Global EDF Scheduling for Parallel Real-Time Tasks

As multicore processors become ever more prevalent, it is important for real-time programs to take advantage of intra-task parallelism in order to support computation-intensive applications with tight deadlines. In this thesis, we consider the Global Earliest Deadline First (GEDF) scheduling policy for task sets consisting of parallel tasks. Each task can be represented by a directed acyclic graph (DAG) where nodes represent computational work and edges represent dependences between nodes. In this model, we prove that GEDF provides a capacity augmentation bound of 4-2/m and a resource augmentation bound of 2-1/m. The capacity augmentation bound acts as a linear-time schedulability test since it guarantees that any task set with total utilization of at most m/(4-2/m) where each task\u27s critical-path length is at most 1/(4-2/m) of its deadline is schedulable on m cores under GEDF. In addition, we present a pseudo-polynomial time fixed-point schedulability test for GEDF; this test uses a carry-in work calculation based on the proof for the capacity bound. Finally, we present and evaluate a prototype platform --- called PGEDF --- for scheduling parallel tasks using GEDF. PGEDF is built by combining the GNU OpenMP runtime system and the LITMUS_RT operating system. This platform allows programmers to write parallel OpenMP tasks and specify real-time parameters such as deadlines for tasks. We perform two kinds of experiments to evaluate the performance of GEDF for parallel tasks. (1) We run numerical simulations for DAG tasks. (2) We execute randomly generated tasks using PGEDF. Both sets of experiments indicate that GEDF performs surprisingly well and outperforms an existing scheduling techniques that involves task decomposition

A generalized parallel task model for recurrent real-time processes

A model is considered for representing recurrent precedence-constrained tasks that are to execute on multiprocessor platforms. A recurrent task is specified as a directed acyclic graph (DAG), a period, and a relative deadline. Each vertex of the DAG represents a sequential job, while the edges of the DAG represent precedence constraints between these jobs. All the jobs of the DAG are released simultaneously and need to complete execution within the specified relative deadline of their release. Each task may release j

An Efficient Online Benefit-aware Multiprocessor Scheduling Technique for Soft Real-Time Tasks Using Online Choice of Approximation Algorithms

Maximizing the benefit gained by soft real-time tasks in many applications and embedded systems is highly needed to provide an acceptable QoS (Quality of Service). Examples of such applications and embedded systems include real-time medical monitoring systems, video- streaming servers, multiplayer video games, and mobile multimedia devices. In these systems, tasks are not equally critical (or beneficial). Each task comes with its own benefit-density function which can be different from the others’. The sooner a task completes, the more benefit it gains. In this work, a novel online benefit-aware preemptive approach is presented in order to enhance scheduling of soft real-time aperiodic and periodic tasks in multiprocessor systems. The objective of this work is enhancing the QoS by increasing the total benefit, while reducing flow times and deadline misses. This method prioritizes the tasks using their benefit-density functions, which imply their importance to the system, and schedules them in a real-time basis. The first model I propose is for scheduling soft real-time aperiodic tasks. An online choice of two approximation algorithms, greedy and load-balancing, is used in order to distribute the low- priority tasks among identical processors at the time of their arrival without using any statistics. The results of theoretical analysis and simulation experiments show that this method is able to maximize the gained benefit and decrease the computational complexity (compared to existing algorithms) while minimizing makespan with fewer missed deadlines and more balanced usage of processors. I also propose two more versions of this algorithm for scheduling SRT periodic tasks, with implicit and non-implicit deadlines, in addition to another version with a modified loadbalancing factor. The extensive simulation experiments and empirical comparison of these algorithms with the state of the art, using different utilization levels and various benefit density functions show that these new techniques outperform the existing ones. A general framework for benefit-aware multiprocessor scheduling in applications with periodic, aperiodic or mixed real-time tasks is also provided in this work.Computer Science, Department o

