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- 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.
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- eBPF and XDP are promising technologies that are capable of accelerating packet processing inside the Linux kernel. Despite these benefits, eBPF is constrained by a number of rigorous restrictions that are imposed to protect the kernel. One such restriction is the lack of support for floating-point values, which was introduced to achieve faster execution and avoid non-deterministic behavior. However, this has become a significant obstacle to expanding the functionality of eBPF programs with advanced algorithms. In this paper, we propose dynamic fixed-point as a solution to overcome this challenge within the restrictions of eBPF. Dynamic fixed-point values are an expansion from traditional fixed-point values, with the bit allocation adjusted dynamically. Benefit of dynamic fixed-point is that the accuracy of calculations are improved, which is one of the critical shortcomings of fixed-point. To demonstrate the effectiveness of our approach, we have designed and implemented a prototype of an entropy-based traffic anomaly detection framework and have reported on its performance and the detection accuracy. Our prototype, which employs dynamic fixed-point, has achieved an 18% improvement in throughput while also matching the detection accuracy of a similar system that employs floating-point values in user space.
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- Data binding in web front-end development has made a significant contribution to removing complexity from development and simplifying programming. However, data binding has caused a degradation of website performance at the cost of reducing the burden on programmers. In this paper, we propose Visible Anchor to solve the performance degradation caused by data binding. We develop a compiler called FaST that implements the method. Then, We compared the rendering time among websites built by existing methods and FaST compiler. The evaluation result revealed that the websites built by FaST compiler are at minimum 2.9 times faster to be rendered than the ones built by the existing methods. FaST made a significant contribution to improving the performance of web front-end data binding. Consequently, data binding with FaST can be a better choice for web front-end development.
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