HOSTED SCIENCE: MANAGING COMPUTATIONAL WORKFLOWS IN THE CLOUD

Author:

DEELMAN EWA1,JUVE GIDEON1,MALAWSKI MACIEJ2,NABRZYSKI JAREK3

Affiliation:

1. USC Information Sciences Institute, Marina del Rey, CA, USA

2. AGH University of Science and Technology, Dept. of Computer Science, Krakow, Poland

3. University of Notre Dame, Center for Research Computing, Notre Dame, IN, USA

Abstract

Scientists today are exploring the use of new tools and computing platforms to do their science. They are using workflow management tools to describe and manage complex applications and are evaluating the features and performance of clouds to see if they meet their computational needs. Although today, hosting is limited to providing virtual resources and simple services, one can imagine that in the future entire scientific analyses will be hosted for the user. The latter would specify the desired analysis, the timeframe of the computation, and the available budget. Hosted services would then deliver the desired results within the provided constraints. This paper describes current work on managing scientific applications on the cloud, focusing on workflow management and related data management issues. Frequently, applications are not represented by single workflows but rather as sets of related workflowsworkflow ensembles. Thus, hosted services need to be able to manage entire workflow ensembles, evaluating tradeoffs between completing as many high-value ensemble members as possible and delivering results within a certain time and budget. This paper gives an overview of existing hosted science issues, presents the current state of the art on resource provisioning that can support it, as well as outlines future research directions in this field.

Publisher

World Scientific Pub Co Pte Lt

Subject

Hardware and Architecture,Theoretical Computer Science,Software

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Hierarchical Recursive Resource Sharing for Containerized Applications;Service-Oriented Computing;2018

2. Workflow performance improvement using model-based scheduling over multiple clusters and clouds;Future Generation Computer Systems;2016-01

3. Adapting Scientific Workflows on Networked Clouds Using Proactive Introspection;2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC);2015-12

4. Utilization of workflow management system for virtual machine instance management on cloud;Concurrency and Computation: Practice and Experience;2015-07-24

5. Achieving Performance Isolation on Multi-Tenant Networked Clouds Using Advanced Block Storage Mechanisms;Proceedings of the 6th Workshop on Scientific Cloud Computing;2015-06-16

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