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.
Highlights
Manufacturing has been observed to be alive with quite a lot of new fields e.g., the 4th industrial revolution, connected devices, industry 4.0, connected factories, smart factories, and Internet of Things (IoT) embedded devices
The goal of scheduling the algorithms can diverge from situation to situation; there are many scheduling algorithms proposed in the literature, each having its own set of goals
We present a scheduling algorithm which proposes a novel solution for the real-time task input in embedded IoT systems, giving flexible parameter setting options in UM (Urgency Measure) and FM (Failure Measure), in order to consider all the system constraints for embedded IoT systems of different nature, and minimizing the starvation rate of the tasks
Summary
Manufacturing has been observed to be alive with quite a lot of new fields e.g., the 4th industrial revolution, connected devices, industry 4.0, connected factories, smart factories, and Internet of Things (IoT) embedded devices. Nowadays we are standing on the verge of technological insurgency that is going to change our ways of living and working. This change, with the revolution in industry 4.0, will be dissimilar to anything human beings have experienced earlier in terms of its complexity and efficiency. Recent advancements in autonomous technology and embedded systems make real-time systems and embedded IoT devices a prominent area of research and development. In real-time embedded devices, software and hardware systems are subjected to many constraints and these embedded devices need to respond within specified time constraints or deadlines. If the tasks in the system have firm deadlines the system is considered a hard real-time system with the strict constraint of tasks executing before their deadline. Some examples of hard real-time system are avionics, nuclear power plants systems, and anti-lock braking systems for automobiles; while some examples for soft-real time system are multimedia streaming and automated windshield wipers. [2]
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