skip to main content

This content will become publicly available on June 8, 2023

Title: Event-triggered Scheduling and Control Co-design for Networked Control Systems with Sub-schedulability
We propose a new concept named subschedulability to relax schedulability conditions on task sets in the context of scheduling and control co-design. Subschedulability is less conservative compared to schedulablity requirement with respect to network utilization. But it can still guarantee that all tasks can be executed before or within a bounded time interval after their deadlines. Based on the subschedulability concept, we derive an analytical timing model to check the sub-schedulability and perform online prediction of time-delays caused by real-time scheduling. A modified event-triggered contention-resolving MPC is presented to co-design the scheduling and control for the sub-schedulable control tasks. Simulation results are demonstrated to show the effectiveness of the proposed method.
Authors:
;
Award ID(s):
1828678 1849228 1934836
Publication Date:
NSF-PAR ID:
10359106
Journal Name:
Proceedings of the 2022 American Control Conference
Page Range or eLocation-ID:
1733 to 1738
Sponsoring Org:
National Science Foundation
More Like this
  1. Intermittently-powered devices have gained much interest in recent years. However, scheduling real-time tasks while supporting data consistency, timekeeping, and schedulability guarantees on these devices still remains a challenge. Many sensing tasks need long indivisible sensor reading operations, but most prior work has limited their focus to the forward progress of computation-only tasks. In this paper, we propose a scheduling framework to execute real-time periodic tasks with atomic sensing operations. Our proposed method keeps track of time progress and ensures the periodic execution of sensing tasks while efficiently utilizing intermittent power sources. We provide schedulability analysis to determine if a taskset is schedulable under a given charging condition. As a proof-of-concept, we design a custom programmable RFID tag device, called R’tag, and demonstrate the effectiveness of our framework in a realistic sensing application. Evaluation results show that the proposed method satisfies the real-time task execution requirements on IPDs in terms of task scheduling, timekeeping, and periodic sensing while significantly outperforming prior work.
  2. In this paper, we present RT-Gang: a novel realtime gang scheduling framework that enforces a one-gang-at-atime policy. We find that, in a multicore platform, co-scheduling multiple parallel real-time tasks would require highly pessimistic worst-case execution time (WCET) and schedulability analysis—even when there are enough cores—due to contention in shared hardware resources such as cache and DRAM controller. In RT-Gang, all threads of a parallel real-time task form a real-time gang and the scheduler globally enforces the one-gangat-a-time scheduling policy to guarantee tight and accurate task WCET. To minimize under-utilization, we integrate a state-of-the-art memory bandwidth throttling framework to allow safe execution of best-effort tasks. Specifically, any idle cores, if exist, are used to schedule best-effort tasks but their maximum memory bandwidth usages are strictly throttled to tightly bound interference to real-time gang tasks. We implement RT-Gang in the Linux kernel and evaluate it on two representative embedded multicore platforms using both synthetic and real-world DNN workloads. The results show that RT-Gang dramatically improves system predictability and the overhead is negligible.
  3. When scheduling multi-mode real-time systems on multi-core platforms, a key question is how to dynamically adjust shared resources, such as cache and memory bandwidth, when resource demands change, without jeopardizing schedulability during mode changes. This paper presents Omni, a first end-to-end solution to this problem. Omni consists of a novel multi-mode resource allocation algorithm and a resource-aware schedulability test that supports general mode-change semantics as well as dynamic cache and bandwidth resource allocation. Omni's resource allocation leverages the platform's concurrency and the diversity of the tasks' demands to minimize overload during mode transitions; it does so by intelligently co-distributing tasks and resources across cores. Omni's schedulability test ensures predictable mode transitions, and it takes into account mode-change effects on the resource demands on different cores, so as to best match their dynamic needs using the available resources. We have implemented a prototype of Omni, and we have evaluated it using randomly generated multi-mode systems with several real-world benchmarks as the workload. Our results show that Omni has low overhead, and that it is substantially more effective in improving schedulability than the state of the art
  4. The concept of Industry 4.0 introduces the unification of industrial Internet-of-Things (IoT), cyber physical systems, and data-driven business modeling to improve production efficiency of the factories. To ensure high production efficiency, Industry 4.0 requires industrial IoT to be adaptable, scalable, real-time, and reliable. Recent successful industrial wireless standards such as WirelessHART appeared as a feasible approach for such industrial IoT. For reliable and real-time communication in highly unreliable environments, they adopt a high degree of redundancy. While a high degree of redundancy is crucial to real-time control, it causes a huge waste of energy, bandwidth, and time under a centralized approach and are therefore less suitable for scalability and handling network dynamics. To address these challenges, we propose DistributedHART—a distributed real-time scheduling system for WirelessHART networks. The essence of our approach is to adopt local (node-level) scheduling through a time window allocation among the nodes that allows each node to schedule its transmissions using a real-time scheduling policy locally and online. DistributedHART obviates the need of creating and disseminating a central global schedule in our approach, thereby significantly reducing resource usage and enhancing the scalability. To our knowledge, it is the first distributed real-time multi-channel scheduler for WirelessHART. We havemore »implemented DistributedHART and experimented on a 130-node testbed. Our testbed experiments as well as simulations show at least 85% less energy consumption in DistributedHART compared to existing centralized approach while ensuring similar schedulability.« less
  5. In this paper, we consider the challenges that arise from the need to scale virtualized network functions (VNFs) at 100 Gbps line speed and beyond. Traditional VNF designs are monolithic in state management and scheduling: internally maintaining all states and operations associated with them. Without proper design considerations, it suffers from limitations when scaling at 100 Gbps link speed and beyond: the inability of efficient utilization of the cache because of the contention due to the frequent control plane activities, computational/memory-intensive tasks taking up CPU times, shares states causing the synchronization among the cores. We address these limitations by arguing for the need to granularly decompose a VNF into data/control components that are co-located within a server but can be independently scaled among the cores. To realize the approach, we design a "serverless" programming framework with novel abstraction to optimize the data components that must process packets at the line speed, reduce the contention of the data states and enable run-time scheduling of different components for improved resource utilization. The abstractions, combined with the runtime system that we design, help NFV developers focus on the logic and correctness of VNF programming without worrying about how VNFs may be scaled inmore »or out. We evaluate our platform by comparing it with monolithic approaches using different workloads and by analyzing its advantages of separation on scalability, performance determinism, and feature velocity.« less