Real-time worst-case temperature analysis with temperature-dependent parameters

With the evolution of today's semiconductor technology, chip temperature increases rapidly mainly due to the growth in power density. Therefore, for modern embedded real-time systems it is crucial to estimate maximal temperatures early in the design in order to avoid burnout and to guarantee that the system can meet its real-time constraints. This paper provides answers to a fundamental question: What is the worst-case peak temperature of a real-time embedded system under all feasible scenarios of task arrivals? Anovel thermal-aware analytic framework is proposed that combines a general event/resource model based on network and real-time calculus with system thermal equations. This analysis framework has the capability to handle a broad range of uncertainties in terms of task execution times, task invocation periods, jitter in task arrivals, and resource availability. The considered model takes both dynamic and leakage power as well as thermal dependent conductivity into consideration. Thorough simulation experiments validate the theoretical result

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: Load and Resource Models Admission Control Feedback-based Allocation and Optimisation Search-based Allocation Heuristics Distributed Allocation based on Swarm Intelligence Value-Based Allocation Each of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments.Note.-- EUR 6,000 BPC fee funded by the EC FP7 Post-Grant Open Access Pilo

Beyond the Weakly Hard Model: Measuring the Performance Cost of Deadline Misses

Most works in schedulability analysis theory are based on the assumption that constraints on the performance of the application can be expressed by a very limited set of timing constraints (often simply hard deadlines) on a task model. This model is insufficient to represent a large number of systems in which deadlines can be missed, or in which late task responses affect the performance, but not the correctness of the application. For systems with a possible temporary overload, models like the m-K deadline have been proposed in the past. However, the m-K model has several limitations since it does not consider the state of the system and is largely unaware of the way in which the performance is affected by deadline misses (except for critical failures). In this paper, we present a state-based representation of the evolution of a system with respect to each deadline hit or miss event. Our representation is much more general (while hopefully concise enough) to represent the evolution in time of the performance of time-sensitive systems with possible time overloads. We provide the theoretical foundations for our model and also show an application to a simple system to give examples of the state representations and their use