Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

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

Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 1

This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines

Grid Global Behavior Prediction

Complexity has always been one of the most important issues in distributed computing. From the first clusters to grid and now cloud computing, dealing correctly and efficiently with system complexity is the key to taking technology a step further. In this sense, global behavior modeling is an innovative methodology aimed at understanding the grid behavior. The main objective of this methodology is to synthesize the grid's vast, heterogeneous nature into a simple but powerful behavior model, represented in the form of a single, abstract entity, with a global state. Global behavior modeling has proved to be very useful in effectively managing grid complexity but, in many cases, deeper knowledge is needed. It generates a descriptive model that could be greatly improved if extended not only to explain behavior, but also to predict it. In this paper we present a prediction methodology whose objective is to define the techniques needed to create global behavior prediction models for grid systems. This global behavior prediction can benefit grid management, specially in areas such as fault tolerance or job scheduling. The paper presents experimental results obtained in real scenarios in order to validate this approach