Reservation-Based Federated Scheduling for Parallel Real-Time Tasks

This paper considers the scheduling of parallel real-time tasks with arbitrary-deadlines. Each job of a parallel task is described as a directed acyclic graph (DAG). In contrast to prior work in this area, where decomposition-based scheduling algorithms are proposed based on the DAG-structure and inter-task interference is analyzed as self-suspending behavior, this paper generalizes the federated scheduling approach. We propose a reservation-based algorithm, called reservation-based federated scheduling, that dominates federated scheduling. We provide general constraints for the design of such systems and prove that reservation-based federated scheduling has a constant speedup factor with respect to any optimal DAG task scheduler. Furthermore, the presented algorithm can be used in conjunction with any scheduler and scheduling analysis suitable for ordinary arbitrary-deadline sporadic task sets, i.e., without parallelism

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

Preemptive scheduling with variable profile, precedence constraints and due dates

This paper is concerned with the problem of scheduling preemptive tasks subject to precedence constraints in order to minimize the maximum lateness and the makespan. The number of available parallel processors is allowed to vary in time. It is shown that when an earliest due date first algorithm provides an optimal nonpreemptive schedule for unit execution time tasks, then the preemptive priority scheduling algorithm, referred to as smallest laxity first, provides an optimal preemptive schedule for real-execution-time tasks. When the objective is to minimize the makespan, we get the same kind of result between highest level first schedules solving nonpremptive tasks with unit execution time and the longest remaining path first schedule for the corresponding preemptive scheduling problem with real-execution-time tasks. These results are applied to four specific profile scheduling problems and new optimality results are obtained

Learning scalable and transferable multi-robot/machine sequential assignment planning via graph embedding

Can the success of reinforcement learning methods for simple combinatorial optimization problems be extended to multi-robot sequential assignment planning? In addition to the challenge of achieving near-optimal performance in large problems, transferability to an unseen number of robots and tasks is another key challenge for real-world applications. In this paper, we suggest a method that achieves the first success in both challenges for robot/machine scheduling problems. Our method comprises of three components. First, we show a robot scheduling problem can be expressed as a random probabilistic graphical model (PGM). We develop a mean-field inference method for random PGM and use it for Q-function inference. Second, we show that transferability can be achieved by carefully designing two-step sequential encoding of problem state. Third, we resolve the computational scalability issue of fitted Q-iteration by suggesting a heuristic auction-based Q-iteration fitting method enabled by transferability we achieved. We apply our method to discrete-time, discrete space problems (Multi-Robot Reward Collection (MRRC)) and scalably achieve 97% optimality with transferability. This optimality is maintained under stochastic contexts. By extending our method to continuous time, continuous space formulation, we claim to be the first learning-based method with scalable performance among multi-machine scheduling problems; our method scalability achieves comparable performance to popular metaheuristics in Identical parallel machine scheduling (IPMS) problems

실시간 멀티코어 플루이드 스케줄링에서 전체 시스템의 시간 · 밀도 트레이드오프

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 이창건.Recent parallel programming frameworks such as OpenCL and OpenMP allow us to enjoy the parallelization freedom for real-time tasks. The parallelization freedom creates the time vs. density tradeoff problem in fluid scheduling, i.e., more parallelization reduces thread execution times but increases the density. By system-widely exercising this tradeoff, this dissertation proposes a parameter tuning of real-time tasks aiming at maximizing the schedulability of multicore fluid scheduling. The experimental study by both simulation and actual implementation shows that the proposed approach well balances the time and the density, and results in up to 80% improvement of the schedulability.1 Introduction 1 1.1 Motivation and Objective 1 1.2 Approach 3 1.3 Organization 4 2 Related Work 6 2.1 Real-Time Scheduling 6 2.1.1 Workload Model 6 2.1.2 Scheduling on Multicore Systems 7 2.1.3 Period Control 9 2.1.4 Real-Time Operating System 10 2.2 Parallel Computing 10 2.2.1 Parallel Computing Framework 10 2.2.2 Shared Resource Management 12 3 System-wide Time vs. Density Tradeoff with Parallelizable Periodic Single Segment Tasks 14 3.1 Introduction 14 3.2 Problem Description 14 3.3 Motivating Example 21 3.4 Proposed Approach 26 3.4.1 Per-task Optimal Tradeoff of Time and Density 26 3.4.2 Peak Density Minimization for a Task Group with the Same Period 27 3.4.3 Heuristic Algorithm for System-wide Time vs. Density Tradeoff 38 3.5 Experimental Results 45 3.5.1 Simulation Study 45 3.5.2 Actual Implementation Results 51 4 System-wide Time vs. Density Tradeoff with Parallelizable Periodic Multi-segment Tasks 64 4.1 Introduction 64 4.2 Problem Description 64 4.3 Extension to Parallelizable Periodic Multi-segment Task Model 70 4.3.1 Peak Density Minimization for a Task Group of Multi-segment Tasks with Same Period 71 4.3.2 Heuristic Algorithm for System-wide Time vs. Density Tradeoff 78 5 Conclusion 81 5.1 Summary 81 5.2 Future Work 82 References 84 Appendices 100 A Period Harmonization 100Docto

A dynamic power-aware partitioner with task migration for multicore embedded systems

Nowadays, a key design issue in embedded systems is how to reduce the power consumption, since batteries have a limited energy budget. For this purpose, several techniques such as Dynamic Voltage Scaling (DVS) or task migration can be used. DVS allows reducing power by selecting the optimal voltage supply, while task migration achieves this effect by balancing the workload among cores. This paper first analyzes the impact on energy due to task migration in multicore embedded systems with DVS capability and using the well-known Worst Fit (WF) partitioning heuristic. To reduce overhead, migrations are only performed at the time that a task arrives to and/or leaves the system and, in such a case, only one migration is allowed. The huge potential on energy saving due to task migration, leads us to propose a new dynamic partitioner, namely DP, that migrates tasks in a more efficient way than typical partitioners. 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