COME-UP: Computation Offloading in Mobile Edge Computing with LSTM Based User Direction Prediction

Applied Sciences

Ali Imran Jehangiri¹  Arif Iqbal Umar¹  Sardar Khaliq uz Zaman¹  Tahir Maqsood²  Muhammad Amir Khan²  Mohammad Shorfuzzaman³  Mehedi Masud³  Noor Zaman Jhanjhi⁴ 

[1] Department of Computer Science and Information Technology, Hazara University Mansehra, Mansehra 21300, Pakistan;Department of Computer Science, Abbottabad Campus, COMSATS University Islamabad, Abbottabad 54590, Pakistan;Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;School of Computer Science and Engineering, Taylor’s University, Subang Jaya 47500, Malaysia;

关键词: task offloading;    machine learning;    location prediction;    mobile edge computing;   

DOI  :  10.3390/app12073312

来源: DOAJ

【 摘 要 】

In mobile edge computing (MEC), mobile devices limited to computation and memory resources offload compute-intensive tasks to nearby edge servers. User movement causes frequent handovers in 5G urban networks. The resultant delays in task execution due to unknown user position and base station lead to increased energy consumption and resource wastage. The current MEC offloading solutions separate computation offloading from user mobility. For task offloading, techniques that predict the user’s future location do not consider user direction. We propose a framework termed COME-UP Computation Offloading in mobile edge computing with Long-short term memory (LSTM) based user direction prediction. The nature of the mobility data is nonlinear and leads to a time series prediction problem. The LSTM considers the previous mobility features, such as location, velocity, and direction, as input to a feed-forward mechanism to train the learning model and predict the next location. The proposed architecture also uses a fitness function to calculate priority weights for selecting an optimum edge server for task offloading based on latency, energy, and server load. The simulation results show that the latency and energy consumption of COME-UP are lower than the baseline techniques, while the edge server utilization is enhanced.

【 授权许可】

Unknown