scholarly journals An Effective Techniques Using Apriori and Logistic Methods in Cloud Computing

2021 ◽  
Vol 11 (2) ◽  
pp. 35-39
Author(s):  
S. Selvam

This paper presents a creativity data prefetching scheme on the loading servers in distributed file systems for cloud computing. The server will get and piggybacked the frequent data from the client system, after analyzing the fetched data is forward to the client machine from the server. To place this technique to work, the data about client nodes is piggybacked onto the real client I/O requests, and then forwarded to the relevant storage server. Next, dual prediction algorithms have been proposed to calculation future block access operations for directing what data should be fetched on storage servers in advance. Finally, the prefetching data can be pressed to the relevant client device from the storage server. Over a series of evaluation experiments with a group of application benchmarks, we have demonstrated that our presented initiative prefetching technique can benefit distributed file systems for cloud environments to achieve better I/O performance. In particular, configuration-limited client machines in the cloud are not answerable for predicting I/O access operations, which can certainly contribute to preferable system performance on them.

2017 ◽  
Vol 5 (3) ◽  
pp. 550-562 ◽  
Author(s):  
Jianwei Liao ◽  
Francois Trahay ◽  
Guoqiang Xiao ◽  
Li Li ◽  
Yutaka Ishikawa

2018 ◽  
Vol 16 (2) ◽  
pp. 299-316 ◽  
Author(s):  
Jianwei Liao ◽  
Dong Yin ◽  
Xiaoning Peng

2018 ◽  
Vol 7 (4.15) ◽  
pp. 16
Author(s):  
Mohammed Fakherldin ◽  
Ibrahim Aaker Targio Hashem ◽  
Abdullah Alzuabi ◽  
Faiz Alotaibi

Recent trends in big data have shown that the amount of data continues to increase at an exponential rate. This trend has inspired many researchers over the past few years to explore new research direction of studies related to multiple areas in big data. Hadoop is one of the most popular platforms for big data, thus, Hadoop MapReduce is used to store data in Hadoop distributed file systems. While, cloud computing is considered an excellent candidate for storing and processing the big data. However, processing big data across multiple nodes is a challenging task. The problem is even more complex using virtualized clusters in a cloud computing to execute a large number of tasks. This paper provides a review and analysis of the impact of using physical versus cloud cluster in the processing a large amount of data. This analysis has an impact on the processing in terms of execution time and cost of using each one of them. The result indicates that the use of cloud virtual machines helped better utilize the resources of the host computer. 


Author(s):  
Sai Wu ◽  
Gang Chen ◽  
Xianke Zhou ◽  
Zhenjie Zhang ◽  
Anthony K. H. Tung ◽  
...  

2013 ◽  
Vol 49 (6) ◽  
pp. 2645-2652 ◽  
Author(s):  
Zhipeng Tan ◽  
Wei Zhou ◽  
Dan Feng ◽  
Wenhua Zhang

Sign in / Sign up

Export Citation Format

Share Document