Key points are not available for this paper at this time.
With the proliferation of novel Internet of Things (IoT) mobile applications and advanced communication technologies, nowadays we are surrounded by ubiquitous sensors and smart devices. These smart IoT devices generate a large volume of data day and night at the edge of the network, create a huge demand for edge computing resources, and thus, promote the emergence of the multiaccess edge computing (MEC) paradigm. In MEC environments, IoT devices or mobile users are allowed to offload their computational tasks to nearby edge servers to overcome the limitation of local computing resources. Though edge servers could provide low-latency service with high-responsible computing capabilities, they are still facing many challenges posed by the limited hardware resources and diverse offloading requests. However, traditional approaches are usually based on the centralized architecture and batch-processing scheduling mode, which might lead to low efficiency and high communication overhead. Besides, they also lack the consideration of task diversity and priorities, which are crucial in real-world application scenarios. Thus, smart task scheduling and resource provision strategies with a high real-time property are urgently needed for better user experience and higher resource utilization. In this article, we target the online edge IoT task scheduling and resource allocation problem and propose a decentralized approach (DoSRA). The experiments based on real-world edge environments have demonstrated that the proposed approach could achieve at most a 35.34% reduction on the average weighted offloading response time.
Peng et al. (Tue,) studied this question.