Tensor-Based Lyapunov Deep Neural Networks Offloading Control Strategy with Cloud-Fog-Edge Orchestration

Abstract

Using DNN (Deep Neural Networks) models to obtain high Quality of Services (QoS) through the cloud has become increasingly popular nowadays. Users want to use DNN by their edges (such as smartphones) anytime and anywhere. For most small and medium-sized enterprises, cloud computing resources are limited. Temporary exhaustion of resources may cause obvious service delay. For users, if all tasks are done locally, the battery capacity of the edge is too small to support such huge computing tasks. To remove this contradiction, we propose a Tensor-based Lyapunov DNN Offloading Control (TLDOC) strategy. First, we offload DNN computational tasks to cloud-fog-edge from an overall perspective. That is, layers in DNN are considered as basic offloadable objects. Second, we provide an original tensor-based Lyapunov equation and the entire process is derived in the tensor space. Lastly, we consider more key limiting factors (e.g., cellular data and remaining edge energy) to achieve better QoS. Via above contributions, our strategy reduces service delay and energy consumption for DNN cloud-fog-edge orchestration. Our experiments include two parts - detail and comparison. The experimental detail verifies that TLDOC strategy is practical and stable. Compared experiments show that our strategy could provide better QoS than existing methods on efficiency and energy saving.