A framework for partitioning and execution of data stream applications in mobile cloud computing

Lei Yang, Jiannong Cao, Shaojie Tang, Tao Li, Alvin T.S. Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

96 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationProceedings - 2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012
Pages794-802
Number of pages9
DOIs
Publication statusPublished - 2 Oct 2012
Event2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012 - Honolulu, HI, United States
Duration: 24 Jun 201229 Jun 2012

Conference

Conference2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012
CountryUnited States
CityHonolulu, HI
Period24/06/1229/06/12

Keywords

  • application partitioning
  • genetic algorithm
  • mobile cloud computing

ASJC Scopus subject areas

  • Software

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