315 research outputs found

    Advanced information processing system: The Army fault tolerant architecture conceptual study. Volume 1: Army fault tolerant architecture overview

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    Digital computing systems needed for Army programs such as the Computer-Aided Low Altitude Helicopter Flight Program and the Armored Systems Modernization (ASM) vehicles may be characterized by high computational throughput and input/output bandwidth, hard real-time response, high reliability and availability, and maintainability, testability, and producibility requirements. In addition, such a system should be affordable to produce, procure, maintain, and upgrade. To address these needs, the Army Fault Tolerant Architecture (AFTA) is being designed and constructed under a three-year program comprised of a conceptual study, detailed design and fabrication, and demonstration and validation phases. Described here are the results of the conceptual study phase of the AFTA development. Given here is an introduction to the AFTA program, its objectives, and key elements of its technical approach. A format is designed for representing mission requirements in a manner suitable for first order AFTA sizing and analysis, followed by a discussion of the current state of mission requirements acquisition for the targeted Army missions. An overview is given of AFTA's architectural theory of operation

    On exploiting spare capacity in hard real-time systems.

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    Remove this page before binding. (The page number was placed before realizing that this was to be the title page.) Also, don’t forget to move the Contents and Figure

    Dynamic Reconfiguration for Adaptive Multiversion Real-Time Systems

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    時間依存データの実時間処理に関する研究

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    本文データは平成22年度国立国会図書館の学位論文(博士)のデジタル化実施により作成された画像ファイルを基にpdf変換したものである京都大学0048新制・課程博士博士(工学)甲第7850号工博第1830号新制||工||1141(附属図書館)UT51-99-G444京都大学大学院工学研究科情報工学専攻(主査)教授 上林 彌彦, 教授 岩間 一雄, 教授 湯淺 太一学位規則第4条第1項該当Doctor of EngineeringKyoto UniversityDFA

    Hard Real-Time Java:Profiles and Schedulability Analysis

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    Synthesis of Flexible Fault-Tolerant Schedules with Preemption for Mixed Soft and Hard Real-Time Systems

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    In this paper we present an approach for scheduling with preemption for fault-tolerant embedded systems composed of soft and hard real-time processes. We are interested to maximize the overall utility for average, most likely to happen, scenarios and to guarantee the deadlines for the hard processes in the worst case scenarios. In many applications, the worst-case execution times of processes can be much longer than their average execution times. Thus, designs for the worst-case can be overly pessimistic, i.e., result in low overall utility. We propose preemption of process executions as a method to generate flexible schedules that maximize the overall utility for the average case while guarantee timing constraints in the worst case. Our scheduling algorithms determine off-line when to preempt and when to resurrect processes. The experimental result

    Design, Implementation, and Evaluation of a Distributed Real-Time Kernel for Distributed Robotics (Dissertation Proposal)

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    Modern robotics applications are becoming more complex due to greater numbers of sensors and actuators. The control of such systems may require multiple processors to meet the computational demands and to support the physical topology of the sensors and actuators. A distributed real-time system is needed to perform the required communication and processing while meeting application-specified timing constraints. We are designing and implementing a real-time kernel for distributed robotics applications. The kernel\u27s salient features are consistent, user-definable scheduling, explicit dynamic timing constraints, and a two-tiered interrupt approach. The kernel wi1l be evaluated by implementing a two-arm robot control example. Its goal is to locate and manipulate cylindrical objects with spillable contents. Using the application and the kernel, we will investigate the effects of time granularity, network type and protocol, and the handling of external events using interrupts versus polling. Our research will enhance understanding of real-time kernels for distributed robotics control

