Q-IoT: QoS-Aware Multilayer Service Architecture for Multiclass IoT Data Traffic Management

In the realm of Internet of Things (IoT) architectures where diversified services exist, latency related network challenges remain as critical problems in cloud-based systems. To address these challenges, fog computing has emerged as a viable solution for Quality of Service (QoS) oriented design. Leveraging heterogeneous services with different requirements, fog layer transmits decision outputs with traffic patterns composed of multipriority data packets. To fully harness the benefits of fog computing, it is crucial to model such data traffic and devise an efficient scheduling approach for its management. Additionally, employing effective management of cloud resources for handling incoming data traffic is pivotal to enhance the design of latency-based QoS aware services. In this article, we present a fog enabled service architecture and its novel IoT data traffic management mechanism for the mitigation of latency-based QoS problems. To this end, we first elaborate on the service components and data traffic characteristics of the architecture with an in-depth analysis of arrival-service model to give insights into the starvation issue in multipriority scheduling. We present a complex event processing (CEP)-based scheduling approach for handling multipriority IoT data traffic with QoS perspective and a dynamic resource scaling mechanism for handling decision data traffic on the cloud. Finally, we discuss the test results obtained from implementation of the proposed policies and services in a real test-bed environment with two IoT use-cases. The results reveal that the proposed architecture significantly improves the wait time-based QoS requirements and resource utilization compared to the baseline system.