Deep learning-based workload prediction and resource provisioning for mobile edge-cloud computing in healthcare applications

Abstract

Edge computing has been greatly assisted by the quick development of cloud computing and mobile communications. Even though there has been a lot of interest in edge computing technologies, the majority of research has been application-specific and did not consider cloud providers' control perspective, which offers general-purpose edge services. Thus, a new model called Parallel Convolutional MobileNet (PConvM-Net) is presented for resource provisioning and workload prediction. First, Multi-Access Edge Computing (MEC) for resource provision is considered, and here resource provisioning manager includes two main components, like workload estimation and monitoring. In the prediction module, the workload prediction is performed by employing a Gated Recurrent Unit (GRU). In the decision module, the threshold scale-up process is executed. Moreover, in order to choose the number of resources in the scale-down and scale-up process, a Parallel Convolutional MobileNet (PConvM-Net) is utilized. Further, the decision is considered based on parameters such as bandwidth, Central Processing Unit (CPU), memory usage, energy, and execution time. Here, PConvM-Net is formulated by the amalgamation of MobileNet and Parallel Convolutional Neural Network (PCNN). The simulation outcomes of PConvM-Net calculated a minimum execution time, energy consumption, CPU utilization, Task Response Time, SLA Violation, and Availability of 8.616 sec, 39.876 J, 83.877 %, 7.644 sec, 2.877 %, and 91.876 %.