On the periodic behavior of real-time schedulers on identical multiprocessor platforms

This paper is proposing a general periodicity result concerning any deterministic and memoryless scheduling algorithm (including non-work-conserving algorithms), for any context, on identical multiprocessor platforms. By context we mean the hardware architecture (uniprocessor, multicore), as well as task constraints like critical sections, precedence constraints, self-suspension, etc. Since the result is based only on the releases and deadlines, it is independent from any other parameter. Note that we do not claim that the given interval is minimal, but it is an upper bound for any cycle of any feasible schedule provided by any deterministic and memoryless scheduler

Configuração de mudanças de modo em sistemas de tempo real escalonados com política preemptiva de prioridade fixa

Orientadores: Paulo Sérgio Martins Pedro, Edson Luiz UrsiniDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de TecnologiaResumo: Modos de operação e mudanças de modo são uma abstração útil para permitir que sistemas de tempo real sejam flexíveis e configuráveis. Trabalhos prévios em escalonamento preemptivo com prioridades fixas permitem que as tarefas passem de um modo de operação para outro provendo garantias de tempo real. No entanto, a configuração adequada dos parâmetros críticos, tais como o offset de uma tarefa, apesar de trabalhos anteriores terem abordado este assunto, permanece uma lacuna a ser explorada. Sem um método que automatize esta etapa do processo, garantindo ao mesmo tempo que os requisitos básicos sejam atendidos, a adoção plena de mudanças de modo em sistemas de tempo real permanece limitada a sistemas relativamente simples, com um conjuntos de tarefas limitado. Propomos um método para atribuir offsets às tarefas em uma mudança modo, através de uma abordagem Metaheurística (algoritmos genéticos). Este método permite a configuração e/ou a minimização da latência de pior caso de uma mudança modo. A latência de uma mudança de modo é um parâmetro crítico para ser minimizado, uma vez que durante a mudança de modo o sistema oferece funcionalidade limitada, uma vez que o conjunto de tarefas está parcialmente em operação. Também elaboramos uma classificação das mudanças de modo de acordo com as necessidades das aplicações. Esta classificação, quando aplicada a uma série de estudos de casos, permitiu validar a abordagem de minimização/configuração, estender a classificação anteriormente existente e demonstrar que o método é flexível, já que pode acomodar uma ampla variedade de tipos de mudanças de modoAbstract: Modes of operation and mode-changes are a useful abstraction to enable configurable, flexible real-time systems. Substantial work on the fixed priority preemptive scheduling approach allowed tasks across a mode-change to be provided with real-time guarantees. However, the proper configuration of critical parameters such as task offsets, despite initial work, remains a gap in research. Without a method that automates this design step, while assuring that the basic requirements are met, the full adoption of mode-changes in real-time systems remains limited to relatively simple systems with limited task sets. We propose a method to assign offsets to tasks across a mode-change, using a metaheuristic approach (genetic algorithms). This method allows the configuration and/or the minimization of the worst-case latency of a mode-change. The latency of a mode change is a critical parameter to be minimized, since during the mode change the system offers limited functionality due to the fact that the task set is still incomplete. We also provide a classification of mode changes according to applications¿ requirements. This classification was useful, once applied to a number of case studies, both to validate the configuration approach and to a greater extent to show that the method is flexible in that it can accommodate a wide variety of types of mode-changesMestradoMestre em Tecnologi

Merging Real-Time and Control Theory for Improving the Performance of Embedded Control Systems

This report describes the work carried out within the research project ``Merging Real-Time and Control Theory for Improving the Performance of Embedded Control Systems''. The overall objective of the work has been to develop integrated control and scheduling methods for improving the performance of real-time control systems with limited resources. The work has fallen into three categories. First, overrun methods for control tasks has been investigated. Specifically, a reservation-based scheduling concept called the Control Server has been further developed, and control experiments on a ball-and-place process have been performed. Second, the issue of jitter in real-time control systems has been explored. The concept of Jitter Margin has been introduced as a link between control stability theory and scheduling theory. In this context, best-case response-time analysis under earliest-deadline-first scheduling has been researched. Third, some development work on the S.Ha.R.K. real-time kernel has been performed. The rate-monotonic and earliest-deadline-first scheduling modules have been extended, and new modules for the elastic task model and the control server model have been implemented

Influence of different abstractions on the performance analysis of distributed hard real-time systems

