Scheduling technique of data intensive application workflows in Cloud computing

2012 ◽  
Author(s):  
Jignesh Lakhani ◽  
Hitesh Bheda
Keyword(s):  
Cloud Computing ◽  
Data Intensive ◽  
Scheduling Technique ◽  
Data Intensive Application

A Semantics Based Routing Scheme for Cloud Computing

2011 ◽  
Vol 267 ◽  
pp. 1054-1058
Author(s):  
Wu Chu Tang ◽  
Jun Xie
Keyword(s):  
Cloud Computing ◽  
Distributed Data ◽  
Data Intensive ◽  
Routing Scheme ◽  
Multiple Datasets ◽  
Application Model ◽  
Data Intensive Application

Each of tasks within the application depends on multiple datasets that may be distributed anywhere within the Cloud Computing. This paper defines the problem of scheduling distributed data-intensive application on to Gird resource and presents a formal resource and application model for the problem.

Data-intensive application scheduling on Mobile Edge Cloud Computing

2020 ◽  
Vol 167 ◽  
pp. 102735 ◽  
Author(s):  
Mohammad Alkhalaileh ◽  
Rodrigo N. Calheiros ◽  
Quang Vinh Nguyen ◽  
Bahman Javadi
Keyword(s):  
Cloud Computing ◽  
Data Intensive ◽  
Data Intensive Application

Probabilistic State Estimation Based Scheduling Approach for Cloud Computing

2011 ◽  
Vol 55-57 ◽  
pp. 1053-1057
Author(s):  
Gui De Zheng ◽  
Ming Chen
Keyword(s):  
Cloud Computing ◽  
Grid Computing ◽  
State Estimation ◽  
Distributed Data ◽  
Scientific Instruments ◽  
Next Generation ◽  
Data Intensive ◽  
Application Model ◽  
Scientific Experiments ◽  
The World

The next generation of scientific experiments and studies are being carried out by large collaborations of researchers distributed around the world engaged in analysis of huge collections of data generated by scientific instruments. Grid computing has emerged as an enabler for such collaborations as it aids communities in sharing resource to achieve common objective. This paper defines the problem of scheduling distributed data-intensive application on to Gird resource and presents a formal resource and application model for the problem.

scholarly journals GATECloud.net: a platform for large-scale, open-source text processing on the cloud

2013 ◽  
Vol 371 (1983) ◽  
pp. 20120071 ◽  
Author(s):  
Valentin Tablan ◽  
Ian Roberts ◽  
Hamish Cunningham ◽  
Kalina Bontcheva
Keyword(s):  
Cloud Computing ◽  
Language Processing ◽  
Large Scale ◽  
Virtual Machines ◽  
Cost Benefit Analysis ◽  
Text Processing ◽  
Cost Benefit ◽  
Data Intensive ◽  
On Demand ◽  
Usage Evaluation

Cloud computing is increasingly being regarded as a key enabler of the ‘democratization of science’, because on-demand, highly scalable cloud computing facilities enable researchers anywhere to carry out data-intensive experiments. In the context of natural language processing (NLP), algorithms tend to be complex, which makes their parallelization and deployment on cloud platforms a non-trivial task. This study presents a new, unique, cloud-based platform for large-scale NLP research—GATECloud. net. It enables researchers to carry out data-intensive NLP experiments by harnessing the vast, on-demand compute power of the Amazon cloud. Important infrastructural issues are dealt with by the platform, completely transparently for the researcher: load balancing, efficient data upload and storage, deployment on the virtual machines, security and fault tolerance. We also include a cost–benefit analysis and usage evaluation.

Current Trends in Cloud Computing for Data Science Experiments

2021 ◽  
Vol 11 (4) ◽  
pp. 80-99
Author(s):  
Syed Imran Jami ◽  
Siraj Munir
Keyword(s):  
Cloud Computing ◽  
Resource Sharing ◽  
Data Science ◽  
Job Scheduling ◽  
Resource Scheduling ◽  
Data Intensive ◽  
Organizational Issues ◽  
Current Trends ◽  
Recent Trends ◽  
And Storage

Recent trends in data-intensive experiments require extensive computing and storage resources that are now handled using cloud resources. Industry experts and researchers use cloud-based services and resources to get analytics of their data to avoid inter-organizational issues including power overhead on local machines, cost associated with maintaining and running infrastructure, etc. This article provides detailed review of selected metrics for cloud computing according to the requirements of data science and big data that includes (1) load balancing, (2) resource scheduling, (3) resource allocation, (4) resource sharing, and (5) job scheduling. The major contribution of this review is the inclusion of these metrics collectively which is the first attempt towards evaluating the latest systems in the context of data science. The detailed analysis shows that cloud computing needs research in its association with data-intensive experiments with emphasis on the resource scheduling area.

PrEstoCloud

2021 ◽  
Vol 34 (1) ◽  
pp. 66-85
Author(s):  
Yiannis Verginadis ◽  
Dimitris Apostolou ◽  
Salman Taherizadeh ◽  
Ioannis Ledakis ◽  
Gregoris Mentzas ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Software Engineering ◽  
Response Time ◽  
Fog Computing ◽  
Data Sources ◽  
Data Intensive ◽  
Service Response Time ◽  
Enabling Services ◽  
Data Intensive Applications ◽  
Multi Cloud

Fog computing extends multi-cloud computing by enabling services or application functions to be hosted close to their data sources. To take advantage of the capabilities of fog computing, serverless and the function-as-a-service (FaaS) software engineering paradigms allow for the flexible deployment of applications on multi-cloud, fog, and edge resources. This article reviews prominent fog computing frameworks and discusses some of the challenges and requirements of FaaS-enabled applications. Moreover, it proposes a novel framework able to dynamically manage multi-cloud, fog, and edge resources and to deploy data-intensive applications developed using the FaaS paradigm. The proposed framework leverages the FaaS paradigm in a way that improves the average service response time of data-intensive applications by a factor of three regardless of the underlying multi-cloud, fog, and edge resource infrastructure.

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