Germany

We regard the industry challenge as a multi- objective real-time scheduling problem. Different from tradi- tional methods, we model it as a nonlinear program (NLP) and use gradient-based methods for efficient solutions. Different requirements can be freely added as constraints, which gives the proposed method effective for a large range of problems. Furthermore, the proposed NLP scheduling method can utilize available scheduling algorithm as initialization method, and improve their performance even further. Preliminary evaluation shows advantages against simple heuristic methods in compli- cated problems.

IEEE RTSS 2021

A General Scheduling Framework for Multi-objective Real-time Systems

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 **RTSS 2021** has successfully concluded. Hope you have enjoyed it, and look forward to seeing you next year in person.
    Announcement for the Awards:
 * Best Paper Award goes to: Monte Carlo Response-Time Analysis, Sergey Bozhko, Georg von der Brüggen, and Björn Brandenburg
 * Best Student Paper Award goes to: Automatic Energy-Hotspot Detection and Elimination in Real-Time Deeply Embedded Systems, Mohsen Shekarisaz, Lothar Thiele, and Mehdi Kargahi.
 * Best Solution Award for Industry Session goes to: Toward Real-Time Guaranteed Scheduling for Autonomous Driving Systems. Jinghao Sun, Tianyi Wang, Kailu Duan, Bin Lu, Jiankang Ren, Zhishan Guo, and Guozhen Tan.



The IEEE Real-Time Systems Symposium (RTSS) is the premier conference in the field of real-time systems, and is a venue for researchers and practitioners to showcase innovations covering all aspects of real-time systems, including theory, design, analysis, implementation, evaluation, and experience. 

Software transactional memory (STM) is a synchronization paradigm originally proposed for throughput-oriented computing to facilitate producing performant concurrent code that is free of synchronization bugs. With STM, programmers merely annotate code sections requiring synchronization; the underlying STM framework automatically resolves how synchronization is done. Today, the programming issues that motivated STM are becoming a concern in embedded computing, where ever more sophisticated systems are being produced that require highly parallel implementations. In this area, an evolution is underway to produce sophisticated systems such as autonomous vehicles that require highly parallel implementations. These implementations are often produced by engineers and control experts who may not be well versed in concurrency-related issues. In this context, a real-time STM framework would be useful in ensuring that the synchronization aspects of a system pass real-time certification. However, all prior STM approaches fundamentally rely on retries to resolve conflicts, and such retries can yield high worst-case synchronization costs compared to lock-based approaches. Unfortunately, prior work on real-time STM is not helpful in this regard because it has been limited to approaches that retry conflicting transactions; such approaches require retry bounds, which can be large and pessimistic on multiprocessors. This paper presents a new STM class called Retry-Free Real-Time STM (R^2STM), which is designed for worst-case real-time performance. The benefit of a retry-free approach for use in a real-time system is demonstrated by a schedulability study, in which it improved overall schedulability across all considered task systems by an average of 95.3% over a retry-based approach. This paper also presents TORTIS, the first R^2STM implementation for real-time systems.

TORTIS: Retry-Free Software Transactional Memory for Real-Time Systems

In real-time scheduling theory, self-suspension describes the behavior that a job can suspend itself from the ready state and thus be exempted from the scheduling for the suspension duration. This behavior makes it non-trivial to resort to established concepts such as the busy-interval analysis to self-suspending task sets which is required to analyze the worst-case response time of tasks with backlog, e.g., arbitrary-deadline task sets. In this paper, we present a novel suspension-aware busy-interval analysis for dynamic self-suspension tasks where the inter-arrival time of subsequent jobs can be bounded by an arrival curve. Based on the general analysis, we provide worst-case response time analyses and hence sufficient schedulability tests for fixed-priority preemptive uniprocessor scheduling algorithms for arrival-curve constrained and sporadic self-suspension task systems with arbitrary deadlines. Moreover, we provide evaluations based on synthetically generated task sets that show that our method indeed exploits the optimism that is introduced when enlarging the relative deadline of tasks. We demonstrate that our approach improves the state of the art by considering arrival curves that are obtained from tasks with release jitter.

