DeSyRe: on-Demand System Reliability

The DeSyRe project builds on-demand adaptive and reliable Systems-on-Chips (SoCs). As fabrication technology scales down, chips are becoming less reliable, thereby incurring increased power and performance costs for fault tolerance. To make matters worse, power density is becoming a significant limiting factor in SoC design, in general. In the face of such changes in the technological landscape, current solutions for fault tolerance are expected to introduce excessive overheads in future systems. Moreover, attempting to design and manufacture a totally defect and fault-free system, would impact heavily, even prohibitively, the design, manufacturing, and testing costs, as well as the system performance and power consumption. In this context, DeSyRe delivers a new generation of systems that are reliable by design at well-balanced power, performance, and design costs. In our attempt to reduce the overheads of fault-tolerance, only a small fraction of the chip is built to be fault-free. This fault-free part is then employed to manage the remaining fault-prone resources of the SoC. The DeSyRe framework is applied to two medical systems with high safety requirements (measured using the IEC 61508 functional safety standard) and tight power and performance constraints

Special Session on Industry 4.0

No abstract available

A study of influential factors in designing self-reconfigurable robots for green manufacturing

© 2018 ACIS2018.org. All rights reserved. There is incremental growth in adopting self-reconfigurable robots in automating manufacturing conventional product lines. Using this class of robots adapting themselves with ever-changing environmental conditions has been acclaimed as a promising way of reducing energy consumption and environmental impact and thus enabling green manufacturing. Whilst the majority of existing research focuses on highlighting the efficacy of self-reconfigurable robots in energy reduction with technical driven solutions, the research on exploring the salient factors in design and development self-reconfigurable robots that directly enable or hinder green manufacturing is non-extant. This interdisciplinary research contributes to the nascent body of the knowledge by empirical investigation of design-time, run-time, and hardware aspects which should be contingently balanced when developing green-aware self-reconfigurable robots

Achieving Autonomic Computing through the Use of Variability Models at Run-time

Increasingly, software needs to dynamically adapt its behavior at run-time in response to changing conditions in the supporting computing infrastructure and in the surrounding physical environment. Adaptability is emerging as a necessary underlying capability, particularly for highly dynamic systems such as context-aware or ubiquitous systems. By automating tasks such as installation, adaptation, or healing, Autonomic Computing envisions computing environments that evolve without the need for human intervention. Even though there is a fair amount of work on architectures and their theoretical design, Autonomic Computing was criticised as being a \hype topic" because very little of it has been implemented fully. Furthermore, given that the autonomic system must change states at runtime and that some of those states may emerge and are much less deterministic, there is a great challenge to provide new guidelines, techniques and tools to help autonomic system development. This thesis shows that building up on the central ideas of Model Driven Development (Models as rst-order citizens) and Software Product Lines (Variability Management) can play a signi cant role as we move towards implementing the key self-management properties associated with autonomic computing. The presented approach encompass systems that are capable of modifying their own behavior with respect to changes in their operating environment, by using variability models as if they were the policies that drive the system's autonomic recon guration at runtime. Under a set of recon guration commands, the components that make up the architecture dynamically cooperate to change the con guration of the architecture to a new con guration. This work also provides the implementation of a Model-Based Recon guration Engine (MoRE) to blend the above ideas. Given a context event, MoRE queries the variability models to determine how the system should evolve, and then it provides the mechanisms for modifying the system.Cetina Englada, C. (2010). Achieving Autonomic Computing through the Use of Variability Models at Run-time [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7484Palanci

A survey of self-management in dynamic software architecture specifications

As dynamic software architecture use becomes more widespread, a variety of formal specification languages have been developed to gain a better understanding of the foundations of this type of software evolutionary change. In this paper we survey 14 formal specification approaches based on graphs, process algebras, logic, and other formalisms. Our survey will evaluate the ability of each approach to specify self-managing systems as well as the ability to address issues regarding expressiveness and scalability. Based on the results of our survey we will provide recommendations on future directions for improving the specification of dynamic software architectures, specifically self-managed architectures