Optimal Time Utility Based Scheduling Policy Design for Cyber-Physical Systems

Classical scheduling abstractions such as deadlines and priorities do not readily capture the complex timing semantics found in many real-time cyber-physical systems. Time utility functions provide a necessarily richer description of timing semantics, but designing utility-aware scheduling policies using them is an open research problem. In particular, optimal utility accrual scheduling design is needed for real-time cyber-physical domains. In this paper we design optimal utility accrual scheduling policies for cyber-physical systems with periodic, non-preemptable tasks that run with stochastic duration. These policies are derived by solving a Markov Decision Process formulation of the scheduling problem. We use this formulation to demonstrate that our technique improves on existing heuristic utility accrual scheduling policies

Design and analysis of target-sensitive real-time systems

A significant number of real-time control applications include computational activities where the results have to be delivered at precise instants, rather than within a deadline. The performance of such systems significantly degrades if outputs are generated before or after the desired target time. This work presents a general methodology that can be used to design and analyze target-sensitive applications in which the timing parameters of the computational activities are tightly coupled with the physical characteristics of the system to be controlled. For the sake of clarity, the proposed methodology is illustrated through a sample case study used to show how to derive and verify real-time constraints from the mission requirements. Software implementation issues necessary to map the computational activities into tasks running on a real-time kernel are also discussed to identify the kernel mechanisms necessary to enforce timing constraints and analyze the feasibility of the application. A set of experiments are finally presented with the purpose of validating the proposed methodology

Resource management in heterogeneous computing systems with tasks of varying importance

2014 Summer.The problem of efficiently assigning tasks to machines in heterogeneous computing environments where different tasks can have different levels of importance (or value) to the computing system is a challenging one. The goal of this work is to study this problem in a variety of environments. One part of the study considers a computing system and its corresponding workload based on the expectations for future environments of Department of Energy and Department of Defense interest. We design heuristics to maximize a performance metric created using utility functions. We also create a framework to analyze the trade-offs between performance and energy consumption. We design techniques to maximize performance in a dynamic environment that has a constraint on the energy consumption. Another part of the study explores environments that have uncertainty in the availability of the compute resources. For this part, we design heuristics and compare their performance in different types of environments

Resource management for extreme scale high performance computing systems in the presence of failures

2018 Summer.Includes bibliographical references.High performance computing (HPC) systems, such as data centers and supercomputers, coordinate the execution of large-scale computation of applications over tens or hundreds of thousands of multicore processors. Unfortunately, as the size of HPC systems continues to grow towards exascale complexities, these systems experience an exponential growth in the number of failures occurring in the system. These failures reduce performance and increase energy use, reducing the efficiency and effectiveness of emerging extreme-scale HPC systems. Applications executing in parallel on individual multicore processors also suffer from decreased performance and increased energy use as a result of applications being forced to share resources, in particular, the contention from multiple application threads sharing the last-level cache causes performance degradation. These challenges make it increasingly important to characterize and optimize the performance and behavior of applications that execute in these systems. To address these challenges, in this dissertation we propose a framework for intelligently characterizing and managing extreme-scale HPC system resources. We devise various techniques to mitigate the negative effects of failures and resource contention in HPC systems. In particular, we develop new HPC resource management techniques for intelligently utilizing system resources through the (a) optimal scheduling of applications to HPC nodes and (b) the optimal configuration of fault resilience protocols. These resource management techniques employ information obtained from historical analysis as well as theoretical and machine learning methods for predictions. We use these data to characterize system performance, energy use, and application behavior when operating under the uncertainty of performance degradation from both system failures and resource contention. We investigate how to better characterize and model the negative effects from system failures as well as application co-location on large-scale HPC computing systems. Our analysis of application and system behavior also investigates: the interrelated effects of network usage of applications and fault resilience protocols; checkpoint interval selection and its sensitivity to system parameters for various checkpoint-based fault resilience protocols; and performance comparisons of various promising strategies for fault resilience in exascale-sized systems

Escalonamento baseado em intervalo de tempo

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica.Esta tese apresenta um novo modelo de tarefas para expressar requisitos temporais que não podem ser facilmente representados em termos de deadlines e períodos. Neste modelo, tarefas são divididas em segmentos A, B e C. O segmento A é responsável por realizar algumas computações e após seu término explicitar o intervalo de tempo dentro do qual o segmento B deve executar para cumprir alguns requisitos de aplicação. Finalmente, após a execução de B o segmento C é liberado para executar. A execução do segmento B é válida se realizada dentro daquele intervalo de tempo; caso contrário, sua contribuição pode ser considerada sem valor para sua tarefa. O modelo utiliza funções benefício para indicar quando a ação deve ser executada para obtenção do máximo benefício. Soluções da literatura de tempo real são adaptadas e integradas para produzir uma solução de escalonamento para este problema. Como resultado, foram criadas algumas abordagens (síncronas e assíncronas) desenvolvidas especificamente para o modelo. Testes de escalonabilidade offline foram desenvolvidos para cada abordagem. Estes testes, além de um resposta aceita/rejeita, fornecem um limite inferior e superior para a qualidade que será obtida pelo segmento B em tempo de execução. No decorrer do trabalho, foram realizadas diversas contribuições à área de tempo real, em específico na área de algoritmos de atribuição de prioridades, redução do pessimismo no tempo de resposta de segmentos não preemptivos e na análise de melhor momento de liberação para os segmentos B