Data-Intensive Technologies for Cloud Computing

2010 ◽  
pp. 83-136 ◽  
Author(s):  
Anthony M. Middleton
Keyword(s):  
Cloud Computing ◽  
Data Intensive

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.

NoSQL Databases

2014 ◽  
pp. 186-215 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Open Source ◽  
Data Storage ◽  
Big Data Processing ◽  
Nosql Databases ◽  
Data Intensive ◽  
Huge Data ◽  
Data Intensive Applications

NoSQL databases are designed to meet the huge data storage requirements of cloud computing and big data processing. NoSQL databases have lots of advanced features in addition to the conventional RDBMS features. Hence, the “NoSQL” databases are popularly known as “Not only SQL” databases. A variety of NoSQL databases having different features to deal with exponentially growing data-intensive applications are available with open source and proprietary option. This chapter discusses some of the popular NoSQL databases and their features on the light of CAP theorem.

Software Asset Re-Use

2012 ◽  
pp. 1-27
Author(s):  
Richard Millham
Keyword(s):  
Cloud Computing ◽  
Software As A Service ◽  
Legacy Systems ◽  
Computing Environment ◽  
Migration Process ◽  
Legacy System ◽  
Cloud Computing Environment ◽  
Data Intensive ◽  
System Migration ◽  
The Web

In this chapter, the author examines the migration process of a legacy system, as a software-as-a-service model, to the Web, and he looks at some of the reasons that drive this legacy system migration. As migration is often a multi-step process, depending on the legacy system being migrated, the author outlines several techniques and transformations for each step of the migration process in order to enable legacy systems, of different types, to be migrated to the cloud. Of particular interest are the different methods to handle data-intensive legacy systems to enable them to function in a cloud computing environment with reduced bandwidth. Unlike the migration of an unstructured legacy system to a locally-distributed desktop system, system migration to a cloud computing environment poses some unique challenges such as restricted bandwidth, scalability, and security. Part of this migration process is adapting the transformed legacy system to be able to function in such an environment. At the end of the chapter, several small case studies of legacy systems, each of a different nature successfully migrated to the cloud, will be given.

The Study of Cloud Computing Data Interaction Platform on the New Type of Rural Cooperative Medical Care System

2014 ◽  
Vol 998-999 ◽  
pp. 1378-1381
Author(s):  
Ru Dan Lin ◽  
Lan Zhen Chen ◽  
Yao Huan Sheng
Keyword(s):  
Cloud Computing ◽  
Medical Care ◽  
Medical Industry ◽  
Medical Data ◽  
Medical Care System ◽  
Data Intensive ◽  
New Type ◽  
Care System

Cloud computing is mainly studied and applied in data-intensive industries. It is rarely seen in the medical industry, though it is the most representative one of data-intensive industries and closely related to people's lives. There is no medical data interaction platform of cloud computing. This paper introduces the framework of cloud computing data interaction platform for the new rural cooperative medical care system (NCSM), which will allow for NCMS data interaction on this interactive platform.

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