Abstract
Recently, the Internet-of-Things technique is believed to play an important role as the foundation of the coming Artificial Intelligence age for its capability to sense and collect real-time context information of the world, and the concept Artificial Intelligence of Things (AIoT) is developed to summarize this vision. However, in typical centralized architecture, the increasing of device links and massive data will bring huge congestion to the network, so that the latency brought by unstable and time-consuming long-distance network transmission limits its development. The multi-access edge computing (MEC) technique is now regarded as the key tool to solve this problem. By establishing a MEC-based AIoT service system at the edge of the network, the latency can be reduced with the help of corresponding AIoT services deployed on nearby edge servers. However, as the edge servers are resource-constrained and energy-intensive, we should be more careful in deploying the related AIoT services, especially when they can be composed to make complex applications. In this paper, we modeled complex AIoT applications using directed acyclic graphs (DAGs), and investigated the relationship between the AIoT application performance and the energy cost in the MEC-based service system by translating it into a multi-objective optimization problem, namely the CA^3D problem — the optimization problem was efficiently solved with the help of heuristic algorithm. Besides, with the actual simple or complex workflow data set like the Alibaba Cloud and the Montage project, we conducted comprehensive experiments to evaluate the results of our approach. The results showed that the proposed approach can effectively obtain balanced solutions, and the factors that may impact the results were also adequately explored.
Highlights
This article is part of the Topical Collection: Special Issue on Green Edge ComputingGuest Editors: Zhiyong Yu, Liming Chen, Sumi Helal, and Zhiwen YuThe rapid development and evolution of Artificial Intelligence (AI) theory and technology have brought a revolution to current information technology architectures
The main contributions are summarized as follows: 1. We investigated the development of artificial intelligence of things technology and discussed the feasibility of adopting the multi-access edge computing architecture to optimize the performance of the Artificial Intelligence of Things (AIoT) systems
To the best of our knowledge, the context-aware AIoT application deployment (CA3 D) problem is the first attempt to consider the deployment of AIoT services as well as optimizing the resource allocation strategy in the multi-access edge computing (MEC) environment, none of the existing approaches in former research works can be directly adopted in our problem
Summary
This article is part of the Topical Collection: Special Issue on Green Edge Computing. What’s more, with the help of the container platforms in the limelight like Kubernetes, it will be easy to manage services (e.g. the data pre-processing services) in the MEC environment These advantages cannot be the excuse of the carelessness in planning the multi-source AIoT sensing and analysing tasks — if the related services are not assigned to appropriate hosts, it may even obtain lower-quality result with much higher cost. We investigated the development of artificial intelligence of things technology and discussed the feasibility of adopting the multi-access edge computing architecture to optimize the performance of the AIoT systems.
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