Constraint Based System-Level Diagnosis of Multiprocessors

Massively parallel multiprocessors induce new requirements for system-level fault diagnosis, like handling a huge number of processing elements in an inhomogeneous system. Traditional diagnostic models (like PMC, BGM, etc.) are insufficient to fulfill all of these requirements. This paper presents a novel modelling technique, based on a special area of artificial intelligence (AI) methods: constraint satisfaction (CS). The constraint based approach is able to handle functional faults in a similar way to the Russel-Kime model. Moreover, it can use multiple-valued logic to deal with system components having multiple fault modes. The resolution of the produced models can be adjusted to fit the actual diagnostic goal. Consequently, constrint based methods are applicable to a much wider range of multiprocessor architectures than earlier models. The basic problem of system-level diagnosis, syndrome decoding, can be easily transformed into a constraint satisfaction problem (CSP). Thus, the diagnosis algorithm can be derived from the related constraint solving algorithm. Different abstraction leveles can be used for the various diagnosis resolutions, employing the same methodology. As examples, two algorithms are described in the paper; both of them is intended for the Parsytec GCel massively parallel system. The centralized method uses a more elaborate system model, and provides detailed diagnostic information, suitable for off-line evaluation. The distributed method makes fast decisions for reconfiguration control, using a simplified model. Keywords system-level self-diagnosis, massively parallel computing systems, constraint satisfaction, diagnostic models, centralized and distributed diagnostic algorithms

Probabilistic diagnostics with P-graphs

This paper presents a novel approach for solving the probabilistic diagnosis problem in multiprocessor systems. The main idea of the algorithm is based on the reformulation of the diagnostic procedure as a P-graph model. The same, well-elaborated mathematical paradigm - originally used to model material flow - can be applied in our approach to model information flow. This idea is illustrated by deriving a maximum likelihood diagnostic decision procedure. The diagnostic accuracy of the solution is considered on the basis of simulation measurements, and a method of constructing a general framework for different aspects of a complex problem is demonstrated with the use of P-graph models

Testing the bus guardian unit of the FTMP

Fault-tolerant multiprocessor (FTMP) operation is discussed. Fault-modeling in the bus guardian units (BGUs) is covered. Testing the BGU is discussed. A testing algorithm is proposed

Gradient based system-level diagnosis

Traditional approaches in system-level diagnosis in multiprocessor systems are usually based on the oversimplified PMC test invalidation model, however Blount introduced a more general model containing conditional probabilities as parameters for different test invalidation situations. He suggested a lookup table based approach, but no algorithmic solution has been elaborated until our P-graph based solution introduced in previous publications. In this approach the diagnostic process is formulated as an optimization problem and the optimal solution is determined. Although the average behavior of the algorithm is quite good, the worst case complexity is exponential. In this paper we introduce a novel group of fast diagnostic algorithms that we named gradient based algorithms. This approach only approximates the optimal maximum likelihood or maximum a posteriori solution, but it has a polynomial complexity of the magnitude of O\left (N \cdot NbCount + N^2\right ), where N is the size of the system and NbCount is number of neighbors of a single unit. The idea of the base algorithm is that it takes an initial fault pattern and iterates till the likelihood of the actual fault pattern can be increased with a single state-change in the pattern. Improvements of this base algorithm, complexity analysis and simulation results are also presented. The main, although not exclusive application field of the algorithms is wafer-scale diagnosis, since the accuracy and the performance is still good even if relative large number of faults are present