사이버-물리 시스템을 위한 기능적/시간적 정확성 보장 시뮬레이션 기법

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 이창건.When developing a Cyber-Physical System (CPS), simulators are commonly used to predict the final performance of the system at the design phase. However, current simulation tools do not consider timing behaviors of the cyber-system such as varying execution times and task preemptions. Thus, their control performance predictions are far different from the real performance, and this leads to enormous time and cost for a system development, because multiple re-design and re-implementation phases are required, until an acceptable system configuration is determined. Motivated by this limitation, this dissertation proposes functionally and temporally correct simulation for the cyber-side of a CPS. The key idea of the proposed approach is to keep the data and time correctness only at the physical interaction points to maximally enjoy the freedom of scheduling simulated jobs. For this, we transform the simulation problem to a real-time job scheduling problem with precedence constraints necessary for the functional and temporal correctness. Then, we propose an efficient scheduling algorithm for the functionally and temporally correct real-time simulation. The proposed approach significantly improves the real-time simulation capacity of the state-of-the-art simulation methods while keeping the functional and temporal correctness. Our evaluation through both synthetic workload and actual implementation confirms both high accuracy and high efficiency of our approach compared with other state-of-the-art methods.1 Introduction 1 1.1 Motivation and Objective 1 1.2 Approach 3 1.3 Contributions 8 1.4 Organization 8 2 Related Work 10 2.1 Design and Verification of Cyber-Physical Systems 10 2.2 Verification Approaches 12 2.2.1 Model-Based Simulations 12 2.2.2 Cycle-Accurate Simulations and Host-Compiled Simulations 14 2.2.3 Real-Time Execution Platforms 15 2.2.4 Distributed Simulations 16 2.3 Job Scheduling Approaches 17 3 System Model and Problem Description 22 3.1 Description on the real cyber-system 23 3.2 Description on the simulated cyber-system 27 3.3 Formal definition of the simulation problem 28 4 Real-Time Simulation for Deterministic Cyber-Systems 31 4.1 Introduction 31 4.2 Construction of Offline Guider 31 4.3 Online Progressive Scheduling of Simulated Jobs 34 4.4 Evaluation 38 5 Real-Time Simulation for Non-Deterministic Cyber-Systems 45 5.1 Introduction 45 5.2 Overview of Approach 45 5.3 Construction of Offline Guider 50 5.4 Online Progressive Scheduling of Simulated Jobs 63 5.5 Evaluation 74 5.5.1 Evaluation Using Synthesized Cyber-Systems 78 5.5.2 Implementation 86 6 Practical Discussions 95 6.1 Data Exchange Delay 95 6.2 Simulation Overhead 97 6.2.1Offline Overhead 97 6.2.2 Online Overhead 100 6.3 Other Useful Features 100 7 Extension for Multicore Simulation PC 102 8 Conclusion 108 8.1 Summary 108 8.2 Future Work 108 References 110Docto

Parallel Real-Time Scheduling for Latency-Critical Applications

In order to provide safety guarantees or quality of service guarantees, many of today\u27s systems consist of latency-critical applications, e.g. applications with timing constraints. The problem of scheduling multiple latency-critical jobs on a multiprocessor or multicore machine has been extensively studied for sequential (non-parallizable) jobs and different system models and different objectives have been considered. However, the computational requirement of a single job is still limited by the capacity of a single core. To provide increasingly complex functionalities of applications and to complete their higher computational demands within the same or even more stringent timing constraints, we must exploit the internal parallelism of jobs, where individual jobs are parallel programs and can potentially utilize more than one core in parallel. However, there is little work considering scheduling multiple parallel jobs that are latency-critical. This dissertation focuses on developing new scheduling strategies, analysis tools, and practical platform design techniques to enable efficient and scalable parallel real-time scheduling for latency-critical applications on multicore systems. In particular, the research is focused on two types of systems: (1) static real-time systems for tasks with deadlines where the temporal properties of the tasks that need to execute is known a priori and the goal is to guarantee the temporal correctness of the tasks prior to their executions; and (2) online systems for latency-critical jobs where multiple jobs arrive over time and the goal to optimize for a performance objective of jobs during the execution. For static real-time systems for parallel tasks, several scheduling strategies, including global earliest deadline first, global rate monotonic and a novel federated scheduling, are proposed, analyzed and implemented. These scheduling strategies have the best known theoretical performance for parallel real-time tasks under any global strategy, any fixed priority scheduling and any scheduling strategy, respectively. In addition, federated scheduling is generalized to systems with multiple criticality levels and systems with stochastic tasks. Both numerical and empirical experiments show that federated scheduling and its variations have good schedulability performance and are efficient in practice. For online systems with multiple latency-critical jobs, different online scheduling strategies are proposed and analyzed for different objectives, including maximizing the number of jobs meeting a target latency, maximizing the profit of jobs, minimizing the maximum latency and minimizing the average latency. For example, a simple First-In-First-Out scheduler is proven to be scalable for minimizing the maximum latency. Based on this theoretical intuition, a more practical work-stealing scheduler is developed, analyzed and implemented. Empirical evaluations indicate that, on both real world and synthetic workloads, this work-stealing implementation performs almost as well as an optimal scheduler