Michael Zolda

Comparing different prenexing strategies for quantified Boolean formulas

The majority of the currently available solvers for quantified Boolean formulas (QBFs) process input formulas only in prenex conjunctive normal form. However, the natural representation of practicably relevant problems in terms of QBFs usually results in formulas which are not in a specific normal form. Hence, in order to evaluate such QBFs with available solvers, suitable normal-form translations are required. In this paper, we report experimental results comparing different prenexing strategies on a class of structured benchmark problems. The problems under consideration encode the evaluation of nested counterfactuals over a propositional knowledge base, and span the entire polynomial hierarchy. The results show that different prenexing strategies influence the evaluation time in different ways across different solvers. In particular, some solvers are robust to the chosen strategies while others are not.

Improving the Confidence in Measurement-Based Timing Analysis

Measurement-based timing analysis (MBTA) is a hybrid approach that combines execution-time measurements with static program analysis techniques to obtain an estimate of the worst-case execution time (WCET) of a program. The most challenging part of MBTA is test data generation. Choosing an adequate set of test vectors determines safety and efficiency of the overall analysis. So far, there are no feasible criteria that determine how well the worst-case temporal behavior of program parts is covered by a given test-suite. In this paper we introduce a relative safety metric that compares test suites with respect to how well the observed worst-case behavior of program parts is exercised. Using this metric, we empirically show that common code coverage criteria from the domain of functional testing can produce unsafe WCET estimates in the context of MBTA for systems with a processor like the TriCore 1796. Further, we use the relative safety metric to examine coverage criteria that require all feasible pairs of, e.g., basic blocks to be exercised in combination. These are shown to be superior to code coverage criteria from the domain of functional testing, but there is still a chance that an unsafe WCET estimate is derived by MBTA in our experimental setup. Based on the outcomes of our evaluation we introduce and examine Balanced Path Generation, an input data generation technique that combines the advantages of all evaluated coverage criteria and random input data generation.

Let's get less optimistic in measurement-based timing analysis

Measurement-based timing analysis (MBTA) is a hybrid approach that combines execution time measurements with static program analysis techniques to obtain an estimate of the worst-case execution time (WCET) of a program. In order to minimize the chance that the WCET estimate is below the real WCET, the set of representative execution-time measurements has to be selected advisedly. We present an input data generation technique that uses a combination of model checking and genetic algorithms in order to heuristically optimize the set of measurements in terms of safety.

Context-Sensitive Measurement-Based Worst-Case Execution Time Estimation

The goal of measurement-based WCET estimation (MBWE) is to derive an estimate of the worst-case execution time (WCET) of a given piece of software on a particular target platform by executing the software on the target hardware and analyzing the obtained time-stamped execution traces. In this paper we introduce context-sensitive MBWE, an approach that can reduce pessimism by making use of state information that is exposed through individual control-flow decisions. We show how to extend the popular IPET method, to obtain tighter WCET estimates. We provide confirmative empirical results that demonstrate the effectiveness of our approach.

Context-sensitivity in IPET for measurement-based timing analysis

The Implicit Path Enumeration Technique (IPET) has become widely accepted as a powerful technique to compute upper bounds on the Worst-Case Execution Time (WCET) of time-critical software components. While the technique works fine whenever fixed execution times can be assumed for the atomic program parts, standard IPET does not consider the context-dependence of execution times. As a result, the obtained WCET bounds can often be overly pessimistic. The issue of context-dependence has previously been addressed in the field of static timing analysis, where context-dependent execution times of program parts can be extracted from a hardware model. In the case of measurement-based execution time analysis, however, contexts must be derived from timed execution traces. In the present extended abstract we present an overview of our work on the automatic detection and exploitation of context dependencies from timed execution traces.

Calculating WCET estimates from timed traces

Real-time systems engineers face a daunting duty: they must ensure that each task in their system can always meet its deadline. To analyse schedulability they must know the worst-case execution time (WCET) of each task. However, determining exact WCETs is practically infeasible in cost-constrained industrial settings involving real-life code and COTS hardware. Static analysis tools that could yield sufficiently tight WCET bounds are often unavailable. As a result, interest in portable analysis approaches like measurement-based timing analysis is growing. We present an approach based on integer linear programming (ILP) for calculating a WCET estimate from a given database of timed execution traces. Unlike previous work, our method specifically aims at reducing overestimation, by means of an automatic classification of code executions into scenarios with differing worst-case behaviour. To ease the integration into existing analysis tool chains, our method is based on the implicit path enumeration technique. It can thus reuse flow facts from other analysis tools and produces ILP problems that can be solved by off-the-shelf solvers.

Optimised Adaptation of Mixed-Criticality Systems with Periodic Tasks on Uniform Multiprocessors in Case of Faults

The standard real-time computing model is based on a notion of deadline that represents both, a design parameter and a critical latency. The tolerance-based real-time computing model (TRTCM) extends the standard real-time computing model to reflect good engineering practice, where a safety margin is added to the critical latency to obtain the corresponding design parameter. The key feature of TRTCM is to add a tolerance range, which in combination with criticality specifications for services allows for fault-tolerant mixed-criticality scheduling with smooth degradation of service utility in case of resource shortage. In this paper we study the applicability of TRTCM for periodic tasks with mixed criticality on uniform multiprocessor systems. To optimise the system adaptation in case of resource shortage we formulate utility maximisation based on TRTCM as an optimisation problem. We describe two implementation strategies and derive an optimisation problem to be solved by a constraint solver for each. We use a video processing application for a concrete evaluation. Our results show that TRTCM can provide better utility than standard mixed-criticality approaches where lower criticality services are skipped in case of resource shortage.