Comparison Between Cloud and Grid Computing: Review Paper

Alwada'n, Tariq; Alhakami, Hosam; Aldabbas, Hamza

doi:10.5121/ijccsa.2012.2401

Cited by 18 publications

(9 citation statements)

References 14 publications

(19 reference statements)

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“…In contrast, Grids have a number of these high level services for instant data transfer and metadata search [8]. In terms of efficiency, IR in a distributed environment is better than a parallel IR.…”

Section: Efficiencymentioning

confidence: 99%

Information Retrieval: Today and Tomorrow

Kumari¹,

Pundhir²,

Kaur³

2015

IJCA

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World wide web (WWW), also known as web has become one of the most valuable resources of Information Retrieval (IR) and Knowledge Discovery. Due to the increasing amount of data available on the Internet, the effective information retrieval requires an efficient framework that can provide a communication between clients as well as for servers. Information Retrieval involves the searching and retrieval of knowledge from large database collections. In this paper, the analysis and comparison of the IR techniques is done. The analysis is based on the Information Retrieval in distributed environments, grid and the cloud environments. Different crawling techniques in these environments are also discussed in this paper.

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Section: Efficiencymentioning

confidence: 99%

Information Retrieval: Today and Tomorrow

Kumari¹,

Pundhir²,

Kaur³

2015

IJCA

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“…Customer can use storage space, processing capabilities, servers, operating system and application development environments. The user can scale up and down the resources in an instant (timely) and on-demand manner in the cloud [2].Cloud computing allows the systems and users to use Platform as a Service (PaaS), Infrastructure as a Service (IaaS), Software as a Service (SaaS) [3]. Based on resource ownership, Cloud computing comes in three forms: public clouds, private clouds and hybrid clouds [4].…”

Section: Introductionmentioning

confidence: 99%

A Sensor Cloud Based Traffic Control System Using War State Battle Field

Kumar¹,

Kaur²

2016

IJSIP

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“…Cloud computing platforms like the commercial Amazon Elastic Compute Cloud [10] or the academic FutureGrid [11] provide rentable computational and storage resources over the Internet. Despite the advantages and disadvantages of clusters, grids and clouds [30], the execution of scientific workflows deals with different challenges depending on the chosen computational platform.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of a Campus Cluster and Open Science Grid Platforms for Protein-Guided Assembly Using Pegasus Workflow Management System

Pavlovikj

Begcy

Behera

et al. 2014

2014 IEEE International Parallel &Amp; Distributed Processing Symposium Workshops

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Abstract-Scientific workflows are a useful tool for managing large and complex computational tasks. Due to its intensive resource requirements, the scientific workflows are often executed on distributed platforms, including campus clusters, grids and clouds. In this paper we build a scientific workflow for blast2cap3, the protein-guided assembly, using the Pegasus Workflow Management System (Pegasus WMS). The modularity of blast2cap3 allows us to decompose the existing serial approach on multiple tasks, some of which can be run in parallel. Afterwards, this workflow is deployed on two distributed execution platforms: Sandhills, the University of Nebraska Campus Cluster, and the Open Science Grid (OSG). We compare and evaluate the performance of the built workflow for the both platforms. Furthermore, we also investigate the influence of the number of clusters of transcripts in the blast2cap3 workflow over the total running time. The performed experiments show that the Pegasus WMS implementation of blast2cap3 significantly reduces the running time compared to the current serial implementation of blast2cap3 for more than 95 %. Although OSG provides more computational resources than Sandhills, our workflow experimental runs have better running time on Sandhills. Moreover, the selection of 300 clusters of transcripts gives the optimum performance with the resources allocated from Sandhills.

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Comparison Between Cloud and Grid Computing: Review Paper

Abstract: ABSTRAC

Cited by 18 publications

References 14 publications

Information Retrieval: Today and Tomorrow

Information Retrieval: Today and Tomorrow

A Sensor Cloud Based Traffic Control System Using War State Battle Field

A Comparison of a Campus Cluster and Open Science Grid Platforms for Protein-Guided Assembly Using Pegasus Workflow Management System

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