Building elastic and scalable edge resources is crucial for the successful implementation of platform-based smart city services. These services rely on edge computing to deliver low-latency applications, but the limited resources of edge devices have always been a challenge. A single edge device simply cannot handle the complex computations required by a smart city, which is why there is a growing need for the large-scale deployment of edge devices from different service providers to build a comprehensive edge resource platform.
However, selecting computing power from different service providers poses a game-theoretic problem. In order to incentivize service providers to actively contribute their resources and facilitate collaborative computing power with low-latency, a game-theoretic deep learning model is introduced. This model aims to help reach a consensus among service providers on task scheduling and resource provisioning.
Traditional centralized resource management approaches prove to be inefficient and lack credibility. This is where the introduction of blockchain technology comes into play, offering a decentralized and secure solution for resource trading and scheduling. By leveraging blockchain technology, a contribution-based proof mechanism is proposed to ensure the low-latency service of edge computing.
The deep learning model at the core of this approach consists of dual encoders and a single decoder. The Graph Neural Network (GNN) encoder processes structured decision action data, while the Recurrent Neural Network (RNN) encoder handles time-series task scheduling data. Through extensive experiments, it has been demonstrated that this model can reduce latency by a significant 584% when compared to the current state-of-the-art.
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This article addresses a critical challenge in the implementation of smart city services: the need for scalable and elastic edge resources. It is important to note that edge computing plays a crucial role in enabling low-latency applications, ensuring that smart city services can be delivered efficiently.
The proposed game-theoretic deep learning model showcases the potential of using advanced technology to address the resource limitations of edge computing. By incentivizing service providers to actively contribute their resources, this model enables efficient task scheduling and resource provisioning. Additionally, the introduction of blockchain technology adds a layer of trust and decentralization to the system, allowing for secure resource trading.
The combination of dual encoders, with the GNN encoder processing decision action data and the RNN encoder handling task scheduling data, allows for a comprehensive approach to resource management. This model has demonstrated impressive results, significantly reducing latency compared to current state-of-the-art solutions.
Looking ahead, this research opens up new possibilities for the development and deployment of smart city services. The game-theoretic approach, coupled with deep learning techniques and blockchain technology, has the potential to revolutionize how edge resources are utilized. As smart cities continue to evolve and grow, it will be essential to have efficient and scalable edge resources in place to support the increasing demand for low-latency applications and services.