An Effective Offloading Model Based on Genetic Markov Process for Cloud Mobile Applications

Author(s):  
Mohamed S. Zalat ◽  
Saad M. Darwish ◽  
Magda M. Madbouly
2005 ◽  
Vol 4 (4) ◽  
pp. 1539-1552 ◽  
Author(s):  
Chia-Chin Chong ◽  
Chor-Min Tan ◽  
D.I. Laurenson ◽  
S. McLaughlin ◽  
M.A. Beach ◽  
...  

2019 ◽  
Vol 49 (1) ◽  
pp. 405-423
Author(s):  
Kamil Przybysz

Abstract The paper pertains to matters related to the quantification of functional availability of military vehicles, with reference to exploitation intensity and reliability aspects. The conducted exploitation research paved the way for elaborating methods of determining functional availability for military vehicles, in particular focusing on exploitation intensity and reliability. The essential research was conducted using the developed mathematical model based on the probabilistic, stochastic Markov process, which allowed modelling the process of changes in the exploitation states of military vehicles. In the course of the research, which enabled the authors to estimate the functional availability value, four-layered probes of the military vehicles were used (different types and makes), from the second exploitation phase, with varied mileage from the beginning of the exploitation and average mileage per year.


2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Ana Rosario Espada ◽  
María del Mar Gallardo ◽  
Alberto Salmerón ◽  
Pedro Merino

This paper presents the foundations and the real use of a tool to automatically detect anomalies in Internet traffic produced by mobile applications. In particular, our MVE tool is focused on analyzing the impact that user interactions have on the traffic produced and received by the smartphones. To make the analysis exhaustive with regard to the potential user behaviors, we follow a model-based approach to automatically generate test cases to be executed on the smartphones. In addition, we make use of a specification language to define traffic patterns to be compared with the actual traffic in the device. MVE also includes monitoring and verification support to detect executions that do not fit the patterns. In these cases, the developer will obtain detailed information on the user actions that produce the anomaly in order to improve the application. To validate the approach, the paper presents an experimental study with the well-known Spotify app for Android, in which we detected some interesting behaviors. For instance, some HTTP connections do not end successfully due to timeout errors from the remote Spotify service.


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