Quantitative Characterization of Event Streams in Analysis of Hard Real-Time Applications

Many real-time embedded systems process event streams that are composed of a finite number of different event types. Each different event type on the stream would typically impose a different workload to the system, and thus the knowledge of possible correlations and dependencies between the different event types could be exploited to get tighter analytic performance bounds of the complete system. We propose an abstract stream model to characterize such an event stream. The model captures the needed information of all possible traces of a class of event streams. Hence, it can be used to obtain hard bounded worst-case and best-case analysis results of a system. We show how the proposed abstract stream model can be obtained from a concrete stream specification, and how it can be used for performance analysis. The applicability of our approach and its advantages over traditional worst-case performance analysis are shown in a case study of a multimedia application.Ernesto Wandeler is a Ph.D. student at the Computer Engineering and Networks Laboratory of the Swiss Federal Institute of Technology, Zurich. His research interests include models, methods and tools for system-level performance analysis of real-time embedded systems. He holds a Dipl. El.-Ing. degree from ETH Zurich. In 2003, he received the Willi Studer Price and the ETH Medal, both from the Swiss Federal Institute of Technology, Zurich. He is a student member of the IEEE and the ACM.Alexander Maxiaguine is a Ph.D. student at the Computer Engineering and Networks Laboratory of the Swiss Federal Institute of Technology, Zurich. His research interests include models and methods for system-level performance analysis and scheduling of embedded multiprocessor architectures, especially for real-time multimedia applications. Maxiaguine has an M.S. in electrical engineering from the Moscow Technical University of Communications and Informatics. He is a member of the IEEE and the ACM.Lothar Thiele is a full professor of computer engineering at the Swiss Federal Institute of Technology, Zurich. His research interests include models, methods and software tools for the design of embedded systems, embedded software and bioinspired optimization techniques. In 1986 he received the Dissertation Award of the Technical University of Munich, in 1987, the Outstanding Young Author Award of the IEEE Circuits and Systems Society, in 1988, the Browder J. Thompson Memorial Award of the IEEE, and in 2000–2001, the IBM Faculty Partnership Award. In 2004, he joined the German Academy of Natural Scientists Leopoldina.  

城市交通网络信号控制系统的实时演算模型

基于实时演算(Real-Time Calculus: RTC)理论,为单/双行道两类城市交通网络的定时和自适应两类信号控制系统建立了统一的形式化模型.首先,将车流和交叉路口分别建模为RTC的到达曲线和资源曲线.然后,根据不同信号控制策略,将紧邻路口间的曲线进行综合计算,得到整个交通网络的RTC模型.应用最小加代数方法,RTC模型能够计算车辆在路口的最长等待时间D和路口拥堵车队的最大长度B.基于RTC模型,应用MATLAB对8组不同规模的城市交通网格进行仿真,实验结果表明:1）与双行道网络相比,单行道网络更能有效处理较稀疏的交通流.以定时控制为例,在车流频率u≤1/2时,单行道网络能够将交通拥堵指标D和B分别降低至少2.66倍和3倍.2)双行道网络中,车流频率u存在一个临界区域.在临界域内,拥堵指标随车流频率递增变化;一旦u低于或超出临界域,拥堵指标则分别保持稳定不变或不可控.3)自适应策略优于定时控制策略.例如在双行道网络中,自适应控制策略对应的拥塞指标D和B比定时控制策略分别降低1.68倍和1.26倍.