Cost optimization strategy for dependent task offloading in vehicular edge computing

Abstract

The geometrically increasing computational demands strain vehicular systems. Vehicular edge computing effectively alleviate this by dividing tasks into sub-modules and offloading these modules to edge servers. However, the interdependence among subtasks makes task offloading and resource allocation highly challenging. To address this issue, we propose a cost optimization strategy for dependent task offloading in vehicular edge computing networks. Specifically, the task offloading process is divided into two sub-problems: task offloading and resource allocation. First, we propose a Sequenced Quantization based on the Recurrent Neural Network (SQ-RNN) algorithm for offloading decisions. This algorithm uses environmental information as the input of the RNN to generate an optimal task offloading strategy. which is then quantified into multiple binary offloading actions through an order-preserving quantization method. Then, we propose a resource allocation method based on Computing Resource Blocks (CRBs), which divides server resources into blocks and assigns them to tasks with the principle of balancing resource allocation and reducing costs. Finally, extensive simulation experiments conducted on real-world datasets demonstrate that our approach reduces computing delay by 15.27% computing energy consumption by 9.93%, and cost by approximately 10.16% on average within the experimental bandwidth range, compared to the baseline algorithm. Moreover, as the number of subtasks increases, the optimization effect becomes more pronounced.