TY  - BOOK
AU  - Christina William Danial Michael
AU  - Imane Aly Saroit Ismail , 
AU  - Shaimaa Mosaad Mohamed , 
TI  - Improving offloading algorithm in mobile coud computing system / 
PY  - 2020///
CY  - Cairo : 
PB  - Christina William Danial Michael , 
KW  - Ant Colony Optimization Algorithm
KW  - Cloud Computing
KW  - Mobile Cloud Computing
N1  - Thesis (M.Sc.) - Cairo University - Faculty of Computer and Artificial Intelligence - Department of Information Technology; Issued also as CD
N2  - Mobile Cloud Computing is a computing paradigm that helps to reduce the application energy consumption, so it increases the battery life. A Mobile application is divided into fine-grained tasks with sequential and parallel topology. Offloading application tasks to a cloud provides more energy but increases the completion time. The scheduling of tasks between executing in a mobile device and cloud is more important to limit the increase in the completion time. The aim of this research is to develop an algorithm that reduces the energy consumed by mobile devices then increasing the battery life. An offloading improvement is the main objective of this thesis. In this thesis, the Energy-efficient Ant Colony cloud Offloading algorithm (EACO) and Energy-efficient Ant System cloud Offloading algorithm (EASO) are developed to reduce the energy consumption with the hard condition of completion time. The optimal values of the ant colony optimization algorithms are determined in this thesis.Experiments are conducted to verify the efficiency of the algorithm using different tasks input data and computation workload. The parameters of the ant colony optimization are as follows: Ü=(3-150), Ý=(3-50), Þ= (1-200), mo=0.9, u=0.1, number of ants=34 and number of iterations=50.EACO decreases the energy by an average of 24%-59% with an increase in completion time by 3.6%- 28% compared with the previous work of liu et al. [7]. According to the mobile execution, EACO reduces 80 % and EASO reduces 70% of the consumed energy
ER  -