Task offloading optimization mechanism based on deep neural network in edge-cloud environment
Lingkang Meng, Yingjie Wang, Haipeng Wang, Xiangrong Tong, Zice Sun, Zhipeng Cai
Abstract
Abstract With the rise of edge computing technology and the development of intelligent mobile devices, task offloading in the edge-cloud environment has become a research hotspot. Task offloading is also a key research issue in Mobile CrowdSourcing (MCS), where crowd workers collect sensed data through smart devices they carry and offload to edge-cloud servers or perform computing tasks locally. Current researches mainly focus on reducing resource consumption in edge-cloud servers, but fails to consider the conflict between resource consumption and service quality. Therefore, this paper considers the learning generation offloading strategy among multiple Deep Neural Network(DNN), proposed a Deep Neural Network-based Task Offloading Optimization (DTOO) algorithm to obtain an approximate optimal task offloading strategy in the edge-cloud servers to solve the conflict between resource consumption and service quality. In addition, a stack-based offloading strategy is researched. The resource sorting method allocates computing resources reasonably, thereby reducing the probability of task failure. Compared with the existing algorithms, the DTOO algorithm could balance the conflict between resource consumption and service quality in traditional edge-cloud applications on the premise of ensuring a higher task completion rate.