Silvano Chiaradonna

Threshold-based mechanisms to discriminate transient from intermittent faults

This paper presents a class of count-and-threshold mechanisms, collectively named /spl alpha/-count, which are able to discriminate between transient faults and intermittent faults in computing systems. For many years, commercial systems have been using transient fault discrimination via threshold-based techniques. We aim to contribute to the utility of count-and-threshold schemes, by exploring their effects on the system. We adopt a mathematically defined structure, which is simple enough to analyze by standard tools. /spl alpha/-count is equipped with internal parameters that can be tuned to suit environmental variables (such as transient fault rate, intermittent fault occurrence patterns). We carried out an extensive behavior analysis for two versions of the count-and-threshold scheme, assuming, first, exponentially distributed fault occurrencies and, then, more realistic fault patterns.

Hidden Markov Models as a Support for Diagnosis: Formalization of the Problem and Synthesis of the Solution

In modern information infrastructures, diagnosis must be able to assess the status or the extent of the damage of individual components. Traditional one-shot diagnosis is not adequate, but streams of data on component behavior need to be collected and filtered over time as done by some existing heuristics. This paper proposes instead a general framework and a formalism to model such over-time diagnosis scenarios, and to find appropriate solutions. As such, it is very beneficial to system designers to support design choices. Taking advantage of the characteristics of the hidden Markov models formalism, widely used in pattern recognition, the paper proposes a formalization of the diagnosis process, addressing the complete chain constituted by monitored component, deviation detection and state diagnosis. Hidden Markov models are well suited to represent problems where the internal state of a certain entity is not known and can only be inferred from external observations of what this entity emits. Such over-time diagnosis is a first class representative of this category of problems. The accuracy of diagnosis carried out through the proposed formalization is then discussed, as well as how to concretely use it to perform state diagnosis and allow direct comparison of alternative solutions

Effective fault treatment for improving the dependability of COTS and legacy-based applications

This paper proposes a novel methodology and an architectural framework for handling multiple classes of faults (namely, hardware-induced software errors in the application, process and/or host crashes or hangs, and errors in the persistent system stable storage) in a COTS and legacy-based application. The basic idea is to use an evidence-accruing fault tolerance manager to choose and carry out one of multiple fault recovery strategies, depending upon the perceived severity of the fault. The methodology and the framework have been applied to a case study system consisting of a legacy system, which makes use of a COTS DBMS for persistent storage facilities. A thorough performability analysis has also been conducted via combined use of direct measurements and analytical modeling. Experimental results demonstrate that effective fault treatment, consisting of careful diagnosis and damage assessment, plays a key role in leveraging the dependability of COTS and legacy-based applications

Discriminating fault rate and persistency to improve fault treatment

In this paper the consolidate identification of faults, distinguished as transient or permanent/intermittent, is approached. Transient faults discrimination has long been performed in commercial systems: threshold-based techniques have been practice for several years for this purpose. The present work aims to contribute to the usefulness of the count-and-threshold scheme, through the analysis of its behaviour and the exploration of its effects on the system. To this goal, the scheme is mechanized as a device named /spl alpha/-count, endowed with a few controllable parameters. /spl alpha/-count tries to balance between two conflicting requirements: to keep in the system those components that have experienced just transient faults; and to remove quickly those affected by permanent or intermittent faults. Analytical models are derived, allowing detailed study of /spl alpha/-counts behaviour; the actual evaluation, in a range of configurations, is performed by standard tools, in terms of the delay in spotting faulty components and the probability of improperly blaming correct ones.

