An Adaptive Emergency First Intelligent Scheduling Algorithm for Efficient Task Management and Scheduling in Hybrid of Hard Real-Time and Soft Real-Time Embedded IoT Systems

Abstract

Industrial revolution is advancing, and the augmented role of autonomous technology and embedded Internet of Things (IoT) systems is at its vanguard. In autonomous technology, real-time systems and real-time computing are of core importance. It is crucial for embedded IoT devices to respond in real-time; along with fulfilling all the constraints. Many combinations for existing approaches have been proposed with different trade-offs between the resources constraints and tasks dropping rate. Hence, it highlights the significance of a task scheduler which not only takes care of complex nature task input; but also maximizes the CPU throughput. A complex nature task input is when combinations of hard real-time tasks and soft real-time tasks, with different priorities and urgency measures, arrive at the scheduler. In this work, we propose a custom tailored adaptive and intelligent scheduling algorithm for the efficient execution and management of hard and soft real time tasks in embedded IoT systems. The proposed scheduling algorithm aims to distribute the CPU resources fairly to the possibly starving, in overloaded cases, soft real-time tasks while focusing on the execution of high priority hard real-time tasks as its primary objective. The proposal is achieved with the help of two intelligent measures; Urgency Measure (UM) and Failure Measure (FM). The proposed mechanism reduces the rate of tasks missed and the rate of tasks starved, by utilizing the free CPU units for maximum CPU utilization and quick response times. We have performed comparisons of our proposed scheme based on performance metrics as percentage of task instances missed, number of tasks with missed instances, and tasks starvation rate to evaluate the CPU utilization. We first compare our proposed approach with multiple traditional and combined scheduling approaches, and then we evaluate the effect of intelligent modules by comparing the intelligent FEF with non-intelligent FEF. We also evaluate the proposed algorithm in contrast to the most commonly-used hybrid scheduling scheme in embedded systems. The results show that the proposed algorithm out performs the other algorithms, by significantly reducing the task starvation rate and increasing the CPU utilization.

Design of Lightweight Driver-Assistance System for Safe Driving in Electric Vehicles

“…The main difference is that, if modern technologies such as edge computing and RT-IoT are considered in the design of EVs, they can help in offloading the system-on-chip ECUs. It has been suggested in these studies that it is quite possible to model an autonomous EV as an RT-IoT system [13,24]. It is envisioned that modern EVs are real-time IoT systems with the constraint of energy and battery.…”

“…Other scheduling algorithms include hierarchical scheduler [43,44,45] based on Scheduling Framework For Fast Prototyping (SF3P) [46] and is used to explore and prototype a hierarchical composition of the real-time scheduler. A younger algorithm for emergency first scenarios of complex tasks having different types was proposed by Malik et al [13] in IoT space and suggested that, if tasks are carefully modeled, it will never drop a hard-deadline task. The current position of the state-of-the-art is summarised in Table 1.…”

“…First and foremost, the guarantee to execute the task within an IoT network has been an enduring hurdle and cannot be 100% assured due to the nondeterministic nature of network delays. In some recent research studies, however, this can be estimated [3,13], paving a way to redesign the system using this approach. Secondly, since EVs perform certain tasks simultaneously at a specific interval of time, proper attention should be made to ensure the correct execution of them.…”

“…Experiments are carried out to assess the reliability of the proposed assistance system with tradition real-time preemptive scheduling algorithms such as Earliest Deadline First (EDF) [15] and Rate Monotonic (RM) [16]. As part of the proposed system, a control scheduling algorithm is also designed and is evaluated with a modern RT-IoT-based Fair Emergency First (FEF) [13] for complex and hybrid task sets. The comparative results indicate that the task-dropping rate of highly critical tasks is decidedly less in the control scheduling algorithm as compared to the other general scheduling algorithms.…”

Performance of Dynamic Queue Based Minimal Deadline First Scheduling Algorithm In Multicore System Under Real Time And Non-Real Time Kernel Environment

“…This scheduling is the most significant component of processing in real-time systems in order to prioritize and perform the significant tasks within the deadline and also to manage all remaining tasks with low priority by proper allocation of these tasks during the idle time of the processor. Hence, this leads to proper utilization of the processor while increasing the performance during the necessary time [9].…”