Scheduling Method of Data-Intensive Applications in Cloud Computing Environments
The virtualization of cloud computing improves the utilization of resources and energy. And a cloud user can deploy his/her own applications and related data on a pay-as-you-go basis. The communications between an application and a data storage node, as well as within the application, have a great i...
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2015/605439 |
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doaj-2df3094f90b14423b31bfa6bd9d811b22020-11-24T20:51:46ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/605439605439Scheduling Method of Data-Intensive Applications in Cloud Computing EnvironmentsXiong Fu0Yeliang Cang1Xinxin Zhu2Song Deng3School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaInstitute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaThe virtualization of cloud computing improves the utilization of resources and energy. And a cloud user can deploy his/her own applications and related data on a pay-as-you-go basis. The communications between an application and a data storage node, as well as within the application, have a great impact on the execution efficiency of the application. The locations of subtasks of an application and the data that transferred between the subtasks are the main reason why communication delay exists. The communication delay can affect the completion time of the application. In this paper, we take into account the data transmission time and communications between subtasks and propose a heuristic optimal virtual machine (VM) placement algorithm. Related simulations demonstrate that this algorithm can reduce the completion time of user tasks and ensure the feasibility and effectiveness of the overall network performance of applications when running in a cloud computing environment.http://dx.doi.org/10.1155/2015/605439 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiong Fu Yeliang Cang Xinxin Zhu Song Deng |
spellingShingle |
Xiong Fu Yeliang Cang Xinxin Zhu Song Deng Scheduling Method of Data-Intensive Applications in Cloud Computing Environments Mathematical Problems in Engineering |
author_facet |
Xiong Fu Yeliang Cang Xinxin Zhu Song Deng |
author_sort |
Xiong Fu |
title |
Scheduling Method of Data-Intensive Applications in Cloud Computing Environments |
title_short |
Scheduling Method of Data-Intensive Applications in Cloud Computing Environments |
title_full |
Scheduling Method of Data-Intensive Applications in Cloud Computing Environments |
title_fullStr |
Scheduling Method of Data-Intensive Applications in Cloud Computing Environments |
title_full_unstemmed |
Scheduling Method of Data-Intensive Applications in Cloud Computing Environments |
title_sort |
scheduling method of data-intensive applications in cloud computing environments |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2015-01-01 |
description |
The virtualization of cloud computing improves the utilization of resources and energy. And a cloud user can deploy his/her own applications and related data on a pay-as-you-go basis. The communications between an application and a data storage node, as well as within the application, have a great impact on the execution efficiency of the application. The locations of subtasks of an application and the data that transferred between the subtasks are the main reason why communication delay exists. The communication delay can affect the completion time of the application. In this paper, we take into account the data transmission time and communications between subtasks and propose a heuristic optimal virtual machine (VM) placement algorithm. Related simulations demonstrate that this algorithm can reduce the completion time of user tasks and ensure the feasibility and effectiveness of the overall network performance of applications when running in a cloud computing environment. |
url |
http://dx.doi.org/10.1155/2015/605439 |
work_keys_str_mv |
AT xiongfu schedulingmethodofdataintensiveapplicationsincloudcomputingenvironments AT yeliangcang schedulingmethodofdataintensiveapplicationsincloudcomputingenvironments AT xinxinzhu schedulingmethodofdataintensiveapplicationsincloudcomputingenvironments AT songdeng schedulingmethodofdataintensiveapplicationsincloudcomputingenvironments |
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1716801309660676096 |