Failure Detector Research Articles

Probabilistic and temporal failure detectors for solving distributed problems

Failure detectors (FD)s are celebrated for their modularity in solving distributed problems. Algorithms are constructed using FD building blocks. Synchrony assumptions to implement FDs are studied separately and are typically expressed as eventual guarantees that need to hold, after some point in time, forever and deterministically. But in practice, they may hold only probabilistically and temporarily. This paper studies FDs in a realistic system N, where asynchrony is inflicted by probabilistic synchronous communication. We first address a problem with ⋄S, the weakest FD to solve consensus: an implementation of “consensus with probability 1” is possible in N without randomness in the algorithm, while an implementation of “⋄S with probability 1” is impossible in N. We introduce ⋄S⁎, a new FD with probabilistic and temporal accuracy. We prove that ⋄S⁎ (i) is implementable in N and (ii) can replace ⋄S, in several existing deterministic consensus algorithms that use ⋄S, to yield an algorithm that solves “consensus with probability 1”. We extend our results to other FD classes, e.g., ⋄P, and to a larger set of problems (beyond consensus), which we call decisive problems.

Using Machine Learning for Dependable Outlier Detection in Environmental Monitoring Systems

Sensor platforms used in environmental monitoring applications are often subject to harsh environmental conditions while monitoring complex phenomena. Therefore, designing dependable monitoring systems is challenging given the external disturbances affecting sensor measurements. Even the apparently simple task of outlier detection in sensor data becomes a hard problem, amplified by the difficulty in distinguishing true data errors due to sensor faults from deviations due to natural phenomenon, which look like data errors. Existing solutions for runtime outlier detection typically assume that the physical processes can be accurately modeled, or that outliers consist in large deviations that are easily detected and filtered by appropriate thresholds. Other solutions assume that it is possible to deploy multiple sensors providing redundant data to support voting-based techniques. In this article, we propose a new methodology for dependable runtime detection of outliers in environmental monitoring systems, aiming to increase data quality by treating them. We propose the use of machine learning techniques to model each sensor behavior, exploiting the existence of correlated data provided by other related sensors. Using these models, along with knowledge of processed past measurements, it is possible to obtain accurate estimations of the observed environment parameters and build failure detectors that use these estimations. When a failure is detected, these estimations also allow one to correct the erroneous measurements and hence improve the overall data quality. Our methodology not only allows one to distinguish truly abnormal measurements from deviations due to complex natural phenomena, but also allows the quantification of each measurement quality, which is relevant from a dependability perspective. We apply the methodology to real datasets from a complex aquatic monitoring system, measuring temperature and salinity parameters, through which we illustrate the process for building the machine learning prediction models using a technique based on Artificial Neural Networks, denoted ANNODE ( ANN Outlier Detection ). From this application, we also observe the effectiveness of our ANNODE approach for accurate outlier detection in harsh environments. Then we validate these positive results by comparing ANNODE with state-of-the-art solutions for outlier detection. The results show that ANNODE improves existing solutions regarding accuracy of outlier detection.

A Review on Fault Tolerance in Distributed Database

In this paper, we study about the different types of fault tolerance techniques which are used in various distributed database systems. The main focus of this research is about how the data are storedin the servers, fault detection techniques and the recovery techniques used. A fault can occur for many reasons. For example, system failure, resource failure, network between the server’s failure and any other reasons. These faults must be emphasis in order to make sure the system can work smoothly without any problem. A proper failure detector and a reliable fault tolerance technique can avoid loss and at once save the system from fail.

On the weakest information on failures to solve mutual exclusion and consensus in asynchronous crash-prone read/write systems

Mutual exclusion and consensus are among the most important coordination problems encountered in asynchronous concurrent systems, whether processes communicate using read/write registers or message passing. Unfortunately, neither can be solved in crash-prone systems, as soon as even a single process may crash. Hence, an important question: which is the weakest information on failures that must be given to the processes so that these problems can be solved whatever the number of crashes. This approach to circumvent impossibility results is known under the name failure detectors.Considering mutual exclusion and consensus in a crash-prone asynchronous system where the processes communicate through read/write registers, this article answers the previous question by presenting two failure detectors. The first, called Quasi-Perfect (QP) allows mutual exclusion to be solved in the presence of any number of process crashes. The second, called Ω⁎, allows consensus to be solved in the general model where not all but an a priori unknown subset of processes participates in consensus. In addition to algorithms solving each of the previous problems with the help of the associated failure detector, the article shows that QP and Ω⁎ provides the weakest information on failures needed to solve mutex exclusion and participant-restricted consensus respectively.

Integration of Failure Detector in Bias Filter for Estimation of False Data Injection Cyberattacks

In order to enable many of the required smart grid functionalities, distribution systems are becoming increasingly dependent on state estimators. Many cyber-attacks attempt false data injection (FDI) attacks on such state estimators. The majority of the existing literature deal with FDIs in distribution systems state estimation either by the analysis of the residual vector elements, or by the analysis of historical data. In this work, we adopt an alternative approach for the detection of FDIs in distribution system state estimation, wherein FDIs are modelled as measurement biases and a bias filter is employed for FDI detection. Additionally, in order to enable the detection of time-variable FDIs, a failure detector is integrated in the recursive formulation of the bias filter, which is based on the Kalman filter. The developed approach is accordingly capable of identifying time-varying FDIs, which can evade many of the existing FDI detection methods. Simulation case studies are performed on the IEEE 13-node and 123-node feeders with different FDIs and the performance of the proposed approach is analyzed.

Low Cost Artificial Ventilator Embedding Unsupervised Learning for Hardware Failure Detection [Society News

In this paper, a less than $<i>200</i> artificial ventilator that can be used against <i>COVID-19</i> pandemic is presented. Using low-cost easyto-find materials, it has been designed for helping developing countries where supplies for building new medical equipments are limited. It complies with medical requirements, allowing to monitor and adjust ventilation parameters such as tidal volume, maximum intra-lung pressure and breath rate. Even if this ventilator is low cost, focus has been placed on improving its global reliability. Using low-cost recycled materials may lead to mechanical failures, this potential drawback is addressed with an intelligent embedded hardware failure detector implemented inside the microcontroller. Using K-means optimized algorithm, it learns in a short time normal operation corresponding to the couple formed by a given ventilator set-up and a patient. In case of a mechanical breakdown, an alert is generated to inform medical staff. First, mechanical, electrical and software architectures of the system are presented, then hardware failure detection algorithm is detailed. Finally, test results done at IRBA using an artificial lung are discussed. The overall project has been published as an open source one on GitHub: https://github.com/iutgeiitoulon/ArtificialVentilator.

Optimally Self-Healing IoT Choreographies

In the industrial Internet of Things domain, applications are moving from the Cloud into the Edge, closer to the devices producing and consuming data. This means that applications move from the scalable and homogeneous Cloud environment into a potentially constrained heterogeneous Edge network. Making Edge applications reliable enough to fulfill Industry 4.0 use cases remains an open research challenge. Maintaining operation of an Edge system requires advanced management techniques to mitigate the failure of devices. This article tackles this challenge with a twofold approach: (1) a policy-enabled failure detector that enables adaptable failure detection and (2) an allocation component for the efficient selection of failure mitigation actions. The parameters and performance of the failure detection approach are evaluated, and the performance of an energy-efficient allocation technique is measured. Finally, a vision for a complete system and an example use case are presented.

Perfect failure detection with very few bits

A failure detector is a distributed oracle that provides each process with a module that continuously outputs an estimate of which processes in the system have failed. The perfect failure detector provides accurate and eventually complete information about process failures. We show that, in asynchronous failure-prone message-passing systems, perfect failure detection can be achieved using an oracle that outputs at most ⌈log⁡α(n)⌉+1 bits per process in n-process systems, where α denotes the inverse-Ackermann function. This result is essentially optimal, as we also show that, in the same environment, no failure detector outputting a constant number of bits per process can achieve perfect failure detection.

