A survey on online monitoring approaches of computer-based systems

This report surveys forms of online data collection that are in current use (as well as being the subject of research to adapt them to changing technology and demands), and can be used as inputs to assessment of dependability and resilience, although they are not primarily meant for this use

Achieving Robust Self-Management for Large-Scale Distributed Applications

Autonomic managers are the main architectural building blocks for constructing self-management capabilities of computing systems and applications. One of the major challenges in developing self-managing applications is robustness of management elements which form autonomic managers. We believe that transparent handling of the effects of resource churn (joins/leaves/failures) on management should be an essential feature of a platform for self-managing large-scale dynamic distributed applications, because it facilitates the development of robust autonomic managers and hence improves robustness of self-managing applications. This feature can be achieved by providing a robust management element abstraction that hides churn from the programmer. In this paper, we present a generic approach to achieve robust services that is based on finite state machine replication with dynamic reconfiguration of replica sets. We contribute a decentralized algorithm that maintains the set of nodes hosting service replicas in the presence of churn. We use this approach to implement robust management elements as robust services that can operate despite of churn. Our proposed decentralized algorithm uses peer-to-peer replica placement schemes to automate replicated state machine migration in order to tolerate churn. Our algorithm exploits lookup and failure detection facilities of a structured overlay network for managing the set of active replicas. Using the proposed approach, we can achieve a long running and highly available service, without human intervention, in the presence of resource churn. In order to validate and evaluate our approach, we have implemented a prototype that includes the proposed algorithm

Context-aware adaptation in DySCAS

DySCAS is a dynamically self-configuring middleware for automotive control systems. The addition of autonomic, context-aware dynamic configuration to automotive control systems brings a potential for a wide range of benefits in terms of robustness, flexibility, upgrading etc. However, the automotive systems represent a particularly challenging domain for the deployment of autonomics concepts, having a combination of real-time performance constraints, severe resource limitations, safety-critical aspects and cost pressures. For these reasons current systems are statically configured. This paper describes the dynamic run-time configuration aspects of DySCAS and focuses on the extent to which context-aware adaptation has been achieved in DySCAS, and the ways in which the various design and implementation challenges are met

Toward Biologically-Inspired Self-Healing, Resilient Architectures for Digital Instrumentation and Control Systems and Embedded Devices

Digital Instrumentation and Control (I&C) systems in safety-related applications of next generation industrial automation systems require high levels of resilience against different fault classes. One of the more essential concepts for achieving this goal is the notion of resilient and survivable digital I&C systems. In recent years, self-healing concepts based on biological physiology have received attention for the design of robust digital systems. However, many of these approaches have not been architected from the outset with safety in mind, nor have they been targeted for the automation community where a significant need exists. This dissertation presents a new self-healing digital I&C architecture called BioSymPLe, inspired from the way nature responds, defends and heals: the stem cells in the immune system of living organisms, the life cycle of the living cell, and the pathway from Deoxyribonucleic acid (DNA) to protein. The BioSymPLe architecture is integrating biological concepts, fault tolerance techniques, and operational schematics for the international standard IEC 61131-3 to facilitate adoption in the automation industry. BioSymPLe is organized into three hierarchical levels: the local function migration layer from the top side, the critical service layer in the middle, and the global function migration layer from the bottom side. The local layer is used to monitor the correct execution of functions at the cellular level and to activate healing mechanisms at the critical service level. The critical layer is allocating a group of functional B cells which represent the building block that executes the intended functionality of critical application based on the expression for DNA genetic codes stored inside each cell. The global layer uses a concept of embryonic stem cells by differentiating these type of cells to repair the faulty T cells and supervising all repair mechanisms. Finally, two industrial applications have been mapped on the proposed architecture, which are capable of tolerating a significant number of faults (transient, permanent, and hardware common cause failures CCFs) that can stem from environmental disturbances and we believe the nexus of its concepts can positively impact the next generation of critical systems in the automation industry

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions