Qingbo Wu

Remaining useful life (RUL) is a metric of health state for essential equipment. It plays a significant role in health management. However, RUL is often random and unknown. One type of physics-based method builds a mathematical model for RUL using prior principles, but this is a tough task in real-world applications. Another type of method estimates RUL from available information through condition and health monitoring; this is known as the data-driven method. Traditional data-driven methods require significant human effort in designing health features to represent performance degradation, yet the prediction accuracy is limited. With breakthroughs in various application scenarios in recent years, deep learning techniques provide new insights into this problem. Over the past few years, deep-learning-based RUL prediction has attracted increasing attention from the academic community. Therefore, it is necessary to conduct a survey on deep-learning-based RUL prediction. To ensure a comprehensive survey, the literature is reviewed from three dimensions. Firstly, a unified framework is proposed for deep-learning-based RUL prediction and the models and approaches in the literature are reviewed under this framework. Secondly, detailed estimation processes are compared from the perspective of different deep learning models. Thirdly, the literature is examined from the perspective of specific problems, such as scenarios where the collected data consist of limited labeled data. Finally, the main challenges and future directions are summarized.

Speculative execution and branch prediction are techniques that are widely used in modern superscalar processors to exploit instruction-level parallelism. Recently, researchers have discovered a new kind of attacks named Spectre which exploits speculation mechanisms with a side channel. Since speculation is widely used in modern superscalar processors, these vulnerabilities are found in many popular processors. Exploiting the security vulnerabilities, the attacker can leak the host memory from inside a KVM guest. While the hardware providers are trying to fix the issues from the microarchitecture designs in the next generation of their products, software mitigation are always desirable. Retpoline is a pure software fix developed by Google and is claimed to have a negligible impact on performance. In this paper, we look into the details of Retpoline and evaluate it with various workloads. We found that Retpoline does have impact on performance to the existing software but varies depending on how applications interact with the kernel. According to our experiment, it shows more regression on the network I/O than the storage. The more a program relies on the kernel, the greater regression it shows. To alleviate the impact, we propose a method that uses userspace network stack. We verify the proposal using Netmap userspace packet I/O framework. Besides, we observe great performance regression of applying Retpoline in some of userspace applications such as Perl interpreter which is thought to be the targets exploited by the new type of attacks. In the end, we review the experiment results and discuss the potential mitigation of Spectre in future.