AUB ScholarWorks

Secure mobile application partitioning in energy-efficient cloud computing

Show simple item record

dc.contributor.author Saab, Salwa Adriana Issam.
dc.date.accessioned 2013-10-02T09:21:44Z
dc.date.available 2013-10-02T09:21:44Z
dc.date.issued 2012
dc.identifier.uri http://hdl.handle.net/10938/9469
dc.description Thesis (M.E.)--American University of Beirut, Department of Electrical and Computer Engineering, 2012.
dc.description Co-Advisor : Dr. Ayman Kayssi, Professor, Electrical and Computer Engineering--Co-Advisor : Dr. Ali Chehab, Associate Professor, Electrical and Computer Engineering--Member of Committee : Dr. Hazem Hajj, Assistant Professor, Electrical and Computer Engineering.
dc.description Includes bibliographical references (leaves 69-71)
dc.description.abstract Many emerging mobile applications nowadays tend to be computation-intensive due to the increasing popularity and convenience of smartphones. Nevertheless, a major obstacle prohibits the direct adoption of such applications and that is battery lifetime. Mobile Cloud Computing (MCC) is a promising solution that suggests the partial processing of applications on the cloud to minimize the overall power consumption at the mobile device. However, this does not necessarily save energy if there is no systematic mechanism for evaluating the effect of offloading the application into the cloud. In this research, we develop a mathematical model that represents this energy consumption optimization problem and extend a minimum-cut based algorithm to dynamically solve it. Furthermore, we propose a proprietary protocol - Free Sequence Protocol (FSP) - that allows the dynamic execution of any application according to its call-graph. Since offloading exposes data to eavesdropping attacks, we extend the model to account for security operations including encryption-decryption and conditional offloading. An existing Android image processing application -ImageProx- was altered according to the FSP protocol and integrated into a complete system that involves a profiler and decision engine. The profiler uses mathematical models developed by intensive experiments on the application itself in addition to Wi-Fi and 3G networks. The power measurements were done using PowerTutor application which reaches a 95percent accuracy of the correct values. Our experimental setup consisted of HTC Nexus One - the official device on which PowerTutor was implemented - and a Java server deployed on Amazon EC2 to act as the cloud. The results demonstrate the effect of workload amount, network type, computation cost of functions, and security operations on the overall power consumption. Moreover, they prove that dynamic partitioning successfully provides energy savings at the mobile phone and supersedes the performance of both permanent local and remote execut
dc.format.extent xiii, 71 leaves : col. ill. ; 30 cm.
dc.language.iso eng
dc.relation.ispartof Theses, Dissertations, and Projects
dc.subject.classification ET:005740 AUBNO
dc.subject.lcsh Cloud computing.
dc.subject.lcsh Energy consumption.
dc.subject.lcsh Network performance (Telecommunication)
dc.subject.lcsh Image processing.
dc.subject.lcsh Cell phones -- Security measures.
dc.title Secure mobile application partitioning in energy-efficient cloud computing
dc.type Thesis
dc.contributor.department American University of Beirut. Faculty of Engineering and Architecture. Department of Electrical and Computer Engineering.


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search AUB ScholarWorks


Browse

My Account