Heuristic partitioning of real-time tasks on multi-processors

This paper tackles the problem of admitting real-time tasks onto a symmetric multi-processor platform, where a partitioned EDF-based scheduler is used. We propose to combine a well-known utilization-based test for the first-fit partitioning strategy, with a simple heuristic based on the number of tasks and exact knowledge of the utilization of the first few biggest tasks. This results in an effective and efficient test improving on the state of the art in terms of admitted tasks, as shown by extensive tests performed on task sets generated using the widely adopted randfixedsum algorithm

Adaptive partitioning of real-time tasks on multiple processors

This paper presents a new algorithm for scheduling real-time tasks on multiprocessor/multicore systems. This new algorithm is based on combining EDF scheduling with a migration strategy that moves tasks only when needed. It has been evaluated through an extensive set of simulations that showed good performance when compared with global or partitioned EDF: a worst-case utilisation bound similar to partitioned EDF for hard real-time tasks, and a tardiness bound similar to global EDF for soft real-time tasks. Therefore, the proposed scheduler is effective for dealing with both soft and hard real-time workloads

Migrating Constant Bandwidth Servers on Multi-Cores

This paper introduces a novel admission test for partitioned CBS reservations on multi-core systems, that, on a new reservation arrival, is capable of exploiting better the CPU capacity in cases in which tasks have just recently left the CPU (for example, due to termination or migration to a different CPU). This is particularly useful for highly dynamic scenarios (with frequent arrivals of new tasks or leaves of existing ones) or when adaptive and possibly power-aware partitioning techniques (in which task migrations are triggered quite often to re-balance the workload among the available cores) are used

Adaptive Resource Management for Uncertain Execution Platforms

Embedded systems are becoming increasingly complex. At the same time, the components that make up the system grow more uncertain in their properties. For example, current developments in CPU design focuses on optimizing for average performance rather than better worst case performance. This, combined with presence of 3rd party software components with unknown properties, makes resource management using prior knowledge less and less feasible. This thesis presents results on how to model software components so that resource allocation decisions can be made on-line. Both the single and multiple resource case is considered as well as extending the models to include resource constraints based on hardware dynam- ics. Techniques for estimating component parameters on-line are presented. Also presented is an algorithm for computing an optimal allocation based on a set of convex utility functions. The algorithm is designed to be computationally efficient and to use simple mathematical expres- sions that are suitable for fixed point arithmetics. An implementation of the algorithm and results from experiments is presented, showing that an adaptive strategy using both estimation and optimization can outperform a static approach in cases where uncertainty is high

Managing multi-mode tasks with time cost and quality levels using optimal discrete control synthesis

International audienceReal-time control systems are complex to design, and automation support is important. We are interested in systems with multiple tasks, each with multiple modes, implementing a functionality with different levels of quality (e.g., computation approximation), and cost (e.g., computation time, energy). It is complex to control the switching of modes in order to insure properties like bounding cost while maximizing quality. We outline a technique for the automatic generation of such controllers involving an automaton-based formal model, and using optimal discrete control synthesi

WCET of OCaml Bytecode on Microcontrollers: An Automated Method and Its Formalisation

Considering the bytecode representation of a program written in a high-level programming language enables portability of its execution as well as a factorisation of various possible analyses of this program. In this article, we present a method for computing the worst-case execution time (WCET) of an embedded bytecode program fit to run on a microcontroller. Due to the simple memory model of such a device, this automated WCET computation relies only on a control-flow analysis of the program, and can be adapted to multiple models of microcontrollers. This method evaluates the bytecode program using concrete as well as partially unknown values, in order to estimate its longest execution time. We present a software tool, based on this method, that computes the WCET of a synchronous embedded OCaml program. One key contribution of this article is a mechanically checked formalisation of the aforementioned method over an idealised bytecode language, as well as its proof of correctness

Building adaptable, QoS-aware dependable embedded systems

Most of today’s embedded systems are required to work in dynamic environments, where the characteristics of the computational load cannot always be predicted in advance. Furthermore, resource needs are usually data dependent and vary over time. Resource constrained devices may need to cooperate with neighbour nodes in order to fulfil those requirements and handle stringent non-functional constraints. This paper describes a framework that facilitates the distribution of resource intensive services across a community of nodes, forming temporary coalitions for a cooperative QoSaware execution. The increasing need to tailor provided service to each application’s specific needs determines the dynamic selection of peers to form such a coalition. The system is able to react to load variations, degrading its performance in a controlled fashion if needed. Isolation between different services is achieved by guaranteeing a minimal service quality to accepted services and by an efficient overload control that considers the challenges and opportunities of dynamic distributed embedded systems