Resource-Aware Dynamic Service Deployment for Local IoT Edge Computing: Healthcare Use Case

Abstract

Edge computing is a novel computing paradigm moving server resources closer to enddevices. In the context of IoT, Edge computing is a centric technology for enabling reliable, context-aware and low-latency services for several application areas such as smart healthcare, smart industry and smart cities. In our previous work, we have proposed a three-tier IoT Edge architecture and a virtual decentralized service platform based on lightweight microservices, called nanoservices, running on it. Together, these proposals form a basis for virtualizing the available local computational capacity and utilizing it to provide localized resource-efficient IoT services based on the applications' need. Furthermore, locally deployed functions are resilient to access network problems and can limit the propagation of sensitive user data for improved privacy. In this paper, we propose an automatic service and resource discovery mechanism for efficient on-the-fly deployment of nanoservices on local IoT nodes. As use case, we have selected a healthcare remote monitoring scenario, which requires high service reliability and availability in a highly dynamic environment. Based on the selected use case, we propose a real-world prototype implementation of the proposed mechanism on Raspberry Pi platform. We evaluate the performance and resourceefficiency of the proposed resource matching function with two alternative deployment approaches: containerized and non-containerized deployment. The results show that the containerized deployment is more resource-efficient, while the resource discovery and matching process takes approximately 6-17 seconds, where containerization adds only 1-1.5 seconds. This can be considered a feasible price for streamlined service management, scalability, resource-efficiency and fault-tolerance. IoT, edge computing, distributed computing, virtualization, resource discovery, microservices, nanoservices. INDEX TERMS

Distributed network and service architecture for future digital healthcare

“…We started by introducing our conceptual nanoEdge service model for enabling efficient distributed local edge computing. Then we presented the results of our study related to dynamic resource-aware service orchestration [29] and the integration of Blockchain with Edge Computing for achieving sufficient level of privacy and trust between various stakeholders of distributed e-health and e-welfare services [30]. As the fourth contribution, we proposed and simulated an algorithm for edge-cloud orchestration to minimize the usage of system resources, while maximizing the number of accepted latency-limited task requests [31], which is especially important latency and mission-critical medical applications, such as surgical navigation.…”

“…In [29], we implemented a dynamic resource/service matching mechanism for distributed local EC. It extends our nanoEdge model by enabling automatic resource discovery and deployment in highly dynamic IoT scenarios, where the population of local nodes changes fast.…”

Emerging Technologies for Next Generation Remote Health Care and Assisted Living

“…We have started the work of addressing these issues in [305] and [306], and the work continues. Furthermore, Intelligent AI-based collaboration between the edge-cloud architecture and the underlying network architectures can bring more intelligence to medical applications.…”

Emerging Technologies for Next Generation Remote Health Care and Assisted Living

“…We have started the work of addressing these issues in [301] and [302]. Furthermore, Intelligent AI-based collaboration between the edge-cloud architecture and the underlying network architectures can bring more intelligence to medical applications.…”