Sensitivity analysis of a hierarchical model of mobile cloud computing

Abstract Mobile cloud computing is a new paradigm that uses cloud computing resources to overcome the limitations of mobile computing. Due to its complexity, dependability and performance studies of mobile clouds may require composite modeling techniques, using distinct models for each subsystem and combining state-based and non-state-based formalisms. This paper uses hierarchical modeling and four different sensitivity analysis techniques to determine the parameters that cause the greatest impact on the availability of a mobile cloud. The results show that distinct approaches provide similar results regarding the sensitivity ranking, with specific exceptions. A combined evaluation indicates that system availability may be improved effectively by focusing on a reduced set of factors that produce large variation on the measure of interest. The time needed to replace a fully discharged battery in the mobile device is a parameter with high impact on steady-state availability, as well as the coverage factor for the failures of some cloud servers. This paper also shows that a sensitivity analysis through partial derivatives may not capture the real level of impact for some parameters in a discrete domain, such as the number of active servers. The analysis through percentage differences, or the factorial design of experiments, fulfills such a gap.

[1]  D. Hamby.  A review of techniques for parameter sensitivity analysis of environmental models , 1994, Environmental monitoring and assessment.

[2]  Kishor S. Trivedi,et al.  GSPN Models: Sensitivity Analysis and Applications , 1990 .

[3]  Stephen A. McGuire,et al.  Introductory Statistics , 2007, Technometrics.

[4]  Arun Venkataramani,et al.  Energy consumption in mobile phones: a measurement study and implications for network applications , 2009, IMC '09.

[5]  Bianca Schroeder,et al.  Disk Failures in the Real World: What Does an MTTF of 1, 000, 000 Hours Mean to You? , 2007, FAST.

[6]  Hongsheng Xi,et al.  Sensitivity analysis and estimates of the performance for M/G/1 queueing systems , 2007, Perform. Evaluation.

[7]  Ronan Farrell,et al.  Value-Chain Engineering of a Tower-Top Cellular Base Station System , 2007, 2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring.

[8]  Kishor S. Trivedi,et al.  Modeling High Availability , 2006, 2006 12th Pacific Rim International Symposium on Dependable Computing (PRDC'06).

[9]  Alec Wolman,et al.  MAUI: making smartphones last longer with code offload , 2010, MobiSys '10.

[10]  A. S. Gundale.  Demystifying Mobile Cloud Computing using Pandaboard , 2012 .

[11]  Haïscam Abdallah,et al.  On the sensitivity analysis of the expected accumulated reward , 2002, Perform. Evaluation.

[12]  Chonho Lee,et al.  A survey of mobile cloud computing: architecture, applications, and approaches , 2013, Wirel. Commun. Mob. Comput..

[13]  Priya. A. Kotwal,et al.  Evolution and effects of mobile cloud computing, middleware services on cloud, future prospects: A peek into the mobile cloud operating systems , 2012, 2012 IEEE International Conference on Computational Intelligence and Computing Research.

[14]  Jin B. Hong,et al.  Availability Modeling and Analysis of a Virtualized System , 2009, 2009 15th IEEE Pacific Rim International Symposium on Dependable Computing.

[15]  Rajkumar Buyya,et al.  A Review on Distributed Application Processing Frameworks in Smart Mobile Devices for Mobile Cloud Computing , 2013, IEEE Communications Surveys & Tutorials.

[16]  Kishor S. Trivedi,et al.  GSPM models: sensitivity analysis and applications , 1990, ACM-SE 28.

[17]  Kishor S. Trivedi,et al.  Sensitivity analysis of reliability and performability measures for multiprocessor systems , 1988, SIGMETRICS '88.

[18]  Raj Jain,et al.  The Art of Computer Systems Performance Analysis : Tech-niques for Experimental Design , 1991 .

[19]  Kishor S. Trivedi.  Probability and Statistics with Reliability, Queuing, and Computer Science Applications , 1984 .

[20]  Joanne Bechta Dugan,et al.  Approximate sensitivity analysis for acyclic Markov reliability models , 2003, IEEE Trans. Reliab..

[21]  Sumit Soni,et al.  A survey of mobile cloud computing architecture, applications, approaches & Current Solution Providers , 2015 .

[22]  Paramvir Bahl,et al.  The Case for VM-Based Cloudlets in Mobile Computing , 2009, IEEE Pervasive Computing.

[23]  Mazliza Othman,et al.  A Survey of Mobile Cloud Computing Application Models , 2014, IEEE Communications Surveys & Tutorials.

[24]  Paulo Romero Martins Maciel,et al.  Availability and Energy Consumption Analysis of Mobile Cloud Environments , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[25]  Kishor S. Trivedi,et al.  Sensitivity analysis of reliability and performability measures for multiprocessor systems , 1988, SIGMETRICS 1988.

[26]  P. Maciel,et al.  Models for Dependability Analysis of Cloud Computing Architectures for Eucalyptus Platform , 2013 .

[27]  Han Qi,et al.  Research on mobile cloud computing: Review, trend and perspectives , 2012, 2012 Second International Conference on Digital Information and Communication Technology and it's Applications (DICTAP).

[28]  M. Eslami,et al.  Introduction to System Sensitivity Theory , 1980, IEEE Transactions on Systems, Man, and Cybernetics.

[29]  Bofeng Zhang,et al.  Comparison of Several Cloud Computing Platforms , 2009, 2009 Second International Symposium on Information Science and Engineering.

[30]  Raj Jain,et al.  The art of computer systems performance analysis - techniques for experimental design, measurement, simulation, and modeling , 1991, Wiley professional computing.

[31]  Pankaj Sareen,et al.  Cloud Computing: Types, Architecture, Applications, Concerns, Virtualization and Role of IT Governance in Cloud , 2013 .