Constraint Based Diagnosis Algorithms For Multiprocessors

Constraint-based diagnosis algorithms for multiprocessors A. Petri, P. Urban, J. Altmann, M. Dal Cin, E. Selenyi, K. Tilly, A. Pataricza In the latest years, new ideas appeared in system level diagnosis of multiprocessor systems. In contrary to the traditional diagnosis models (like PMC, BGM, etc.) which use strictly graph-oriented methods to determine the faulty components in a system, these new theories prefer AI-based algorithms, especially CSP methods. Syndrome decoding, the basic problem of self-diagnosis, can be easily transformed into constraints between the state of the tester and the tested components. Therefore, the diagnosis algorithm can be derived from a special constraint solving algorithm. The "benign" nature of the constraints (all their variables, representing the fault states of the components, have a very limited domain; the constraints are simple and similar to each other) reduces the algorithm's complexity so it can be converted to a powerful distributed diagnosis method with a minimal overhead. Experimental algorithms (using both centralized and distributed approach) were implemented for a Parsytec GC massively parallel multiprocessor system

Mixture-Based Clustering and Hidden Markov Models for Energy Management and Human Activity Recognition: Novel Approaches and Explainable Applications

In recent times, the rapid growth of data in various fields of life has created an immense need for powerful tools to extract useful information from data. This has motivated researchers to explore and devise new ideas and methods in the field of machine learning. Mixture models have gained substantial attention due to their ability to handle high-dimensional data efficiently and effectively. However, when adopting mixture models in such spaces, four crucial issues must be addressed, including the selection of probability density functions, estimation of mixture parameters, automatic determination of the number of components, identification of features that best discriminate the different components, and taking into account the temporal information. The primary objective of this thesis is to propose a unified model that addresses these interrelated problems. Moreover, this thesis proposes a novel approach that incorporates explainability. This thesis presents innovative mixture-based modelling approaches tailored for diverse applications, such as household energy consumption characterization, energy demand management, fault detection and diagnosis and human activity recognition. The primary contributions of this thesis encompass the following aspects: Initially, we propose an unsupervised feature selection approach embedded within a finite bounded asymmetric generalized Gaussian mixture model. This model is adept at handling synthetic and real-life smart meter data, utilizing three distinct feature extraction methods. By employing the expectation-maximization algorithm in conjunction with the minimum message length criterion, we are able to concurrently estimate the model parameters, perform model selection, and execute feature selection. This unified optimization process facilitates the identification of household electricity consumption profiles along with the optimal subset of attributes defining each profile. Furthermore, we investigate the impact of household characteristics on electricity usage patterns to pinpoint households that are ideal candidates for demand reduction initiatives. Subsequently, we introduce a semi-supervised learning approach for the mixture of mixtures of bounded asymmetric generalized Gaussian and uniform distributions. The integration of the uniform distribution within the inner mixture bolsters the model's resilience to outliers. In the unsupervised learning approach, the minimum message length criterion is utilized to ascertain the optimal number of mixture components. The proposed models are validated through a range of applications, including chiller fault detection and diagnosis, occupancy estimation, and energy consumption characterization. Additionally, we incorporate explainability into our models and establish a moderate trade-off between prediction accuracy and interpretability. Finally, we devise four novel models for human activity recognition (HAR): bounded asymmetric generalized Gaussian mixture-based hidden Markov model with feature selection~(BAGGM-FSHMM), bounded asymmetric generalized Gaussian mixture-based hidden Markov model~(BAGGM-HMM), asymmetric generalized Gaussian mixture-based hidden Markov model with feature selection~(AGGM-FSHMM), and asymmetric generalized Gaussian mixture-based hidden Markov model~(AGGM-HMM). We develop an innovative method for simultaneous estimation of feature saliencies and model parameters in BAGGM-FSHMM and AGGM-FSHMM while integrating the bounded support asymmetric generalized Gaussian distribution~(BAGGD), the asymmetric generalized Gaussian distribution~(AGGD) in the BAGGM-HMM and AGGM-HMM respectively. The aforementioned proposed models are validated using video-based and sensor-based HAR applications, showcasing their superiority over several mixture-based hidden Markov models~(HMMs) across various performance metrics. We demonstrate that the independent incorporation of feature selection and bounded support distribution in a HAR system yields benefits; Simultaneously, combining both concepts results in the most effective model among the proposed models