Uniform reliable broadcast in anonymous distributed systems with fair lossy channels

Uniform reliable broadcast (URB) is an important abstraction in distributed systems, offering delivery guarantee when spreading messages between processes. Informally, URB guarantees that if a process (correct or not) delivers a message m, then all correct processes deliver m. This abstraction has been extensively investigated in distributed systems where all processes have unique identifiers. Furthermore, the majority of papers in the literature usually assume that the communication channels of the system are reliable, which is not always the case in real systems. In this paper, the URB abstraction is investigated in anonymous asynchronous message passing distributed systems with process crash failures and fair lossy channels. For that, we assume the availability of a random number generator that generates unique global values with very high probability. Firstly, a simple URB algorithm is given assuming a majority of correct processes. Then, we prove the impossibility of solving URB without a majority of correct processes if no failure detector is used. Subsequently, a new failure detector class $$A{\varTheta }$$ is proposed, which can be used to implement URB with any number of correct processes. However, the two previous URB algorithms are non-quiescent because every correct process, to offset the loss of messages caused by fair lossy channels, has to broadcast all URB $$\_$$ delivered messages forever. Hence, a perfect anonymous failure detector $$AP^*$$ is proposed, together with $$A{\varTheta }$$ , to make the URB algorithm quiescent. Finally, we discuss an alternative failure detector $$A{\varTheta }P^*$$ , which combines the properties of $$A{\varTheta }$$ and $$AP^*$$ .

An Eventually Perfect Failure Detector for Networks of Arbitrary Topology Connected with ADD Channels Using Time-To-Live Values

We present an implementation of an eventually perfect failure detector in an arbitrarily connected, partitionable network. We assume ADD channels: for each one there exist constants [Formula: see text], [Formula: see text], not known to the processes, such that for every [Formula: see text] consecutive messages sent in one direction, at least one is delivered within time [Formula: see text]. The best previous implementation used messages of bounded size, but exponential in [Formula: see text], the number of nodes. The main contribution of this paper is a novel use of time-to-live values in the design of failure detectors, obtaining a flexible implementation that uses messages of size [Formula: see text].

A Comprehensive Study on Failure Detectors of Distributed Systems

A Comprehensive Study on Failure Detectors of Distributed Systems

Hybrid binary consensus in anonymous asynchronous systems using coins and failure detectors

Fault tolerance and agreement are essential capabilities of current distributed systems. In this context, Binary Consensus is a key problem. It is well-known that consensus cannot be solved in a pure asynchronous system when at least one process can crash. To overcome this impossibility result, the asynchronous system is usually augmented with randomization, failure detectors or some kind of synchronization. Random binary consensus in classic asynchronous systems is usually solved using a common coin to achieve a constant expected number of rounds. However, to our knowledge, the literature lacks the existence of common coin algorithms for anonymous asynchronous systems. In this paper, an implementation of a common coin that runs on anonymous asynchronous systems is proposed and it is proved that the probability that this common coin gives the same outcome to all processes is bigger than 0.75 with small bias. This result allows to solve random consensus, in anonymous asynchronous systems, in four expected rounds, independently of the number of processes in the system. The main result of this paper is a hybrid (deterministic/random) binary consensus algorithm which solves consensus in anonymous asynchronous systems enriched with a common coin and a failure detector of class $$A{\varOmega}'$$, where less than half of the processes may fail, which is an optimal bound on the number of failures. To our knowledge, this is the first such algorithm. It only needs a quadratic number of interchanged messages per round, and the size of these messages is minimal (O(1) bytes). Consensus is achieved in one round by our hybrid algorithm if the failure detector does not make any errors or all processes propose the same value, and in four expected rounds if the adversary does not control the common coin. Additionally, we conducted an experimental evaluation whose results agree with the expected ones and slightly improve them. In particular, above 95% of the times, all processes get the same outcome from the coin, and random consensus is achieved in less than two rounds on average with very small standard deviation. This makes our algorithms good candidates for deployment in practical distributed systems.

Consensus using omega in asynchronous systems with unknown membership and degenerative Byzantine failures

We study consensus in asynchronous systems where membership is unknown, and where up to f degenerative Byzantine failures can happen. In our failure model a faulty process can have a Byzantine behavior (i.e., it deviates from its specification), and, furthermore, every faulty process will degenerate such that eventually it will have permanent physical or transmission failures. We present a simple algorithm that solves Consensus using the Omega failure detector and a new broadcast primitive called RFLOB in an asynchronous system with degenerative Byzantine failures, which is optimal with respect to failures because it works when f<n/3. RFLOB guarantees reliable, FIFO and local order broadcast in systems with Byzantine processes and unknown membership. Finally, we present an algorithm that implements an Omega failure detector with unknown membership and minimum connectivity (i.e., communication reliability, and synchrony properties) in a system with degenerative Byzantine failures.

Implementing ♢P with Bounded Messages on a Network of ADD Channels

We present an implementation of the eventually perfect failure detector [Formula: see text] from the original hierarchy of the Chandra-Toueg [3] oracles on an arbitrary partitionable network composed of unreliable channels that can lose and reorder messages. Prior implementations of [Formula: see text] have assumed different partially synchronous models ranging from bounded point-to-point message delay and reliable communication to unbounded message size and known network topologies. We implement [Formula: see text] under very weak assumptions on an arbitrary, partitionable network composed of Average Delayed/Dropped (ADD) channels [11] to model unreliable communication. Unlike older implementations, our failure detection algorithm uses bounded-sized messages to eventually detect all nodes that are unreachable (crashed or disconnected) from it.

&lt;italic&gt;RT-ByzCast&lt;/italic&gt;: Byzantine-Resilient Real-Time Reliable Broadcast

Today's cyber-physical systems face various impediments to achieving their intended goals, namely, communication uncertainties and faults, relative to the increased integration of networked and wireless devices, hinder the synchronism needed to meet real-time deadlines. Moreover, being critical, these systems are also exposed to significant security threats. This threat combination increases the risk of physical damage. This paper addresses these problems by studying how to build the first real-time Byzantine reliable broadcast protocol (RTBRB) tolerating network uncertainties, faults, and attacks. Previous literature describes either real-time reliable broadcast protocols, or asynchronous (non real-time) Byzantine ones. We first prove that it is impossible to implement RTBRB using traditional distributed computing paradigms, e.g., where the error/failure detection mechanisms of processes are decoupled from the broadcast algorithm itself, even with the help of the most powerful failure detectors. We circumvent this impossibility by proposing RT-ByzCast , an algorithm based on aggregating digital signatures in a sliding time-window and on empowering processes with self-crashing capabilities to mask and bound losses. We show that RT-ByzCast (i) operates in real-time by proving that messages broadcast by correct processes are delivered within a known bounded delay, and (ii) is reliable by demonstrating that correct processes using our algorithm crash themselves with a negligible probability, even with message loss rates as high as 60 percent.

A Hierarchical Failure Detector Based on Architecture in VANETs

The failure detector is one of the fundamental components for maintaining high availability of Vehicular Ad-hoc Networks (VANETs). However, the dynamic nature of VANETs caused by the high mobility of vehicles and communication link failures has a serious impact on the performance of failure detection. Therefore, it is very meaningful to design a suitable failure detector that can deal with the dynamic nature of VANETs well. In this paper, we propose a hierarchical failure detector based on the architecture of VANETs. This failure detector can adapt to the dynamic network conditions and meet the different Quality of Service (QoS) requirements of multiple applications in VANETs. Different from existing failure detectors, we propose a failure detector that employs a detection-result sharing mechanism and groups the nodes according to the architecture of VANETs. We evaluate our proposed failure detector by using NS2 and GT-ITM to simulate the work environment of VANETs. The experimental result shows that our proposed failure detector can improve the detection time by at most 45% and the detection accuracy by at most 25% under similar detection overhead.

Experimental research of dependence between informative parameters NDT techniques and steel physical and mechanical properties

Research of new methods and technologies of non-destructive control of change of long-term metal structures is an actual task of the present. In this article the methodology and results of experimental studies, the main aim of which is to determine the correlation between the intensity of structural noise in the material determined by failure detectors based on ultrasonic field-induced phase grating and the physical and mechanical properties of steels are presented.

Failure detectors for crash faults in cloud

Failure detector (FD) is an inherent component in atomic broadcast and consensus protocols. Failures are broadly categorized into two types: crash and byzantine. The crash failures simply discontinue the working of a system whereas byzantine reflects the malicious behavior while ongoing communication. The problem to detect a failure becomes more challenging in a dynamic asynchronous environment like cloud computing. The paper proposes a failure detector in order to handle crash faults in cloud while addressing scalability. We introduce BCMP networks in order to compute performance parameters of the proposed algorithm, thereby, detecting failures in an accurate manner. Although, failure detection schemes have a tradeoff between efficiency and latency, the proposed algorithm achieves optimal balance between both metrics.

A 16-Element W-Band Phased-Array Transceiver Chipset With Flip-Chip PCB Integrated Antennas for Multi-Gigabit Wireless Data Links

This paper describes the design and implementation of a W-band phased-array system with printed circuit board (PCB) integrated antennas in two polarizations capable of multi-gigabit spectrally efficient wireless communication. The chipset is manufactured in a 0.18-μm SiGe BiCMOS technology with f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sub> /f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MAX</sub> of 240-/270-GHz and is flip-chipped onto a lowcost organic PCB with integrated antenna arrays. Each chip is equipped with 16-transmit/4-receive or 16-receive/4-transmit calibrated phase shifter elements and direct upand downconverters plus a half-rate phase-locked loop. The different system tradeoffs required to establish a multi-Gb/s wireless link at millimeter waves are carefully studied. Built-in element failure detectors, power detectors, and digital interface enable factory calibration and self-test capability. Each transceiver chip operates from 1.5and 2.5-V supplies and consumes 5.5 and 4.5 W in transmit and receive mode, respectively. The peak transmitter effective isotropic radiation power is 34 dBm in each polarization with a measured receiver noise figure of 6.5 dB at 94 GHz. At a distance of 1 m, a maximum wireless data rate of 30 Gb/s (per polarization) using 64-QAM can be achieved and at 20 m, 8 Gb/s (dual polarization) can be established using QPSK modulation.

Impact FD: An Unreliable Failure Detector Based on Process Relevance and Confidence in the System

This paper presents a new unreliable failure detector, called the Impact failure detector (FD) that, contrarily to the majority of traditional FDs, outputs a trust level value which expresses the degree of confidence in the system. An impact factor is assigned to each process and the trust level is equal to the sum of the impact factors of the processes not suspected of failure. Moreover, a threshold parameter defines a lower bound value for the trust level, over which the confidence in the system is ensured. In particular, we defined a f l exi bi l i t y property that denotes the capacity of the Impact FD to tolerate a certain margin of failures or false suspicions, i.e., its capacity of considering different sets of responses that lead the system to trusted states. The Impact FD is suitable for systems that present node redundancy, heterogeneity of nodes, clustering feature, and allow a margin of failures which does not degrade the confidence in the system. The paper also includes a timer-based distributed algorithm which implements an Impact FD, as well as its proof of correctness, for systems whose links are lossy asynchronous or for those whose all (or some) links are eventually timely. Performance evaluation results, based on PlanetLab [1] traces, confirm the degree of flexible applicability of our failure detector and that, due to the accepted margin of failure, both failures and false suspicions are more tolerated when compared to traditional unreliable failure detectors.