An official website of the United States government
Existing models used in real-time scheduling are inadequate to take advantage of simultaneous multithreading (SMT), which has been shown to improve performance in many areas of computing, but has seen little application to real-time systems. The SMART task model, which allows for combining SMT and real time by accounting for the variable task execution costs caused by SMT, is introduced, along with methods and conditions for scheduling SMT tasks under global earliest-deadline-first scheduling. The benefits of using SMT are demonstrated through a large-scale schedulability study in which we show that task systems with utilizations 30% larger than what would be schedulable without SMT can be correctly scheduled. This artifact includes benchmark experiments used to compare execution times with and without SMT and code to duplicate the reported schedulability experiments.
more »
« less
Simultaneous Multithreading and Hard Real Time: Can it be Safe?
The applicability of Simultaneous Multithreading (SMT) to real-time systems has been hampered by the difficulty of obtaining reliable execution costs in an SMT-enabled system. This problem is addressed by introducing a scheduling framework, called CERT-MT, that combines scheduling-aware timing analysis with a cyclic-executive scheduler in a way that minimizes SMT-related timing variations. The proposed scheduling-aware timing analysis is based on maximum observed execution times and accounts for the uncertainty inherent in measurement-based timing analysis. The timing analysis is found to work for tasks with and without SMT, though some adjustments are required in the former case. A large-scale schedulability study is presented that shows CERT-MT can schedule systems with total utilizations approaching 1.4 times the core count, without sacrificing safety.
more »
« less
- Award ID(s):
- 1717589
- PAR ID:
- 10183928
- Date Published:
- Journal Name:
- Proceedings of the 32nd Euromicro Conference on Real-Time Systems
- Page Range / eLocation ID:
- 14:1-14:25
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
null (Ed.)Simultaneous Multithreading (SMT) has the ability to dramatically improve real-time scheduling, but existing methods are cumbersome, frequently need specialized hardware, or are limited to producing table-based schedules. Here, an easily portable method for quickly applying SMT to priority-driven hard real-time systems is given. Using a combination of integer linear programming and heuristic bin-packing, a partitioned Earliest-Deadline-First (EDF) scheduler that takes advantage of SMT is produced. The integer linear programming and partitioning are done offline, but generally require only a few seconds, even given over a hundred tasks. A large-scale schedulability study is conducted, showing that compared to partitioned scheduling without SMT, the schedulable utilization for the considered hardware platform is nearly doubled in the best cases.more » « less
-
To design performant, expressive, and reliable cyber-physical systems (CPSs), researchers extensively perform quasi-static scheduling for concurrent models of computation (MoCs) on multi-core hardware. However, these quasi-static scheduling approaches are developed independently for their corresponding MoCs, despite commonality in the approaches. To help generalize the use of quasi-static scheduling to new and emerging MoCs, this article proposes aunifiedapproach for a class of deterministic timed concurrent models (DTCMs), including prominent models such as synchronous dataflow (SDF), Boolean-controlled dataflow (BDF), scenario-aware dataflow (SADF), and Logical Execution Time (LET). In contrast to scheduling techniques tailored exclusively to specific MoCs, our unified approach leverages a commonintermediateformalism called state space finite automata (SSFA), bridging the gap between high-level MoCs and executable schedules. Once identified as DTCMs, new MoCs can directly adopt SSFA-based scheduling, significantly easing adoption. We show that quasi-static schedules facilitated by SSFA are provably free from timing anomalies and enable straightforward worst-case makespan analysis. We demonstrate the approach using the reactor model—an emerging discrete-event MoC—programmed using the Lingua Franca (LF) language. Experiments show that quasi-statically scheduledLFprograms exhibit lower runtime overhead compared to the dynamically scheduledLFprograms, and that the analyzable worst-case makespans enable compile-time deadline checking.more » « less
-
As renewable energy sources become more integrated into the power grid, efficient scheduling of household energy consumption is essential to reduce costs and carbon footprint. In this paper, we introduce an energy optimization framework that optimizes the timing of appliance use based on dynamic carbon intensity and electricity prices. We determine optimal operation schedules using a multi-objective optimization model with a Gurobi solver and machine learning. We combine Random Forest and XGBoost for demand prediction, incorporating their uncertainty estimates into the optimization constraints. The model optimizes ON/OFF appliance schedules while considering constraints like minimum operating times and power balance. It shifts usage to lower-cost and carbon-intensity periods, which helps to reduce energy consumption.Key contributions include ML-based demand predictions and mixed-integer programming (MIP) optimization that improves robustness to prediction errors while adjusting schedules based on time-of-use pricing and carbon intensity. Our smart scheduling and load shifting achieve a 35.8% reduction in costs, a 38.6% decrease in carbon emissions, and a 25.8% reduction in peak demand using a carbon-aware scheduling algorithm. This framework effectively shifts loads to low-cost, low-carbon times, offering significant economic and environmental benefits for residential energy management without compromising user comfort.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Conference Paper:
- https://doi.org/10.4230/DARTS.6.1.1
