[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1109/CLOUD.2012.97guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
Article

A Framework for Partitioning and Execution of Data Stream Applications in Mobile Cloud Computing

Published: 24 June 2012 Publication History

Abstract

The advances in technologies of cloud computing and mobile computing enable the newly emerging mobile cloud computing paradigm. Three approaches have been proposed for mobile cloud applications: 1) extending the access to cloud services to mobile devices; 2) enabling mobile devices to work collaboratively as cloud resource providers; 3) augmenting the execution of mobile applications on portable devices using cloud resources. In this paper, we focus on the third approach in supporting mobile data stream applications. More specifically, we study the computation partitioning, which aims at optimizing the partition of a data stream application between mobile and cloud such that the application has maximum speed/throughput in processing the streaming data. To the best of our knowledge, it is the first work to study the partitioning problem for mobile data stream applications, where the optimization is placed on achieving high throughput of processing the streaming data rather than minimizing the make span of executions in other applications. We first propose a framework to provide runtime support for the dynamic partitioning and execution of the application. Different from existing works, the framework not only allows the dynamic partitioning for a single user but also supports the sharing of computation instances among multiple users in the cloud to achieve efficient utilization of the underlying cloud resources. Meanwhile, the framework has better scalability because it is designed on the elastic cloud fabrics. Based on the framework, we design a genetic algorithm to perform the optimal partition. We have conducted extensive simulations. The results show that our method can achieve more than 2X better performance over the execution without partitioning.

Cited By

View all
  1. A Framework for Partitioning and Execution of Data Stream Applications in Mobile Cloud Computing

      Recommendations

      Comments

      Please enable JavaScript to view thecomments powered by Disqus.

      Information & Contributors

      Information

      Published In

      cover image Guide Proceedings
      CLOUD '12: Proceedings of the 2012 IEEE Fifth International Conference on Cloud Computing
      June 2012
      1009 pages
      ISBN:9780769547558

      Publisher

      IEEE Computer Society

      United States

      Publication History

      Published: 24 June 2012

      Author Tags

      1. application partitioning
      2. genetic algorithm
      3. mobile cloud computing

      Qualifiers

      • Article

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)0
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 07 Jan 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2019)DECOProceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing10.1145/3323679.3326509(111-120)Online publication date: 2-Jul-2019
      • (2018)Mobile Computation BurstingProceedings of the 19th International Conference on Distributed Computing and Networking10.1145/3154273.3154299(1-10)Online publication date: 4-Jan-2018
      • (2018)Distributed Scheduling of Event Analytics across Edge and CloudACM Transactions on Cyber-Physical Systems10.1145/31402562:4(1-28)Online publication date: 5-Jul-2018
      • (2018)An Efficient Computation Offloading Management Scheme in the Densely Deployed Small Cell Networks With Mobile Edge ComputingIEEE/ACM Transactions on Networking10.1109/TNET.2018.287300226:6(2651-2664)Online publication date: 1-Dec-2018
      • (2018)A quick-response framework for multi-user computation offloading in mobile cloud computingFuture Generation Computer Systems10.1016/j.future.2017.10.03481:C(166-176)Online publication date: 1-Apr-2018
      • (2015)Time and Energy Saving through Computation Offloading with Bandwidth Consideration for Mobile Cloud ComputingProceedings of the Third International Symposium on Women in Computing and Informatics10.1145/2791405.2791527(527-532)Online publication date: 10-Aug-2015
      • (2015)Application-level task execution issues in mobile cloud computingProceedings of the 30th Annual ACM Symposium on Applied Computing10.1145/2695664.2695965(2285-2287)Online publication date: 13-Apr-2015
      • (2015)Seamless application execution in mobile cloud computingJournal of Network and Computer Applications10.1016/j.jnca.2015.03.00152:C(154-172)Online publication date: 1-Jun-2015
      • (2015)Application optimization in mobile cloud computingJournal of Network and Computer Applications10.1016/j.jnca.2015.02.00352:C(52-68)Online publication date: 1-Jun-2015
      • (2015)Application partitioning algorithms in mobile cloud computingJournal of Network and Computer Applications10.1016/j.jnca.2014.09.00948:C(99-117)Online publication date: 1-Feb-2015
      • Show More Cited By

      View Options

      View options

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media