On a Modeling Framework for the Analysis of Interdependencies in Electric Power Systems

Nowadays, economy, security and quality of life heavily depend on the resiliency of a number of critical infrastructures, including the electric power system (EPS), through which vital services are provided. In existing EPS two cooperating infrastructures are involved: the electric infrastructure (EI) for the electricity generation and transportation to final users, and its information-technology based control system (ITCS) devoted to controlling and regulating the EI physical parameters and triggering reconfigurations in emergency situations. This paper proposes a modeling framework to capture EI and ITCS aspects, focusing on their interdependencies that contributed to the occurrence of several cascading failures in the past 40 years. A quite detailed analysis of the EI and ITCS structure and behavior is performed; in particular, the ITCS and EI behaviors are described by discrete and hybrid-state processes, respectively. To substantiate the approach, the implementation of a few basic modeling mechanisms inside an existing multiformalism/ multi-solution tool is also discussed.

Definition, implementation and application of a model-based framework for analyzing interdependencies in electric power systems

Abstract Electric power systems are prominent representatives of the critical infrastructure. Existing electric power systems are composed by two cooperating infrastructures: the electric infrastructure, which is responsible for electricity generation and electricity transmission to customers; and information technology based control systems that monitor and control the physical parameters of the electric infrastructure and trigger appropriate reconfigurations in emergency situations. The interactions between these two cooperating infrastructures must be carefully analyzed to understand and characterize their (inter)dependencies, i.e., how the state of one infrastructure influences or is correlated to the state of the other. This paper proposes a model-based framework for quantitatively analyzing the propagation and impact of malfunctions in electric power systems. The framework is implemented using the stochastic activity network (SAN) formalism and is applied to concrete case studies that support the understanding and assessment of the impact of interdependencies. The results assist in developing countermeasures that can help reduce electric power system vulnerabilities.

Quantification of dependencies between electrical and information infrastructures

Abstract In this paper we present an approach to model and quantify (inter)dependencies between the Electrical Infrastructure (EI) and the Information Infrastructure (II) that implements the EI control and monitoring system. The quantification is achieved through the integration of two models: one that concentrates more on the structure of the power grid and its physical quantities and one that concentrates on the behavior of the control system supported by the II. The modeling approach is exemplified on a scenario whose goal is to study the effects of an II partial failure (a denial of service attack that compromises the communication network) on the remote control of the EI. The approach has been initially developed as part of the European project CRUTIAL.

On Performability Modeling and Evaluation of Software Fault Tolerant Structures

An adaptive scheme for software fault-tolerance is evaluated from the point of view of performability, comparing it with previously published analyses of the more popular schemes, recovery blocks and multiple version programming. In the case considered, this adaptive scheme, “Self-Configuring Optimistic Programming” (SCOP), is equivalent to N-version programming in terms of the probability of delivering correct results, but achieves better performance by delaying the execution of some of the variants until it is made necessary by an error. A discussion follows highlighting the limits in the realism of these analyses, due to the assumptions made to obtain mathematically tractable models, to the lack of experimental data and to the need to consider also resource consumption in the definition of the models. We consider ways of improving usability of the results of comparative evaluation for guiding design decisions.

Dependability evaluation of web service-based processes

As Web service-based system integration recently became the mainstream approach to create composite services, the dependability of such systems becomes more and more crucial. Therefore, extensions of the common service composition techniques are urgently needed in order to cover dependability aspects and a core concept for the dependability estimation of the target composite service. Since Web services-based workflows fit into the class of systems composed of multiple phases, this paper attempts to apply methodologies and tools for dependability analysis of Multiple Phased Systems (MPS) to this emerging category of dependability critical systems. The paper shows how this dependability analysis constitutes a very useful support to the service provider in choosing the most appropriate service alternatives to build up its own composite service

An adaptive approach to achieving hardware and software fault tolerance in a distributed computing environment

This paper focuses on the problem of providing tolerance to both hardware and software faults in independent applications running on a distributed computing environment. Several hybrid-fault-tolerant architectures are identified and proposed. Given the highly varying and dynamic characteristics of the operating environment, solutions are developed mainly exploiting the adaptation property. They are based on the adaptive execution of redundant programs so as to minimise hardware resource consumption and to shorten response time, as much as possible, for a required level of fault tolerance. A method is introduced for evaluating the proposed architectures with respect to reliability, resource utilisation and response time. Examples of quantitative evaluations are also given.