부산대학교 도서관

Mobile edge computing (MEC) can sufficiently meet the computing demands of complex application consists of multiple interdependent tasks which can be represented by a directed acyclic graph (DAG). For tasks in a DAG, different scheduling orders and offloading decisions will generate different completion time, which further affects the Quality of Experiences (QoEs). So it is important to study the scheduling and offloading schemes for tasks in MEC scenarios. To this end, we first designed a scheme that schedules tasks with the highest response ratio and offloads tasks to the optimal processor with the optimization method for a DAG, which is termed as HRRO algorithm. Then, considering the complexity of the reality, we extended the HRRO to the ultradense MEC system and achieved the optimal joint scheduling and offloading scheme for multi-DAG based on the genetic algorithm, which can be concluded as HRRO based on the genetic algorithm (HRRO-GA). Subsequently, to evaluate the performance of the algorithms, we conducted amounts of the simulation experiments and compared the results with several state-of-the-art algorithms, including distributed earliest finish-time offloading (DEFO), potential game-based offloading algorithm (PGOA), and GA-based multiuser earliest finish time (GA-MEFT). Meanwhile, we selected some random strategies to verify the schemes of HRRO-GA are the best. Finally, we concluded that HRRO-GA is more suitable for the ultradense MEC system.