Arne Hamann

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

System level performance analysis plays a fundamental role in the design process of 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 predictions.

A framework for modular analysis and exploration of heterogeneous embedded systems

The increasing complexity of heterogeneous systems-on-chip, SoC, and distributed embedded systems makes system optimization and exploration a challenging task. Ideally, a designer would try all possible system configurations and choose the best one regarding specific system requirements. Unfortunately, such an approach is not possible because of the tremendous number of design parameters with sophisticated effects on system properties. Consequently, good search techniques are needed to find design alternatives that best meet constraints and cost criteria. In this paper, we present a compositional design space exploration framework for system optimization and exploration using SymTA/S, a software tool for formal performance analysis. In contrast to many previous approaches pursuing closed automated exploration strategies over large sets of system parameters, our approach allows the designer to effectively control the exploration process to quickly find good design alternatives. An important aspect and key novelty of our approach is system optimization with traffic shaping.

Design space exploration and system optimization with SymTA/S - symbolic timing analysis for systems

The increasing complexity of heterogeneous SoC and distributed systems confronts the system designer with problems how to determine reasonable design alternatives leading to well functioning systems. Ideally, a designer would try all possible system configuration and choose the best one regarding specific system requirements. Unfortunately, such an approach is not possible because the high number of design parameters in complex systems leads to a very large design-space, prohibiting an exhaustive search. Consequently, good search techniques are needed to find optimal, or at least good, design alternatives. In this paper, we present a design space exploration framework for system optimization using SymTA/S, a software tool for formal performance analysis. In contrast to many previous approaches, our approach takes the hierarchical structure of the design space of heterogeneous SoC and distributed systems into account, allowing the designer to control the exploration process. A main technique in our approach is systematic system optimization using traffic shaping.

TDMA Time Slot and Turn Optimization with Evolutionary Search Techniques

In this paper we present arithmetic real-coded variation operators tailored for time slot and turn optimization on TDMA-scheduled resources with evolutionary algorithms. Our operators implement an heuristic strategy to converge towards the solution space and are able to escape local minima. Furthermore, we explicitly separate the variation of the admitted loads and the turn-length in order to give the designer increased control over the optimization process. Experimental results show that our variation operators have advantages over string-coded binary variation operators which are frequently used to solve continuous optimization problems.

Combined approach to system level performance analysis of embedded systems

Compositional approaches to system-level performance analysis have shown great flexibility and scalability in the design of heterogeneous systems. These approaches often assume certain system architectures and application domains, and are thus tailored to give tight analysis results for specific systems. We consider two different compositional analysis methods. Both methods have their particular strengths for different architectures and applications. In this paper, we aim to enhance the analysis capabilities for these techniques. A method for event model conversion allows us a seamless integration of the two methods. Finally, we present a detailed case study to show the applicability and benefits of the enhanced performance analysis technique.

A formal approach to robustness maximization of complex heterogeneous embedded systems

Embedded system optimization typically considers objectives such as cost, timing, buffer sizes and power consumption. Robustness criteria, i.e. sensitivity of the system to variations of properties like execution and transmission delays, input data rates, CPU clock rates, etc., has found less attention despite its practical relevance. In this paper we introduce robustness metrics and propose an algorithm considering these metrics in design space exploration and system optimization. The algorithm can optimize for static and for dynamic robustness, the latter including system or designer reactions to property variations. We explain several applications ranging from platform optimization to critical component identification. By means of extensive experiments we show that design space exploration pursuing classical design goals does not necessarily yield robust systems, and that our method leads to systems with significantly higher design robustness.

Multi-dimensional Robustness Optimization in Heterogeneous Distributed Embedded Systems

Embedded system optimization typically considers objectives such as cost, timing, buffer sizes, and power consumption. Robustness criteria, i.e. sensitivity of the system to property variations like execution and transmission delays, input data rates, CPU clock rates, etc., has found less attention despite its practical relevance. In this paper we present an approach for optimizing multidimensional robustness criteria in complex distributed embedded systems. The key novelty of our approach is a scalable stochastic multi-dimensional sensitivity analysis technique approximating the sought-after sensitivity front from two sides, i.e. coming from the space of working and from the space of non-working system property combinations. We utilize the proposed stochastic sensitivity analysis to derive multi-dimensional robustness metrics, which are capable of bounding the robustness of given system configurations with little computational effort. The proposed metrics can significantly speed up multidimensional robustness optimization by quickly identifying promising system configurations, whose in-depth robustness evaluation can be performed subsequently to the optimization process

A formal approach to multi-dimensional sensitivity analysis of embedded real-time systems

System robustness is a major concern in the design of efficient and reliable state-of-the-art heterogenous embedded real-time systems. Due to complex component interactions, resource sharing and functional dependencies, one-dimensional sensitivity analysis cannot cover all effects that modifications of one system property may have on system performance. One reason is that the variation of one property can also affect the values of other system properties requiring new approaches to keep track of simultaneous parameter changes. In this paper we present a heuristic and a stochastic approach suited for the multi-dimensional sensitivity analysis of large heterogenous embedded systems with complex timing constraints

Sensitivity analysis of complex embedded real-time systems

Abstract The robustness of an architecture to changes is a major concern in the design of efficient and reliable state-of-the-art embedded real-time systems. Robustness is important during design process to identify if and in how far a system can accommodate later changes or updates, or whether it can be reused in a next generation product. In the product life-cycle, robustness helps the designer to perform changes as a result of product updates, integration of new components and subsystems, or modifications of the environment. In this paper we determine robustness as a performance reserve, the slack in performance before a system fails to meet timing requirements. This is measured as design sensitivity. Due to complex component interactions, resource sharing and functional dependencies, one-dimensional sensitivity analysis might not cover all effects that modifications of one system property may have on system performance. One reason is that the variation of one property can also affect the values of other system properties requiring new approaches to keep track of simultaneous parameter changes. In this paper we present a framework for one-dimensional and multi-dimensional sensitivity analysis of real-time systems. The framework is based on compositional analysis that is scalable to large systems. The one-dimensional sensitivity analysis combines a binary search technique with a set of formal equations derived from the real-time scheduling theory. The multi-dimensional sensitivity analysis engine consists of an exact algorithm that extends the one-dimensional approach, and a stochastic algorithm based on evolutionary search techniques.

Improved response time analysis of tasks scheduled under preemptive Round-Robin

Round-Robin scheduling is the most popular time triggered scheduling policy, and has been widely used in communication networks for the last decades. It is an efficient scheduling technique for integration of unrelated system parts, but the worst-case timing depends on the system properties in a very complex way. The existing works on response time analysis of task scheduled under Round-Robin determine very pessimistic response time bounds, without considering in detail the interactions between tasks. This may lead to a degradation of the efficiency of Round-Robin scheduling algorithm, and becomes a practical obstacle to its application in realtime systems. In this paper we present an approach to compute much tighter best-case and worst-case response time bounds of tasks scheduled under preemptive Round-Robin, including also the effects of the scheduling algorithm.