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Improving performance of mobile cloud applications using application partitioning algorithms - 2016

Improving performance of mobile cloud applications using application partitioning algorithms

Research paper on Improving performance of mobile cloud applications using application partitioning algorithms

Research Area:  Mobile Cloud Computing

Abstract:

Cloud computing is a very important feature in which application resources are provided over the internet as a service to users that discovering its utilization all over. Mobile Cloud Computing (MCC) combines the features of mobile computing and cloud computing for improving performance. Mobile computing and cloud computing technologies has drastically changed the current perspective on distributed computing. In mobile cloud computing, programmer offloads the entire application from mobile devices to computational clouds where applications are partitioned at different levels of granularity for distributed application processing. Application partitioning is used to separate the intensive components of the mobile applications which operate independently in the distributed processing environment. In this paper, we will discuss computational offloading process of resources to cloud and different application partitioning algorithms for offloading in mobile cloud computing. Here, we give a review of application partitioning schemes in mobile cloud computing highlighting important features and key issues in this area. In this paper, we also discuss about challenges in partitioning of elastic application selecting appropriate research domains and exploring technique of distributed application processing in MCC that offering more data reliability.

Keywords:  
Cloud computing
mobile cloud computing
computational offloading
application partitioning algorithm

Author(s) Name:  Dipika S. Diwase

Journal name:  

Conferrence name:  2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC)

Publisher name:  IEEE

DOI:  10.1109/ICGTSPICC.2016.7955362

Volume Information: