Resource Provisioning in Edge Computing for Latency-Sensitive Applications

Low-latency IoT applications, such as autonomous vehicles, augmented/virtual reality devices, and security applications, require high computation resources to make decisions on the fly. However, these kinds of applications cannot tolerate offloading their tasks to be processed on a cloud infrastructure due to the experienced latency. Therefore, edge computing (EC) is introduced to enable low latency by moving the tasks processing closer to the users at the edge of the network. The edge of the network is characterized by the heterogeneity of edge devices (EDs) forming it; thus, it is crucial to devise novel solutions that take into account the different physical resources of each ED. In this article, we propose a resource representation scheme, allowing each ED to expose its resource information to the supervisor of the edge node through the mobile EC application programming interfaces proposed by the European Telecommunications Standards Institute. The information about the ED resource is exposed to the supervisor of the edge node each time a resource allocation is required. To this end, we leverage a Lyapunov optimization framework to dynamically allocate resources at the EDs. To test our proposed model, we performed intensive theoretical and experimental simulations on a testbed to validate the proposed scheme and its impact on different system's parameters. The simulations have shown that our proposed approach outperforms other benchmark approaches and provides low latency and optimal resource consumption.

Automated summary

The article introduces an edge computing solution for low-latency IoT applications, which moves task processing to the network edge to reduce latency, along with proposing a resource representation scheme and dynamic resource allocation framework.