engine for Resource Provisioning of Scientific Workflows in IaaS Clouds
conference, January 2015

Discovering cluster-based local outliers
journal, June 2003

Algorithms for cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds
journal, July 2015

Wings: Intelligent Workflow-Based Design of Computational Experiments
journal, January 2011

Performing statistical analyses on quantitative data in Taverna workflows: An example using R and maxdBrowse to identify differentially-expressed genes from microarray data
journal, August 2008

Predicting application performance using supervised learning on communication features
conference, January 2013

A Pareto-based approach for CPU provisioning of scientific workflows on clouds
journal, May 2019

Resource-efficient workflow scheduling in clouds
journal, May 2015

In Situ Visualization at Extreme Scale: Challenges and Opportunities
journal, November 2009

The future of scientific workflows
journal, April 2017

Scheduling Multilevel Deadline-Constrained Scientific Workflows on Clouds Based on Cost Optimization
journal, January 2015

Workload-aware anomaly detection for Web applications
journal, March 2014

Performance Anomaly Detection and Bottleneck Identification
journal, July 2015

Aspen: A domain specific language for performance modeling
conference, November 2012

A survey of data provenance in e-science
journal, September 2005

How to Track Your Data: The Case for Cloud Computing Provenance
conference, November 2011

Sequential Competitive Learning and the Fuzzy c-Means Clustering Algorithms
journal, July 1996