On the Pitfalls of Resource Augmentation Factors and Utilization Bounds in Real-Time Scheduling

In this paper, we take a careful look at speedup factors, utilization bounds, and capacity augmentation bounds. These three metrics have been widely adopted in real-time scheduling research as the de facto standard theoretical tools for assessing scheduling algorithms and schedulability tests. Despite that, it is not always clear how researchers and designers should interpret or use these metrics. In studying this area, we found a number of surprising results, and related to them, ways in which the metrics may be misinterpreted or misunderstood. In this paper, we provide a perspective on the use of these metrics, guiding researchers on their meaning and interpretation, and helping to avoid pitfalls in their use. Finally, we propose and demonstrate the use of parametric augmentation functions as a means of providing nuanced information that may be more relevant in practical settings

Capacity Augmentation Bound of Federated Scheduling for Parallel DAG Tasks

We present a novel federated scheduling approach for parallel real-time tasks under a general directed acyclic graph (DAG) model. We provide a capacity augmentation bound of 2 for hard real-time scheduling; here we use the worst-case execution time and critical-path length of tasks to determine schedulability. This is the best known capacity augmentation bound for parallel tasks. By constructing example task sets, we further show that the lower bound on capacity augmentation of federated scheduling is also 2 for any m \u3e 2. Hence, the gap is closed and bound 2 is a strict bound for federated scheduling. The federated scheduling algorithm is also a schedulability test that often admits task sets with utilization much greater than 50%m

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

Real-Time Wireless Sensor-Actuator Networks for Cyber-Physical Systems

A cyber-physical system (CPS) employs tight integration of, and coordination between computational, networking, and physical elements. Wireless sensor-actuator networks provide a new communication technology for a broad range of CPS applications such as process control, smart manufacturing, and data center management. Sensing and control in these systems need to meet stringent real-time performance requirements on communication latency in challenging environments. There have been limited results on real-time scheduling theory for wireless sensor-actuator networks. Real-time transmission scheduling and analysis for wireless sensor-actuator networks requires new methodologies to deal with unique characteristics of wireless communication. Furthermore, the performance of a wireless control involves intricate interactions between real-time communication and control. This thesis research tackles these challenges and make a series of contributions to the theory and system for wireless CPS. (1) We establish a new real-time scheduling theory for wireless sensor-actuator networks. (2) We develop a scheduling-control co-design approach for holistic optimization of control performance in a wireless control system. (3) We design and implement a wireless sensor-actuator network for CPS in data center power management. (4) We expand our research to develop scheduling algorithms and analyses for real-time parallel computing to support computation-intensive CPS

Simulation of Real-Time Scheduling with Various Execution Time Models

Presented during the Work-in-Progress session (WiP session)International audienceIn this paper, we present SimSo, a simulator that aims at facilitating the design of experimental evaluations for real-time scheduling algorithms. Currently, more than twenty-five algorithms were implemented. Special attention is paid to the execution time model of tasks. We show that the worst-case execution time for experimental simulation can introduce a bias in evaluation and we discuss as a work in progress how cache effects could be taken into consideration in the simulation

CPU Energy-Aware Parallel Real-Time Scheduling

Both energy-efficiency and real-time performance are critical requirements in many embedded systems applications such as self-driving car, robotic system, disaster response, and security/safety control. These systems entail a myriad of real-time tasks, where each task itself is a parallel task that can utilize multiple computing units at the same time. Driven by the increasing demand for parallel tasks, multi-core embedded processors are inevitably evolving to many-core. Existing work on real-time parallel tasks mostly focused on real-time scheduling without addressing energy consumption. In this paper, we address hard real-time scheduling of parallel tasks while minimizing their CPU energy consumption on multicore embedded systems. Each task is represented as a directed acyclic graph (DAG) with nodes indicating different threads of execution and edges indicating their dependencies. Our technique is to determine the execution speeds of the nodes of the DAGs to minimize the overall energy consumption while meeting all task deadlines. It incorporates a frequency optimization engine and the dynamic voltage and frequency scaling (DVFS) scheme into the classical real-time scheduling policies (both federated and global) and makes them energy-aware. The contributions of this paper thus include the first energy-aware online federated scheduling and also the first energy-aware global scheduling of DAGs. Evaluation using synthetic workload through simulation shows that our energy-aware real-time scheduling policies can achieve up to 68% energy-saving compared to classical (energy-unaware) policies. We have also performed a proof of concept system evaluation using physical hardware demonstrating the energy efficiency through our proposed approach