EDF-Like Scheduling for Self-Suspending Real-Time Tasks

In real-time systems, schedulability tests are utilized to provide timing guarantees. However, for self-suspending task sets, current suspension-aware schedulability tests are limited to Task-Level Fixed-Priority~(TFP) scheduling or Earliest-Deadline-First~(EDF) with constrained-deadline task systems. In this work we provide a unifying schedulability test for the uniprocessor version of Global EDF-Like (GEL) schedulers and arbitrary-deadline task sets. A large body of existing scheduling algorithms can be considered as EDF-Like, such as EDF, First-In-First-Out~(FIFO), Earliest-Quasi-Deadline-First~(EQDF) and Suspension-Aware EDF~(SAEDF). Therefore, the unifying schedulability test is applicable to those algorithms. Moreover, the schedulability test can be applied to TFP scheduling as well. Our analysis is the first suspension-aware schedulability test applicable to arbitrary-deadline sporadic real-time task systems under Job-Level Fixed-Priority (JFP) scheduling, such as EDF. Moreover, it is the first unifying suspension-aware schedulability test framework that covers a wide range of scheduling algorithms. Through numerical simulations, we show that the schedulability test outperforms the state of the art for EDF under constrained-deadline scenarios. Moreover, we demonstrate the performance of different configurations under EQDF and SAEDF

On the Equivalence of Maximum Reaction Time and Maximum Data Age for Cause-Effect Chains

Real-time systems require a formal guarantee of timing-constraints, not only for individual tasks but also for data-propagation. The timing behavior of data-propagation paths in a given system is typically described by its maximum reaction time and its maximum data age. This paper shows that they are equivalent. To reach this conclusion, partitioned job chains are introduced, which consist of one immediate forward and one immediate backward job chain. Such partitioned job chains are proven to describe maximum reaction time and maximum data age in a universal manner. This universal description does not only show the equivalence of maximum reaction time and maximum data age, but can also be exploited to speed up the computation of such significantly. In particular, the speed-up for synthesized task sets based on automotive benchmarks can be up to 1600. Since only very few non-restrictive assumptions are made, the equivalence of maximum data age and maximum reaction time holds for almost any scheduling mechanism and even for tasks which do not adhere to the typical periodic or sporadic task model. This observation is supported by a simulation of a ROS2 navigation system

Efficiently Approximating the Probability of Deadline Misses in Real-Time Systems

This paper explores the probability of deadline misses for a set of constrained-deadline sporadic soft real-time tasks on uniprocessor platforms. We explore two directions to evaluate the probability whether a job of the task under analysis can finish its execution at (or before) a testing time point t. One approach is based on analytical upper bounds that can be efficiently computed in polynomial time at the price of precision loss for each testing point, derived from the well-known Hoeffding\u27s inequality and the well-known Bernstein\u27s inequality. Another approach convolutes the probability efficiently over multinomial distributions, exploiting a series of state space reduction techniques, i.e., pruning without any loss of precision, and approximations via unifying equivalent classes with a bounded loss of precision. We demonstrate the effectiveness of our approaches in a series of evaluations. Distinct from the convolution-based methods in the literature, which suffer from the high computation demand and are applicable only to task sets with a few tasks, our approaches can scale reasonably without losing much precision in terms of the derived probability of deadline misses

Renovation of EdgeCloudSim: An Efficient Discrete-Event Approach

Due to the growing popularity of the Internet of Things, edge computing concept has been widely studied to relieve the load on the original cloud and networks while improving the service quality for end-users. To simulate such a complex environment involving edge and cloud computing, EdgeCloudSim has been widely adopted. However, it suffers from certain efficiency and scalability issues due to the ignorance of the deficiency in the originally adopted data structures and maintenance strategies. Specifically, it generates all events at beginning of the simulation and stores unnecessary historical information, both result in unnecessarily high complexity for search operations. In this work, by fixing the mismatches on the concept of discrete-event simulation, we propose enhancement of EdgeCloudSim which improves not only the runtime efficiency of simulation, but also the flexibility and scalability. Through extensive experiments with statistical methods, we show that the enhancement does not affect the expressiveness of simulations while obtaining 2 orders of magnitude speedup, especially when the device count is large

Register Your Forests:Decision Tree Ensemble Optimization by Explicit CPU Register Allocation

Bringing high-level machine learning models to efficient and well-suited machine implementations often invokes a bunch of tools, e.g.~code generators, compilers, and optimizers. Along such tool chains, abstractions have to be applied. This leads to not optimally used CPU registers. This is a shortcoming, especially in resource constrained embedded setups. In this work, we present a code generation approach for decision tree ensembles, which produces machine assembly code within a single conversion step directly from the high-level model representation. Specifically, we develop various approaches to effectively allocate registers for the inference of decision tree ensembles. Extensive evaluations of the proposed method are conducted in comparison to the basic realization of C code from the high-level machine learning model and succeeding compilation. The results show that the performance of decision tree ensemble inference can be significantly improved (by up to $\approx1.6\times$), if the methods are applied carefully to the appropriate scenario