29,745 research outputs found

    Extending snBench to Support Hierarchical and Configurable Scheduling

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    It is useful in systems that must support multiple applications with various temporal requirements to allow application-specific policies to manage resources accordingly. However, there is a tension between this goal and the desire to control and police possibly malicious programs. The Java-based Sensor Execution Environment (SXE) in snBench presents a situation where such considerations add value to the system. Multiple applications can be run by multiple users with varied temporal requirements, some Real-Time and others best effort. This paper outlines and documents an implementation of a hierarchical and configurable scheduling system with which different applications can be executed using application-specific scheduling policies. Concurrently the system administrator can define fairness policies between applications that are imposed upon the system. Additionally, to ensure forward progress of system execution in the face of malicious or malformed user programs, an infrastructure for execution using multiple threads is described

    A C-DAG task model for scheduling complex real-time tasks on heterogeneous platforms: preemption matters

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    Recent commercial hardware platforms for embedded real-time systems feature heterogeneous processing units and computing accelerators on the same System-on-Chip. When designing complex real-time application for such architectures, the designer needs to make a number of difficult choices: on which processor should a certain task be implemented? Should a component be implemented in parallel or sequentially? These choices may have a great impact on feasibility, as the difference in the processor internal architectures impact on the tasks' execution time and preemption cost. To help the designer explore the wide space of design choices and tune the scheduling parameters, in this paper we propose a novel real-time application model, called C-DAG, specifically conceived for heterogeneous platforms. A C-DAG allows to specify alternative implementations of the same component of an application for different processing engines to be selected off-line, as well as conditional branches to model if-then-else statements to be selected at run-time. We also propose a schedulability analysis for the C-DAG model and a heuristic allocation algorithm so that all deadlines are respected. Our analysis takes into account the cost of preempting a task, which can be non-negligible on certain processors. We demonstrate the effectiveness of our approach on a large set of synthetic experiments by comparing with state of the art algorithms in the literature

    Planning, scheduling, and control for automatic telescopes

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    This paper presents an argument for the appropriateness of Entropy Reduction Engine (ERE) technology to the planning, scheduling, and control components of Automatic Photoelectric Telescope (APT) management. The paper is organized as follows. In the next section, we give a brief summary of the planning and scheduling requirements for APTs. Following this, in section 3, we give an ERE project precis, couched primarily in terms of project objectives. Section 4 gives a sketch of the match-up between problem and technology, and section 5 outlines where we want to go with this work

    Energy Efficient Scheduling of MapReduce Jobs

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    MapReduce is emerged as a prominent programming model for data-intensive computation. In this work, we study power-aware MapReduce scheduling in the speed scaling setting first introduced by Yao et al. [FOCS 1995]. We focus on the minimization of the total weighted completion time of a set of MapReduce jobs under a given budget of energy. Using a linear programming relaxation of our problem, we derive a polynomial time constant-factor approximation algorithm. We also propose a convex programming formulation that we combine with standard list scheduling policies, and we evaluate their performance using simulations.Comment: 22 page

    The APT/ERE planning and scheduling manifesto

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    The Entropy Reduction Engine, ERE project, is focusing on the construction of integrated planning and scheduling systems. Specifically, the project is studying the problem of integrating planning and scheduling in the context of the closed loop plan use. The results of this research are particularly relevant when there is some element of dynamism in the environment, and thus some chance that a previously formed plan will fail. After a preliminary study of the APT management and control problem, it was felt that it presents an excellent opportunity to show some of the ERE Project's technical results. Of course, the alignment between technology and problem is not perfect, so planning and scheduling for APTs presents some new and difficult challenges as well
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