CONCURRENT DIAGNOSTICS IN MULTIPROCESSOR SYSTEMS

The paper presents a survey of diagnostic methods for multiprocessor systems. The diagnostic means known so far are first summarized and evaluated from the point of view of their applicability to systems with distributed control and specifically to the multiprocessor systems. A combination of different diagnostic means is then suggested in order to achieve the maximum diagnostic coverage with minimum overhead

Dynamic Fault Diagnosis in Mobile Ad Hoc Networks

Fault diagnosis in Mobile Ad-hoc Networks (MANETs) is very challenging task. Diagnosis algorithm should be efficient enough to find the status (either faulty or fault free) of each mobile in the network. The models in the literature are either for static fault or dynamic fault. Dynamic fault identification is more complex and difficult than static fault. In this thesis, we proposed Dynamic Distributed Diagnosis Model to identify dynamic faults arising during the testing phase of the diagnosis session. The model assumes that each node has fixed and same set of neighbours i.e. the MANET topology is static throughout the diagnosis session. Our model works on a network with $n$ number of nodes, which is $\sigma$-diagnosable. Where $\sigma$ is one less than the minimum degree of a node in the network. It has two variation based on dissemination method, first is simple flooding approach and second is based on spanning tree. The flooding based model consists of two phases; a testing phase and a dissemination phase. The spanning tree based model has three phase; a testing phase, a building phase and a dissemination phase. In testing phase, we have used the concept of heartbeat, where every mobile broadcasts a response message at fixed interval, so that a node can correctly be diagnosed by at least one fault free neighbour. Building phase constructs a spanning tree with fault-free mobiles. Dissemination phase, with the help of spanning tree, disseminates the local diagnostic views through the fault-free mobiles. After aggregating the entire views, initiator node disseminates the global diagnostic view to the fault free mobiles down the spanning tree. In this way, all fault free units reach to an agreement about the status of other nodes in the network. Further, we have given the proof of correctness and completeness of our model and found the time complexity, and compared the simulation results with the existing fault diagnosis protocols

Multilevel distributed diagnosis and the design of a distributed network fault detection system based on the SNMP protocol.

In this thesis, we propose a new distributed diagnosis algorithm using the multilevel paradigm. This algorithm is a generalization of both the ADSD and Hi-ADSD algorithms. We present all details of the design and implementation of this multilevel adaptive distributed diagnosis algorithm called the ML-ADSD algorithm. We also present extensive simulation results comparing the performance of these three algorithms.In 1967, Preparata, Metze and Chien proposed a model and a framework for diagnosing faulty processors in a multiprocessor system. To exploit the inherent parallelism available in a multiprocessor system and thereby improving fault tolerance, Kuhl and Reddy, in 1980, pioneered a new area of research known as distributed system level diagnosis. Following this pioneering work, in 1991, Bianchini and Buskens proposed an adaptive distributed algorithm to diagnose fully connected networks. This algorithm called the ADSD algorithm has a diagnosis latency of O(N) testing rounds for a network with N nodes. With a view to improving the diagnosis latency of the ADSD algorithm, in 1998 Duarte and Nanya proposed a hierarchical distributed diagnosis algorithm for fully connected networks. This algorithm called the Hi-ADSD algorithm has a diagnosis latency of O(log2N) testing rounds. The Hi-ADSD algorithm can be viewed as a generalization of the ADSD algorithm.In all cases, the time required by the ML-ADSD algorithm is better than or the same as for the Hi-ADSD algorithm. The performance of the ML-ADSD algorithm can be improved by an appropriate choice of the number of clusters and the number of levels. Also, the ML-ADSD algorithm is scalable in the sense that only some minor modifications will be required to adapt the algorithm to networks of varying sizes. This property is not shared by the Hi-ADSD algorithm. The primary application of our research is to develop and implement a prototype network fault detection/monitoring system by integrating the ML-ADSD algorithm into a SNMP-based (Simple Network Management Protocol) fault management system. We report the details of the design and implementation of such a distributed network fault detection system