Worst-case Execution Time Analysis Methods Research Articles

Big data-driven scheduling optimization algorithm for Cyber–Physical Systems based on a cloud platform

In this paper, we study big data-driven Cyber–Physical Systems (CPS) through cloud platforms and design scheduling optimization algorithms to improve the efficiency of the system. A task scheduling scheme for large-scale factory access under cloud–edge collaborative computing architecture is proposed. The method firstly merges the directed acyclic graphs on cloud-side servers and edge-side servers; secondly, divide the tasks using a critical path-based partitioning strategy to effectively improve the allocation accuracy; then achieves load balancing through reasonable processor allocation, and finally compares and analyses the proposed task scheduling algorithm through simulation experiments. The experimental system is thoroughly analysed, hierarchically designed, and modelled, simulated, and the experimental data analysed and compared with related methods. The experimental results prove the effectiveness and correctness of the worst-case execution time analysis method and the idea of big data-driven CPS proposed in this paper and show that big data knowledge can help improve the accuracy of worst-case execution time analysis. This paper implements a big data-driven scheduling optimization algorithm for Cyber–Physical Systems based on a cloud platform, which improves the accuracy and efficiency of the algorithm by about 15% compared to other related studies.

MBBench: WCET Kıyaslama Kümesi

One of the important features of any real-time software is the worst-case execution time (WCET). To get an understanding of the timing behavior of real-time systems and to prove that the real-time software meets its deadlines, WCET analysis is performed. Today, researchers actively develop new WCET analysis methods and tools. Therefore, they need benchmark programs to evaluate and compare their work. To meet this need, in this study we present a new benchmark suite, called MBBench. MBBench includes a collection of C programs for Linux operating system and RTEMS real-time operating system. Its main aim is to help the evaluation and comparison of measurement-based WCET analysis methods/tools. MBBench has been published as open source. It can be obtained freely over the Internet.

PBench: A Parallel, Real-Time Benchmark Suite

Real-time systems are widely used from the automotive industry to the aerospace industry. The scientists, researchers, and engineers who develop real-time platforms, worst-case execution time analysis methods and tools need to compare their solutions to alternatives. For this purpose, they use benchmark applications. Today many of our computing systems are multicore and/or multiprocessor systems. Therefore, to be able to compare the effectiveness of real-time platforms, worst-case execution time analysis methods and tools, the research community need multi-threaded benchmark applications which scale on multicore and/or multiprocessor systems. In this paper, we present the first version of PBench, a parallel, real-time benchmark suite. PBench includes different types of multi-threaded applications which implement various algorithms from searching to sorting, matrix multiplication to probability distribution calculation. In addition, PBench provides single-threaded versions of all programs to allow side by side comparisons.

ACDC

In multitasking real-time systems, the worst-case execution time (WCET) of each task and also the effects of interferences between tasks in the worst-case scenario need to be calculated. This is especially complex in the presence of data caches. In this article, we propose a small instruction-driven data cache (256 bytes) that effectively exploits locality. It works by preselecting a subset of memory instructions that will have data cache replacement permission. Selection of such instructions is based on data reuse theory. Since each selected memory instruction replaces its own data cache line, it prevents pollution and performance in tasks becomes independent of the size of the associated data structures. We have modeled several memory configurations using the Lock-MS WCET analysis method. Our results show that, on average, our data cache effectively services 88% of program data of the tested benchmarks. Such results double the worst-case performance of our tested multitasking experiments. In addition, in the worst case, they reach between 75% and 89% of the ideal case of always hitting in instruction and data caches. As well, we show that using partitioning on our proposed hardware only provides marginal benefits in worst-case performance, so using partitioning is discouraged. Finally, we study the viability of our proposal in the MiBench application suite by characterizing its data reuse, achieving hit ratios beyond 90% in most programs.

A new way about using statistical analysis of worst-case execution times

In this paper, we revisit the problem of using Extreme Value Theory (EVT) in the Worst-Case Execution Time (WCET) analysis of the programs running on a single processor. Our proposed statistical WCET analysis method consists of a novel sampling mechanism tackling with some problems that hindered the application of using EVT in the context, and a statistical inference about computation of a WCET estimate of the target program. To be specific, the presented sampling mechanism takes analysis samples from the target program based around end-to-end measurements. Next, the statistical inference using EVT together with other statistical techniques, analyzes such timing traces which contain the execution time data of the program, to compute a WCET estimate with a certain predictable probability of being exceeded.

Beyond loop bounds: comparing annotation languages for worst-case execution time analysis

Worst-case execution time (WCET) analysis is concerned with computing a precise-as-possible bound for the maximum time the execution of a program can take. This information is indispensable for developing safety-critical real-time systems, e. g., in the avionics and automotive fields. Starting with the initial works of Chen, Mok, Puschner, Shaw, and others in the mid and late 1980s, WCET analysis turned into a well-established and vibrant field of research and development in academia and industry. The increasing number and diversity of hardware and software platforms and the ongoing rapid technological advancement became drivers for the development of a wide array of distinct methods and tools for WCET analysis. The precision, generality, and efficiency of these methods and tools depend much on the expressiveness and usability of the annotation languages that are used to describe feasible and infeasible program paths. In this article we survey the annotation languages which we consider formative for the field. By investigating and comparing their individual strengths and limitations with respect to a set of pivotal criteria, we provide a coherent overview of the state of the art. Identifying open issues, we encourage further research. This way, our approach is orthogonal and complementary to a recent approach of Wilhelm et al. who provide a thorough survey of WCET analysis methods and tools that have been developed and used in academia and industry.

Symbolic WCET Analysis of Programs Containing Input-Dependent Branches

Symbolic WCET (worst-case execution time) analysis yields symbolic upper bound expressions for tasks that contain parameters. Quickly evaluated at run-time, such expressions can improve the accuracy of WCET estimate. This paper proposes a symbolic WCET analysis method that is especially for the input-data dependent branches. First, the formulas described by Blieberger is expanded, so that they can express the input-data dependent branches. Simplification of the formulas using control-dependent graph yields conditional symbolic expressions that have different forms corresponding to different input value ranges. Different from the existing methods, the symbolic formulas are directly dependent on input-data, so WCET estimate evaluation at run-time is more simple and straightforward.

Clustered Worst-Case Execution-Time Calculation

Knowing the worst-case execution time (WCET) of a program is necessary when designing and verifying real-time systems. A correct WCET analysis method must take into account the possible program flow, such as loop iterations and function calls, as well as the timing effects of different hardware features, such as caches and pipelines. A critical part of WCET analysis is the calculation, which combines flow information and hardware timing information in order to calculate a program WCET estimate. The type of flow information which a calculation method can take into account highly determines the WCET estimate precision obtainable. Traditionally, we have had a choice between precise methods that perform global calculations with a risk of high computational complexity and local methods that are fast but cannot take into account all types of flow information. This paper presents an innovative hybrid method to handle complex flows with low computational complexity, but still generate safe and tight WCET estimates. The method uses flow information to find the smallest parts of a program that have to be handled as a unit to ensure precision. These units are used to calculate a program WCET estimate in a demand-driven bottom-up manner. The calculation method to use for a unit is not fixed, but could depend on the included flow and program characteristics.