Optimal Online Liveness Fault Detection for Multilayer Cloud Computing Systems

Lee, Yen Lin; Liang, Deron; Wang, Wei-Jen

doi:10.1109/tdsc.2021.3100680

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…There are, however, other detectors that can find many faults much faster. Considering the need for rapid fault detection in cloud computing environments, the authors in [17] propose an online liveness fault detection mechanism that integrates existing detectors. In [18], the authors propose a new scheme to manage resources in the cloud market and to reduce the service level agreement violation, cost, energy usage, and time using fuzzy logic.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Failure-Aware Scheme for Profit Maximization in the Cloud Market

et al. 2022

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A successful cloud trading system requires suitable financial incentives for all parties involved. Cloud providers in the cloud market provide computing services to clients in order to perform their tasks and earn extra money. Unfortunately, the applications in the cloud are prone to failure for several reasons. Cloud service providers are responsible for managing the availability of scheduled computing tasks in order to provide high-level quality of service for their customers. However, the cloud market is extremely heterogeneous and distributed, making resource management a challenging problem. Protecting tasks against failure is a challenging and non-trivial mission due to the dynamic, heterogeneous, and largely distributed structure of the cloud environment. The existing works in the literature focus on task failure prediction and neglect the remedial (post) actions. To address these challenges, this paper suggests a fault-tolerant resource management scheme for the cloud computing market in which the optimal amount of computing resources is extracted at each system epoch to replace failed machines. When a cloud service provider detects a malfunctioning machine, they transfer the associated work to new machinery.

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Section: Related Workmentioning

confidence: 99%

Machine Learning Failure-Aware Scheme for Profit Maximization in the Cloud Market

et al. 2022

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“…Duan et al (2022) proposed a two-stage Bayesian control scheme for fault detection of complex equipment, which can effectively avoid false positives and detect early faults. Lee et al (2021) proposed an efficient dynamic fault detection mechanism combined with the existing detectors, which reduced the time of their fault detection mechanism by 70.3% compared with the existing fault detection mechanism. For periodic concurrent faults (Li et al, 2020), parallel cyclic redundancy detection is more commonly used for fault detection.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous attitude fault detection and control of the six-rotor UAV based on event trigger mechanism

Huang,

Zhang,

Dai

et al. 2024

Transactions of the Institute of Measurement and Control

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This paper analyzes the problem of simultaneous fault detection and control of the six-rotor unmanned aerial vehicle control system, considering external disturbance, measurement disturbance, and actuator fault. The integral event trigger mechanism is introduced in the control side and sensor side of the system. Based on Lyapunov stability and H∞ control theory, the sufficient conditions to make the fault system asymptotically stable and have certain performance indexes are given by means of linear matrix inequalities; at the same time, the design criteria of event trigger parameters are also given. The linear matrix inequality is decoupled, and the calculation method of gain matrix of simultaneous fault detection and control module is given. The effectiveness of the proposed method is verified by simulation experiments.

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