Service Function Chains multi-resource orchestration in Virtual Mobile Edge Computing

Next-Generation Networks (NGNs) provide efficient services to improve the overall network performance, especially in Internet of Things (IoT) networks. However, service placement requires a significant cost in terms of multi-resource chaining guarantees. Therefore, the balance between improving network performance and optimizing resources orchestration is still a fundamental issue to assure the benefits of NGNs with optimal resources' utilization. In this paper, we present new approaches for Service Function Chain (SFC) orchestration in Virtual Mobile Edge Computing (VMEC) environments, where IoT devices are used as on-demand virtual edge servers. We propose Optimal SFC Placement in VMEC over AI-IoT (OSPV) algorithm to select the optimal virtual mobile edge (VME) according to heterogeneous computing resources constraints. Moreover, to deal with OSPV complexity issues in dense IoT networks, we propose an Efficient SFC Placement in VMEC over AI-IoT (ESPV) algorithm that selects sufficient VME nodes for services operations and deployments. Furthermore, recent Deep Learning (DL) techniques such LSTM and GRU are implied to predict mobility and energy consumption sequences of IoT devices. These instances introduce feasible sets of IoT nodes, where the optimization algorithms should operate. Results show the efficiency of the DL instances and prove that the prediction awareness prevents the selected VME from failure and disconnection during the communication. Moreover, the proposed optimization algorithms are implemented and evaluated under different computing scenarios. Then, they are compared according to total allocated servers and SFC placement time. Optimization results show that both algorithms are efficient in terms of predetermined key performance indicators compared to the state of the art.