In Smart Things and smart city applications, IoT devices generate large amounts of data and deep learning technologies are used to acquire useful information from it. Based on the kind of application and data, there are various non-functional requirements, such as low latency for information presentation by MR and privacy for video processing. To serve these requirements, a computing platform needs to make appropriate use of computing resources, namely Cloud, Fog, and Dew. However, there are some technical challenges in designing such a platform: i) transparently satisfying application QoS; ii) running the application across various hardware and OSes without modification; iii) sharing the application context taking into account the validity of values (temporal locality) and the data privacy (spatial locality). In this paper, we introduce Laqista, a novel Cloud-Fog-Dew computing platform. Laqista serves applications in a serverless manner via the Edgeless API, which schedules requests and abstracts the details of the platform. Applications are separated into Logics and Models, which are converted to lightweight, platform-agnostic formats such as WebAssembly and ONNX, respectively. Additionally, the Context Store synchronizes application context among the nodes, handling the privacy and validity of data. We developed a prototype implementation of Laqista in Rust and evaluated its performance. Experimental results show that the Laqista design has practical performance and is applicable to real-time applications such as video processing and MR.

Many smart city applications utilize deep learning technologies to process the data generated by sensors and smart devices. However, current application hosting platforms are not suitable for deep learning applications, because of their special requirements, including GPU and large trained model data. We propose a smart city application hosting platform named MLess, which serves and scales deep-learning applications across servers. MLess adds an extra abstraction layer between applications and executing hosts, thus it allows developers to write applications in a serverless manner. We developed a Proof-of-Concept implementation of MLess and made preliminary evaluations against it. In future work, we plan to add QoS support like inference accuracy or throughput.