A Cloud-Based Interactive Application Service

Markatchev, Nayden; Curry, Roger; Kiddle, Cameron; Mirtchovski, Andrey; Simmonds, Rob; Tan, Tingxi

doi:10.1109/e-science.2009.23

Cited by 17 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cloud computing techniques have been applied to various eScience applications. We have surveyed a lot of eScience papers and summarized them in the following four categories based on their areas of expertise: Life sciences [51,56], Physical sciences [28,37], Climate and Earth sciences [32,43] as well as Social sciences and Humanities [23,49].…”

Section: Applicationmentioning

confidence: 99%

eScience and Big Data Workflows in Clouds

Zhou¹,

He²,

Ibrahim³

2016

Big Data

View full text Add to dashboard Cite

Cloud computing has evolved as a popular computing infrastructure for many applications. With (big) data acquiring a crucial role in eScience, efforts have been made recently exploring how to efficiently develop and deploy scientific applications on the unprecedentedly scalable cloud infrastructures. We review recent efforts in developing and deploying scientific computing applications in the cloud. In particular, we introduce a taxonomy specifically designed for scientific computing in the cloud, and further review the taxonomy with four major kinds of science applications, including life sciences, physics sciences, social and humanities sciences, and climate and earth sciences. Due to the large data size in most scientific applications, the performance of I/O operations can greatly affect the overall performance of the applications. We notice that, the dynamic I/O performance of the cloud has made the resource provisioning an important and complex problem for scientific applications in the cloud. We present our efforts on improving the resource provisioning efficiency and effectiveness of scientific applications in the cloud. Finally, we present the open problems for developing the next-generation eScience applications and systems in the cloud and conclude this chapter.

show abstract

Section: Applicationmentioning

confidence: 99%

eScience and Big Data Workflows in Clouds

Zhou¹,

He²,

Ibrahim³

2016

Big Data

View full text Add to dashboard Cite

show abstract

“…Cloud computing techniques have been applied to various eScience applications. We have surveyed a lot of eScience papers and summarized them in the following four categories based on their areas of expertise: Life sciences [4], [14], Physical sciences [38], [27], Climate and Earth sciences [32], [6] as well as Social sciences and Humanities [39], [40].…”

Section: Applicationmentioning

confidence: 99%

A Taxonomy and Survey on eScience as a Service in the Cloud

Zhou,

He,

Ibrahim

2014

Preprint

View full text Add to dashboard Cite

Cloud computing has recently evolved as a popular computing infrastructure for many applications. Scientific computing, which was mainly hosted in private clusters and grids, has started to migrate development and deployment to the public cloud environment. eScience as a service becomes an emerging and promising direction for science computing. We review recent efforts in developing and deploying scientific computing applications in the cloud. In particular, we introduce a taxonomy specifically designed for scientific computing in the cloud, and further review the taxonomy with four major kinds of science applications, including life sciences, physics sciences, social and humanities sciences, and climate and earth sciences. Our major finding is that, despite existing efforts in developing cloud-based eScience, eScience still has a long way to go to fully unlock the power of cloud computing paradigm. Therefore, we present the challenges and opportunities in the future development of cloud-based eScience services, and call for collaborations and innovations from both the scientific and computer system communities to address those challenges.

show abstract

“…Most researches [16] focus on how to take the advantage of Hadoop for processing sequence data. There are a few publications [17,18] discussing the development of a generic workflow with Grid/Cloud resources. But all of them manage workflows with their science workflow engines without using Hadoop streaming.…”

Section: Related Workmentioning

confidence: 99%

GreenPipe: A Hadoop Based Workflow System on Energy-efficient Clouds

Mao

Zhang

et al. 2012

2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops &Amp; PhD Forum

View full text Add to dashboard Cite

Cloud computing is increasingly becoming a popular solution to massive data analysis in bioinformatics. In order to enable scientists to harness the computing power provided by Cloud platforms, we designed GreenPipe, a scalable computational workflow system, which runs jobs as MapReduce tasks on virtual Hadoop clusters. This paper introduces a poweraware scheduling algorithm in the workflow engine to optimize workflow execution in terms of running time and energy consumption.Experimental results demonstrate the performance improvement in GreenPipe.

show abstract

A Cloud-Based Interactive Application Service

Cited by 17 publications

References 4 publications

eScience and Big Data Workflows in Clouds

eScience and Big Data Workflows in Clouds

A Taxonomy and Survey on eScience as a Service in the Cloud

GreenPipe: A Hadoop Based Workflow System on Energy-efficient Clouds

Contact Info

Product

Resources

About