Suspension-Aware Fixed-Priority Schedulability Test with Arbitrary Deadlines And Arrival Curves

We consider the problem of scheduling a set of preemptible independent periodic implicit-deadline hard real- time tasks on heterogeneous processors. We divide this problem into two sub-problems: (a) assigning portions of each proces- sor (offline) to each task without jeopardizing schedulability; and (b) generating a schedule satisfying the assigned portions using an online semi-partitioned scheduler, called Heterogeneous Quasi-Partitioned Scheduling (hQPS). The scheduler handles task servers at run-time for ensuring that the processor shares assigned to tasks are timely available to them. Assessments indicate that the proposed solution (i) has good scalability (up to 64 tasks, 64 processors), (ii) is effective in generating schedules with few preemptions and few migrations, and (iii) is effective in managing resources; for task sets where an extra processor speed is required, our solution needs at most 10% extra compared to an optimal scheduler.

Heterogeneous Quasi-Partitioned Scheduling

Industrial Challenge: Opening, Introduction, and Overview of Solutions

In this paper we address the timing verification for processing graphs used to implement autonomous driving systems by the company Perceptin. We demonstrate how to extend our previous results on end-to-end timing analysis in terms of maximum reaction time and maximum data age for cause-effect chains in automotive systems for the posed challenge.

End-To-End Processing Chain Analysis

The 2021 RTSS Industry challenge considers timing constraints in autonomous driving systems. One real-time requirement is that the differences of timestamps among all source data of an output must be upper-bounded by a pre-defined value, to make sure that data from different sources are correctly fused, e.g, object detection by using different sensing data. In this paper, we formally describe the challenge system as well as the requirements. In particular, we present timing analysis to bound the maximum differences of timestamps among all sources for an output. We show that an output may possibly be caused by two different data from the same source, when fork-join structure exists. This result suggests that data fusion after different long pipelined processing procedure must be avoided to meet required timing constraints.

Analysis of Time difference among Sensing Data in Autonomous Driving Systems

Given the problem proposed in RTSS 2021 Industry Challenge, we formally define a system model where dependent tasks are organized in a direct acyclic graph structure and communicate with an unblocking manner. For such a system, we present solutions to estimate three time latencies: maximum reaction time, maximum data age, and maximum difference among the timestamps of ”source” sensor data. One major contribution made in this paper is that the clock shifts among different processing units have been taken into account during the estimations without a mandatory schedule simulation process. By doing this, fairly tight upper bounds can be calculated for all three latencies.

Timing Analysis for Dependent Tasks in Distributed Embedded Systems

We propose a SMT method to derive a real-time guaranteed schedule for the autonomous driving system de- scribed in the industry challenge. All timing constraints proposed as the industry challenge are considered in our SMT model.

Toward Real-Time Guaranteed Scheduling for Autonomous Driving Systems

Considering the instance of autonomous driving system mentioned in RTSS 2021 Industry Challenge, a mixed integer linear programming (MILP) model is built to formulate this specific instance. Based on the specific instance, system and scheduling models are proposed correspondingly.

A Linear Programming Solution to Real-time Scheduling in Autonomous Driving System

In this paper, we propose a general solution to the problem of scheduling real-time applications on heterogeneous hardware platforms. To fully utilize the computing capacity of heterogeneous processing units, we model the real-time applica- tion as a heterogeneous Directed Acyclic Graph (DAG) which specifies the types of processors (CPU, GPU, etc.) where each task should run. In this well-known DAG context, we propose a novel extension aimed at safety-critical systems that operate in unpredictable environments: the coupling of conditional DAG nodes with stochasticity. Specifically, conditional DAG nodes en- able the modeling of systems that execute computational pipelines based on environmental context, while stochasticity of DAG edges captures the uncertain nature of a system’s environment or the reliability of its hardware. Furthermore, considering the pessimism of deterministic worst-case execution time (WCET) in scheduling processes, we model execution times of tasks (DAG nodes) as probability distributions which yields a novel stochastic conditional DAG model. Coupled with a novel S-bottleneck heuris- tic and safety-performance (SP) metric, our proposed framework allows for efficient online scheduling in complex computational pipelines, with more flexible representation of timing constraints, and ultimately, safety-performance trade offs.

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