    Exploiting Soft Computing for Real time performance

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    The classic approach to the design of real time systems is to determine worst-case scenarios for the system statically and manually and then build the system with sufficient resources to meet deadlines and goals. This approach has worked well for traditional real time systems which operate in relatively simple, well-characterized environments. However the emerging generation of complex, dynamic and uncertain real time application domains accentuates the growing need for flexible, adaptable design approaches for real time systems. With the increasing complexity of real time systems, it is becoming infeasible to build systems with sufficient resources to meet the functional and timing requirements of all application tasks at all times. What is becoming increasingly important in the new paradigm of real time computing is the need to meet deadlines with sufficient system solution quality without having to design the system to support worst case program execution. In this thesis, we explore the possibility of exploiting "soft computing" properties of kernels to meet this objective. The chief characteristic of "soft computations" is the fact that they are able to provide cruder results before they complete, or they may execute for a long time refining an already adequate result. In other words, such computations are able to provide useful/incremental results before fully completing execution. More specifically they provide a trade-off between computation time and algorithm solution quality. This thesis addresses the design issues involved in building a system that exploits the "soft computing" properties of kernels to optimize real time performance. In this context, we make the following contributions. Firstly we build a system prototype of a real time situational assessment scenario. We thereafter identify "soft computations" in the system and characterize the computation time/solution quality trade-off opportunities provided by them using performance profiles. Thirdly, we introduce a method to use performance profile based models at run time to determine the optimal composition of different "soft computations" in order to meet real time deadlines with sufficient system solution quality. We quantify the gains from our method both in terms of functional correctness of the system as well as CPU utilization as compared to conventional real time scheduling techniques. We observe that our dynamic scheduling scheme on an average is able to meet the system goals with 39% more accuracy with no missed deadlines as compared to conventional real time scheduling techniques for various design points that do not support worst case behavior. In addition, our method is able to meet the system objective while being highly utilized. Most importantly, our scheme exploits the soft computing properties of kernels to facilitate the design of the system at less aggressive design points while meeting deadlines and system goals at the same level as conventional real time design methodology. Finally, we perform an experimental study to understand the sensitivity of performance profiles to various input data parameters and identify the potential for online learning of performance profiles

    WCET and Priority Assignment Analysis of Real-Time Systems using Search and Machine Learning

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    Real-time systems have become indispensable for human life as they are used in numerous industries, such as vehicles, medical devices, and satellite systems. These systems are very sensitive to violations of their time constraints (deadlines), which can have catastrophic consequences. To verify whether the systems meet their time constraints, engineers perform schedulability analysis from early stages and throughout development. However, there are challenges in obtaining precise results from schedulability analysis due to estimating the worst-case execution times (WCETs) and assigning optimal priorities to tasks. Estimating WCET is an important activity at early design stages of real-time systems. Based on such WCET estimates, engineers make design and implementation decisions to ensure that task executions always complete before their specified deadlines. However, in practice, engineers often cannot provide a precise point of WCET estimates and they prefer to provide plausible WCET ranges. Task priority assignment is an important decision, as it determines the order of task executions and it has a substantial impact on schedulability results. It thus requires finding optimal priority assignments so that tasks not only complete their execution but also maximize the safety margins from their deadlines. Optimal priority values increase the tolerance of real-time systems to unexpected overheads in task executions so that they can still meet their deadlines. However, it is a hard problem to find optimal priority assignments because their evaluation relies on uncertain WCET values and complex engineering constraints must be accounted for. This dissertation proposes three approaches to estimate WCET and assign optimal priorities at design stages. Combining a genetic algorithm and logistic regression, we first suggest an automatic approach to infer safe WCET ranges with a probabilistic guarantee based on the worst-case scheduling scenarios. We then introduce an extended approach to account for weakly hard real-time systems with an industrial schedule simulator. We evaluate our approaches by applying them to industrial systems from different domains and several synthetic systems. The results suggest that they are possible to estimate probabilistic safe WCET ranges efficiently and accurately so the deadline constraints are likely to be satisfied with a high degree of confidence. Moreover, we propose an automated technique that aims to identify the best possible priority assignments in real-time systems. The approach deals with multiple objectives regarding safety margins and engineering constraints using a coevolutionary algorithm. Evaluation with synthetic and industrial systems shows that the approach significantly outperforms both a baseline approach and solutions defined by practitioners. All the solutions in this dissertation scale to complex industrial systems for offline analysis within an acceptable time, i.e., at most 27 hours
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