ExoApp: Performance Evaluation of Data-Intensive Applications on ExoGENI

2013 ◽  
Author(s):  
Ze Yu ◽  
Xinxin Liu ◽  
Min Li ◽  
Kaikai Liu ◽  
Xiaolin Li
Keyword(s):  
Performance Evaluation ◽  
Data Intensive ◽  
Evaluation Of Data ◽  
Data Intensive Applications

Performance Evaluation of Data-Intensive Computing Applications on a Public IaaS Cloud

2014 ◽  
Vol 59 (3) ◽  
pp. 287-307 ◽  
Author(s):  
Roberto R. Expósito ◽  
Guillermo L. Taboada ◽  
Sabela Ramos ◽  
Juan Touriño ◽  
Ramón Doallo
Keyword(s):  
Performance Evaluation ◽  
Data Intensive Computing ◽  
Data Intensive ◽  
Evaluation Of Data

scholarly journals Performance Evaluation of Data Intensive Computing In the Cloud

2014 ◽  
Vol 4 (2) ◽  
pp. 34-47
Author(s):  
Sanjay P. Ahuja ◽  
Bhagavathi Kaza
Keyword(s):  
Data Storage ◽  
Performance Effect ◽  
Data Intensive ◽  
Amazon Ec2 ◽  
Active Research ◽  
Increasing Demand ◽  
Primary Focus ◽  
Data Layers ◽  
Evaluation Of Data ◽  
Data Intensive Applications

Big data is a topic of active research in the cloud community. With increasing demand for data storage in the cloud, study of data-intensive applications is becoming a primary focus. Data-intensive applications involve high CPU usage for processing large volumes of data on the scale of terabytes or petabytes. While some research exists for the performance effect of data intensive applications in the cloud, none of the research compares the Amazon Elastic Compute Cloud (Amazon EC2) and Google Compute Engine (GCE) clouds using multiple benchmarks. This study performs extensive research on the Amazon EC2 and GCE clouds using the TeraSort, MalStone and CreditStone benchmarks on Hadoop and Sector data layers. Data collected for the Amazon EC2 and GCE clouds measure performance as the number of nodes is varied. This study shows that GCE is more efficient for data-intensive applications compared to Amazon EC2.

scholarly journals Performance Evaluation of Data Intensive Computing In the Cloud

2015 ◽  
pp. 1901-1914 ◽  
Author(s):  
Sanjay P. Ahuja ◽  
Bhagavathi Kaza
Keyword(s):  
Data Storage ◽  
Performance Effect ◽  
Data Intensive ◽  
Amazon Ec2 ◽  
Active Research ◽  
Increasing Demand ◽  
Primary Focus ◽  
Data Layers ◽  
Evaluation Of Data ◽  
Data Intensive Applications

Big data is a topic of active research in the cloud community. With increasing demand for data storage in the cloud, study of data-intensive applications is becoming a primary focus. Data-intensive applications involve high CPU usage for processing large volumes of data on the scale of terabytes or petabytes. While some research exists for the performance effect of data intensive applications in the cloud, none of the research compares the Amazon Elastic Compute Cloud (Amazon EC2) and Google Compute Engine (GCE) clouds using multiple benchmarks. This study performs extensive research on the Amazon EC2 and GCE clouds using the TeraSort, MalStone and CreditStone benchmarks on Hadoop and Sector data layers. Data collected for the Amazon EC2 and GCE clouds measure performance as the number of nodes is varied. This study shows that GCE is more efficient for data-intensive applications compared to Amazon EC2.

Performance Evaluation of Data Transmission in Maritime Delay-Tolerant-Networks

2013 ◽  
Vol E96.B (6) ◽  
pp. 1435-1443 ◽  
Author(s):  
Shuang QIN ◽  
Gang FENG ◽  
Wenyi QIN ◽  
Yu GE ◽  
Jaya Shankar PATHMASUNTHARAM
Keyword(s):  
Performance Evaluation ◽  
Data Transmission ◽  
Delay Tolerant Networks ◽  
Delay Tolerant ◽  
Evaluation Of Data

scholarly journals 6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1709
Author(s):  
Agbotiname Lucky Imoize ◽  
Oluwadara Adedeji ◽  
Nistha Tandiya ◽  
Sachin Shetty
Keyword(s):  
Wireless Communication ◽  
Psychological Health ◽  
Future Research ◽  
Agriculture Education ◽  
Social Psychological ◽  
Research Issues ◽  
Data Intensive ◽  
Wireless Communication Network ◽  
Data Intensive Applications

The 5G wireless communication network is currently faced with the challenge of limited data speed exacerbated by the proliferation of billions of data-intensive applications. To address this problem, researchers are developing cutting-edge technologies for the envisioned 6G wireless communication standards to satisfy the escalating wireless services demands. Though some of the candidate technologies in the 5G standards will apply to 6G wireless networks, key disruptive technologies that will guarantee the desired quality of physical experience to achieve ubiquitous wireless connectivity are expected in 6G. This article first provides a foundational background on the evolution of different wireless communication standards to have a proper insight into the vision and requirements of 6G. Second, we provide a panoramic view of the enabling technologies proposed to facilitate 6G and introduce emerging 6G applications such as multi-sensory–extended reality, digital replica, and more. Next, the technology-driven challenges, social, psychological, health and commercialization issues posed to actualizing 6G, and the probable solutions to tackle these challenges are discussed extensively. Additionally, we present new use cases of the 6G technology in agriculture, education, media and entertainment, logistics and transportation, and tourism. Furthermore, we discuss the multi-faceted communication capabilities of 6G that will contribute significantly to global sustainability and how 6G will bring about a dramatic change in the business arena. Finally, we highlight the research trends, open research issues, and key take-away lessons for future research exploration in 6G wireless communication.

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