Cyber Physical Systems (CPS) has been a popular research area in the last decade. The dependability of CPS is still a critical issue, and few surveys have been published in this domain. CPS is a dynamic complex system, which involves various multidisciplinary technologies. To avoid human errors and to simplify management, self-management CPS (SCPS) is a wise choice. To achieve dependable self-management, systematic solutions are necessary to verify the design and to guarantee the safety of self-adaptation decisions, as well as to maintain the health of SCPS. This survey first recalls the concepts of dependability, and proposes a generic environment-in-loop processing flow of self-management CPS, and then analyzes the error sources and challenges of self-management through the formal feedback flow. Focusing on reducing the complexity, we first survey the self-adaptive architecture approaches and applied dependability means, then we introduce a hybrid multi-role self-adaptive architecture, and discuss the supporting technologies for dependable self-management at the architecture level. Focus on dependable environment-centered adaptation, we investigate the verification and validation (V&V) methods for making safe self-adaptation decision and the solutions for processing decision dependably. For system-centered adaptation, the comprehensive self-healing methods are summarized. Finally, we analyze the missing pieces of the technology puzzle and the future directions. In this survey, the technical trends for dependable CPS design and maintenance are discussed, an all-in-one solution is proposed to integrate these technologies and build a dependable organic SCPS. To the best of our knowledge, this is the first comprehensive survey on dependable SCPS building and evaluation.

Graphics Processing Units (GPU) are widely used as deep learning accelerators because of its high performance and low power consumption. Additionally, it remains secure against hardware-induced transient fault injection attacks, a classic type of attacks that have been developed on other computing platforms. In this work, we demonstrate that well-trained machine learning models are robust against hardware fault injection attacks when the faults are generated randomly. However, we discover that these models have components, which we refer to as sensitive targets, that are vulnerable to faults. By exploiting this vulnerability, we propose the Lightning attack, which precisely strikes the model’s sensitive targets with hardware-induced transient faults based on the Dynamic Voltage and Frequency Scaling (DVFS). We design a sensitive targets search algorithm to find the most critical processing units of Deep Neural Network (DNN) models determining the inference results, and develop a genetic algorithm to automatically optimize the attack parameters for DVFS to induce faults. Experiments on three commodity Nvidia GPUs for four widely-used DNN models show that the proposed Lightning attack can reduce the inference accuracy by 69.1% on average for non-targeted attacks, and, more interestingly, achieve a success rate of 67.9% for targeted attacks.

Directed acyclic graph (DAG)-aware task scheduling algorithms have been studied extensively in recent years, and these algorithms have achieved significant performance improvements in data-parallel analytic platforms. However, current DAG-aware task scheduling algorithms, among which HEFT and GRAPHENE are notable, pay little attention to the cache management policy, which plays a vital role in in-memory data-parallel systems such as Spark. Cache management policies that are designed for Spark exhibit poor performance in DAG-aware task-scheduling algorithms, which leads to cache misses and performance degradation. In this study, we propose a new cache management policy known as Long-Running Stage Set First (LSF), which makes full use of the task dependencies to optimize the cache management performance in DAG-aware scheduling algorithms. LSF calculates the caching and prefetching priorities of resilient distributed datasets according to their unprocessed workloads and significance in parallel scheduling, which are key factors in DAG-aware scheduling algorithms. Moreover, we present a cache-aware task scheduling algorithm based on LSF to reduce the resource fragmentation in computing. Experiments demonstrate that, compared to DAG-aware scheduling algorithms with LRU and MRD, the same algorithms with LSF improve the JCT by up to 42% and 30%, respectively. The proposed cache-aware scheduling algorithm also exhibits about 12% reduction in the average job completion time compared to GRAPHENE with LSF.

The failure detector is one of the fundamental components that maintain high availability of Peer-to-Peer (P2P) networks. Under different network conditions, the adaptive failure detector based on quality of service (QoS) can achieve the detection time and accuracy required by upper applications with lower detection overhead. In P2P systems, complexity of network and high churn lead to high message loss rate. To reduce the impact on detection accuracy, baseline detection strategy based on retransmission mechanism has been employed widely in many P2P applications; however, Chen’s classic adaptive model cannot describe this kind of detection strategy. In order to provide an efficient service of failure detection in P2P systems, this paper establishes a novel QoS evaluation model for the baseline detection strategy. The relationship between the detection period and the QoS is discussed and on this basis, an adaptive failure detector (B-AFD) is proposed, which can meet the quantitative QoS metrics under changing network environment. Meanwhile, it is observed from the experimental analysis that B-AFD achieves better detection accuracy and time with lower detection overhead compared to the traditional baseline strategy and the adaptive detectors based on Chen’s model. Moreover, B-AFD has better adaptability to P2P network.