Intelligent Resource Allocations for Software-Defined Mission-Critical IoT燬ervices

Heterogeneous Internet of Things (IoT) applications generate a diversity of novelty applications and services in next-generation networks (NGN), which is essential to guarantee end-to-end (E2E) communication resources for both control plane (CP) and data plane (DP). Likewise, the heterogeneous 5th generation (5G) communication applications, including Mobile Broadband Communications (MBBC), massive Machine-Type Commutation (mMTC), and ultra-reliable low latency communications (URLLC), obligate to perform intelligent Quality-of-Service (QoS) Class Identifier (QCI), while the CP entities will be suffered from the complicated massive HIOT applications. Moreover, the existing management and orchestration (MANO) models are inappropriate for resource utilization and allocation in large-scale and complicated network environments. To cope with the issues mentioned above, this paper presents an adopted software-defined mobile edge computing (SDMEC) with a lightweight machine learning (ML) algorithm, namely support vector machine (SVM), to enable intelligent MANO for real-time and resource-constraints IoT applications which require lightweight computation models. Furthermore, the SVM algorithm plays an essential role in performing QCI classification. Moreover, the software-defined networking (SDN) controller allocates and configures priority resources according to the SVM classification outcomes. Thus, the complementary of SVM and SDMEC conducts intelligent resource MANO for massive QCI environments and meets the perspectives of mission-critical communication with resource constraint applications. Based on the E2E experimentation metrics, the proposed scheme shows remarkable outperformance in key performance indicator (KPI) QoS, including communication reliability, latency, and communication throughput over the various powerful reference methods.