An optimal energy aware aperiodic task server for autonomous IoT sensors

A real-time applicative software consists of both aperiodic and periodic tasks. The periodic tasks have regular arrival times and strict deadlines. The aperiodic tasks have irregular arrival times and no deadline. The objective of an optimal aperiodic task server is to guarantee minimal response times for the aperiodic tasks and no violation of hard deadlines for periodic tasks. We consider a real-time energy harvesting system composed of energy harvester, energy storage unit, uniprocessing unit and the real-time tasks. We introduce a novel periodic task scheduler, namely SSP which is an extension of the slack stealing server so as to cope with fluctuations in energy availability. Experimental results show that the proposed algorithm achieves better performance in terms of aperiodic responsiveness. Simulations have been conducted for various settings of workloads and harvested energy profiles.

ITANS: Incremental Task and Network Scheduling for Time-Sensitive Networks

The rapid development in information and communication technology confronts designers of real-time systems with new challenges that have arisen due to the increasing amount of data and an intensified interconnection of functions. This is e.g. driven by recent trends such as automated driving in the automotive field and digitization in factory automation. For distributed safety-critical systems, this progression has the impact that the complexity of scheduling tasks with precedence constraints organized in so-called task chains increases the more data has to be exchanged between tasks and the more functions are involved. Especially when data has to be transmitted over an Ethernet-based communication network, the coordination between the processing tasks running on different end-devices and the communication network has to be ensured to meet strict end-to-end deadlines of task chains. In this work, we present a heuristic approach that computes schedules for distributed and data-dependent task chains consisting of preemptive and periodic tasks, taking into account the network communication delays of time-sensitive networks. Our algorithm is able to solve large problems for synthetic network topologies with randomized data dependencies in a few seconds. A high success rate was achieved, which can also be further enhanced by relaxing the deadline conditions.

ITANS: Incremental Task and Network Scheduling for Time-Sensitive Networks

The rapid development in information and communication technology confronts designers of real-time systems with new challenges that have arisen due to the increasing amount of data and an intensified interconnection of functions. This is e.g. driven by recent trends such as automated driving in the automotive field and digitization in factory automation. For distributed safety-critical systems, this progression has the impact that the complexity of scheduling tasks with precedence constraints organized in so-called task chains increases the more data has to be exchanged between tasks and the more functions are involved. Especially when data has to be transmitted over an Ethernet-based communication network, the coordination between the processing tasks running on different end-devices and the communication network has to be ensured to meet strict end-to-end deadlines of task chains. In this work, we present a heuristic approach that computes schedules for distributed and data-dependent task chains consisting of preemptive and periodic tasks, taking into account the network communication delays of time-sensitive networks. Our algorithm is able to solve large problems for synthetic network topologies with randomized data dependencies in a few seconds. A high success rate was achieved, which can also be further enhanced by relaxing the deadline conditions.

A Novel Feasibility Test for Energy Minimization of Real-Time Mixed Task Sets for DVS-Enabled Uniprocessor System

With advancements in computing and communication technologies on mobile devices, the performance requirements of embedded processors have significantly increased, resulting in a corresponding increase in its energy consumption. Dynamic scaling of operating voltage and operating frequency has a strong correlation to energy minimization in CMOS real-time circuits. Simultaneous optimization of ([Formula: see text], [Formula: see text] pairs under dynamic activity levels is thus extensively investigated over several years. The supply voltage is tuned dynamically during runtime (DVS), with a fixed threshold voltage, to achieve energy minimization. This work addresses the issue of maximizing the energy efficiency of real-time periodic, aperiodic and mixed task sets, in a uniprocessor system, by developing a novel task feasibility methodology, with a novel processor performance-based constraint, to generate the optimal operating supply voltage to the individual task of task sets. The energy minimization of real-time mixed task sets is formulated as Geometric Programming (GP) problem, by varying frequency for periodic tasks sets and keeping fixed frequency for aperiodic tasks set, over a range of task sets and hence computing optimal operating voltages. Simulation experiments show energy savings on the cumulative basis of 50%, 38% and 29% for periodic, aperiodic and mixed task sets, respectively, based on the processing timing constraints of task sets.

Analysis of FreeRTOS Overheads on Periodic Tasks

Real-Time Operating Systems (RTOS) have their own modules that need to be executed to manage system resources and such modules add overhead to task response times. FreeRTOS is used for experimental purposes since its is a widely used open-source RTOS. This work presents the investigation of two important sources of overhead: Function Tick, a FreeRTOS time marker, and the Context Switch between tasks. In this paper we also describe a model for reducing Tick analysis pessimism due to its temporal variation. Experiments measuring the execution time of Tick and Context Switch on ARM-Cortex M4 microprocessor were made to present the Best-Case Execution Time and the Worst-Case Execution time within a periodic task scenario. Measurements are used to validate the analytic models.