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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

Author

Listed:
  • Sehrish Malik

    (Computer Engineering Department, Jeju National University, Jeju-si 63243, Korea)

  • Shabir Ahmad

    (Computer Engineering Department, Jeju National University, Jeju-si 63243, Korea)

  • Israr Ullah

    (Computer Engineering Department, Jeju National University, Jeju-si 63243, Korea)

  • Dong Hwan Park

    (Electronics and Telecommunications Research Institute, Daejeon-si 34129, Korea)

  • DoHyeun Kim

    (Computer Engineering Department, Jeju National University, Jeju-si 63243, Korea)

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.

Suggested Citation

  • Sehrish Malik & Shabir Ahmad & Israr Ullah & Dong Hwan Park & DoHyeun Kim, 2019. "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," Sustainability, MDPI, vol. 11(8), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:8:p:2192-:d:222135
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    References listed on IDEAS

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    1. Miltiadis D. Lytras & Vijay Raghavan & Ernesto Damiani, 2017. "Big Data and Data Analytics Research: From Metaphors to Value Space for Collective Wisdom in Human Decision Making and Smart Machines," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(1), pages 1-10, January.
    2. Shabir Ahmad & Sehrish Malik & Israr Ullah & Dong-Hwan Park & Kwangsoo Kim & DoHyeun Kim, 2019. "Towards the Design of a Formal Verification and Evaluation Tool of Real-Time Tasks Scheduling of IoT Applications," Sustainability, MDPI, vol. 11(1), pages 1-28, January.
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    Cited by:

    1. Gomatheeshwari Balasekaran & Selvakumar Jayakumar & Rocío Pérez de Prado, 2021. "An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning," Energies, MDPI, vol. 14(6), pages 1-22, March.

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