An Intelligent Decision Method for Task Offloading in Fog Computing System

• Main Authors: YEN, CHIA-CHUN, 顏嘉君
• Other Authors: Ku, Cheng-Yuan
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/sprx5c

碩士 === 國立交通大學 === 資訊管理研究所 === 108 === Nowadays, the use of mobile devices has grown rapidly across the world. More and more applications or services are transferred to mobile devices. However, it will result in some problems and limitations. For example, mobile devices’ resources and battery life are not better than normal machines or they cannot execute complex applications. In this situation, it is beneficial for mobile devices to offload computation-intensive tasks to high-level machines. While choosing a suitable time to do task offloading to the cloud/fog, choosing a better place to offload, what portion of the application should be offloading, and offloading appropriately are important issues. Many papers have already analyzed and discussed according to the four aspects. Moreover, in a mobile network system, task offloading always emphasizes execution efficiency and energy consumption and does the trade-off between the two factors. Therefore, we expect to optimize both execution performance and energy consumption. In this paper, we will improve the disadvantage of [1] which just considers the cloud severs and mobile devices’ entire capability regardless of the current task execution status. Therefore, we proposed a more preferable offloading policy based on [1]’s fog computing model and offloading policy considers energy consumption, execution time, other expenses, especially throughput and load of cloud server’s utilization. Finally, we evaluate the performance of our method through simulation compared to [1]. The result from the simulation shows that our proposed method can be more preferable for reality and bring more computation effectiveness and performance to mobile users.