Cost Effective for Erlang Traffic of Mobile Learning Over the Clouds

Cost Effective for Erlang Traffic of Mobile Learning Over the Clouds

Khaing Sandar Htun
Copyright: © 2014 |Pages: 11
DOI: 10.4018/ijesma.2014040101
OnDemand:
(Individual Articles)
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

The progression of technology in this up-to-the-minute era keeps on booming rapidly from time to time (Aljabre, 2012). Cloud computing is the today's latest trend which offers unlimited and flexible shared storage server in a computer system. It can be used by any organizations and institutions. It provides services anytime anywhere. It gives user satisfaction because it enhances efficiency, preserves resource utilization, and improves information sharing. This technology is very beneficial to mobile learners as it helps eliminate problems of distance barrier and the access to education in different geographical locations. Software licensing and manpower training are no longer needed with this latest technology. However real-time applications have constraint with their response time. This paper investigates the cost effective for Erlang traffic of Mobile Learning over the clouds. Throughputs over cost are analyzed. The analysis results that although Google Cloud offers a poor performance but it is the most cost effective compared to the other four clouds.
Article Preview
Top

2. Cloud Architercture

Figure 1 shows the design of different gadgets operating mobile learning accessing data or information from cloud. As different gadgets have their own data rate or speed, the promptness of accessing data or information from cloud could vary. However they all must have equal probability upon accessing into the cloud by computing, every device have the probability to access at 0.20. The probability of accessing data is equal to the number of ways it will occur over the total of outcome.

Figure 1.

Cloud architecture

ijesma.2014040101.f01

Complete Article List

Search this Journal:
Reset
Volume 16: 1 Issue (2024)
Volume 15: 1 Issue (2023)
Volume 14: 4 Issues (2022): 1 Released, 3 Forthcoming
Volume 13: 4 Issues (2021)
Volume 12: 4 Issues (2020)
Volume 11: 4 Issues (2019)
Volume 10: 4 Issues (2018)
Volume 9: 4 Issues (2017)
Volume 8: 4 Issues (2016)
Volume 7: 4 Issues (2015)
Volume 6: 4 Issues (2014)
Volume 5: 4 Issues (2013)
Volume 4: 4 Issues (2012)
Volume 3: 4 Issues (2011)
Volume 2: 4 Issues (2010)
Volume 1: 4 Issues (2009)
View Complete Journal Contents Listing