System level performance analysis plays a fundamental role in the design process of hard real-time embedded systems. Several different approaches have been presented so far to address the problem of accurate performance analysis of distributed embedded systems in early design stages. The existing formal analysis methods are based on essentially different concepts of abstraction. However, the influence of these different models on the accuracy of the system analysis is widely unknown, as a direct comparison of performance analysis methods has not been considered so far. We define a set of benchmarks aimed at the evaluation of performance analysis techniques for distributed systems. We apply different analysis methods to the benchmarks and compare the results obtained in terms of accuracy and analysis times, highlighting the specific effects of the various abstractions. We also point out several pitfalls for the analysis accuracy of single approaches and investigate the reasons for pessimistic performance prediction

A Heuristic Method for Task Selection in Persistent ISR Missions Using Autonomous Unmanned Aerial Vehicles

The Persistent Intelligence, Surveillance, and Reconnaissance (PISR) problem seeks to provide timely collection and delivery of data from prioritized ISR tasks using an autonomous Unmanned Aerial Vehicle (UAV). In the literature, PISR is classified as a type of Vehicle Routing Problem (VRP), often called by other names such as persistent monitoring, persistent surveillance, and patrolling. The objective of PISR is to minimize the weighted revisit time to each task (called weighted latency) using an optimal task selection algorithm. In this research, we utilize the average weighted latency as our performance metric and investigate a method for task selection called the Maximal Distance Discounted and Weighted Revisit Period (MD2WRP) utility function. The MD2WRP function is a heuristic method of task selection that uses n+1 parameters, where n is the number of PISR tasks. We develop a two-step optimization method for the MD2WRP parameters to deliver optimal latency performance for any given task configuration, which accommodates both single and multi-vehicle scenarios. To validate our optimization method, we compare the performance of MD2WRP to common Traveling Salesman Problem (TSP) methods for PISR using different task configurations. We find that the optimized MD2WRP function is competitive with the TSP methods, and that MD2WRP often results in steady-state task visit sequences that are equivalent to the TSP solution for a single vehicle. We also compare MD2WRP to other utility methods from the literature, finding thatMD2WRP performs on par with or better than these other methods even when optimizing only one of its n + 1 parameters. To address real-world operational factors, we test MD2WRP with Dubins constraints, no-y zones in the operational area, return-to-base requirements, and the addition and removal of vehicles and tasks mid-mission. For each operational factor, we demonstrate its effect on PISR task selections using MD2WRP and how MD2WRP needs to be modified, if at all, to compensate. Finally, we make practical suggestions about implementing MD2WRP for flight testing, outline potential areas for future study, and offer recommendations about the conduct of PISR missions in general

Fixed-Priority Scheduling Algorithms with Multiple Objectives in Hard Real-Time Systems

In the context ofFixed-Priority Scheduling in Real-Time Systems, we investigate scheduling mechanisms for supporting systems where, in addition to timing constraints, their performance with respect to additional QoS requirements must be improved. This'type of situation may occur when the worst-case res~urce requirements of all or some running tasks cannot be simultaneously met due to task contention. . Solutions to these problems have been proposed in the context of both fixed-priority and dynamic-priority scheduling. In fixed-priority scheduling, the typical approach is to artificially modify the attributes or structure of tasks, and/or usually require non-standard run-time support. In dynamic-priority scheduling approaches, utility functions are employed to make scheduling decisions with the objective of maximising the utility. The main difficulties with these approaches are the inability to formulate and model appropriately utility functions for each task, and the inability to guarantee hard deadlines without executing computationally costly algorithms. In this thesis we propose a different approach. Firstly, we introduce the concept of relative importance among tasks as a new metric for expressing QoS requirements. The meaning of this importance relationship is to express that in a schedule it i~ desirable to run a task in preference to other ones. This model is more intuitive and less restrictive than traditional utility-based app~oaches. Secondly, we formulate a scheduling problem in terms of finding a feasible assignment of fixed priorities that maximises the new QoS metric, and propose the DI and DI+ algorithms that find optimal solutions. By extensive simulation, we show that the new QoS metric combined with the DI algorithm outperforms the rate monotonic priority algorithm in several practical problems such as minimising jitter, minimising the number of preemptions or minimising the latency. In addition, our approach outperforms EDF in several scenarios

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

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 AllocationEach of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments