IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i8p2192-d222135.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/8/2192/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/8/2192/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yanfang Zhang & Mushang Lee, 2019. "A Hybrid Model for Addressing the Relationship between Financial Performance and Sustainable Development," Sustainability, MDPI, vol. 11(10), pages 1-15, May.
    2. Jie Gao & Xinping Huang & Lili Zhang, 2019. "Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018," Sustainability, MDPI, vol. 11(23), pages 1-18, November.
    3. Michela Arnaboldi, 2018. "The Missing Variable in Big Data for Social Sciences: The Decision-Maker," Sustainability, MDPI, vol. 10(10), pages 1-18, September.
    4. Anna Visvizi & Miltiadis D. Lytras, 2019. "Sustainable Smart Cities and Smart Villages Research: Rethinking Security, Safety, Well-being, and Happiness," Sustainability, MDPI, vol. 12(1), pages 1-4, December.
    5. Miltiadis D. Lytras & Anna Visvizi & Akila Sarirete, 2019. "Clustering Smart City Services: Perceptions, Expectations, Responses," Sustainability, MDPI, vol. 11(6), pages 1-19, March.
    6. Faisal Mehmood & Shabir Ahmad & DoHyeun Kim, 2019. "Design and Implementation of an Interworking IoT Platform and Marketplace in Cloud of Things," Sustainability, MDPI, vol. 11(21), pages 1-22, October.
    7. Abdulrahman Obaid AI-Youbi & Abdulmonem Al-Hayani & Hisham J. Bardesi & Mohammed Basheri & Miltiadis D. Lytras & Naif Radi Aljohani, 2020. "The King Abdulaziz University (KAU) Pandemic Framework: A Methodological Approach to Leverage Social Media for the Sustainable Management of Higher Education in Crisis," Sustainability, MDPI, vol. 12(11), pages 1-21, May.
    8. Yanru Lu & Kai Cao, 2019. "Spatial Analysis of Big Data Industrial Agglomeration and Development in China," Sustainability, MDPI, vol. 11(6), pages 1-22, March.
    9. Sunmin Lee & Yunjung Hyun & Moung-Jin Lee, 2019. "Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
    10. Shet, Sateesh V. & Pereira, Vijay, 2021. "Proposed managerial competencies for Industry 4.0 – Implications for social sustainability," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    11. Miltiadis D. Lytras & Anna Visvizi, 2018. "Who Uses Smart City Services and What to Make of It: Toward Interdisciplinary Smart Cities Research," Sustainability, MDPI, vol. 10(6), pages 1-16, June.
    12. Abdulrahman Housawi & Amal Al Amoudi & Basim Alsaywid & Miltiadis Lytras & Yara H. bin Μoreba & Wesam Abuznadah & Sami A. Alhaidar, 2020. "Evaluation of Key Performance Indicators (KPIs) for Sustainable Postgraduate Medical Training: An Opportunity for Implementing an Innovative Approach to Advance the Quality of Training Programs at the," Sustainability, MDPI, vol. 12(19), pages 1-37, September.
    13. Grigorios Kyriakopoulos & Stamatios Ntanos & Theodoros Anagnostopoulos & Nikolaos Tsotsolas & Ioannis Salmon & Klimis Ntalianis, 2020. "Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes," IJERPH, MDPI, vol. 17(2), pages 1-14, January.
    14. Patricia Ordóñez de Pablos & Miltiadis Lytras, 2018. "Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness," Sustainability, MDPI, vol. 10(6), pages 1-7, June.
    15. Lily Popova Zhuhadar & Miltiadis D. Lytras, 2023. "The Application of AutoML Techniques in Diabetes Diagnosis: Current Approaches, Performance, and Future Directions," Sustainability, MDPI, vol. 15(18), pages 1-24, September.
    16. Lihuan Guo & Dongqiang Guo & Wei Wang & Hongwei Wang & Yenchun Jim Wu, 2018. "Distance Diffusion of Home Bias for Crowdfunding Campaigns between Categories: Insights from Data Analytics," Sustainability, MDPI, vol. 10(4), pages 1-22, April.
    17. Yung Yau & Wai Kin Lau, 2018. "Big Data Approach as an Institutional Innovation to Tackle Hong Kong’s Illegal Subdivided Unit Problem," Sustainability, MDPI, vol. 10(8), pages 1-17, August.
    18. Yenchun Jim Wu & Chih-Hung Yuan & Chia-I Pan, 2018. "Entrepreneurship Education: An Experimental Study with Information and Communication Technology," Sustainability, MDPI, vol. 10(3), pages 1-13, March.
    19. Miltiadis D. Lytras & Anna Visvizi, 2019. "Big Data Research for Social Science and Social Impact," Sustainability, MDPI, vol. 12(1), pages 1-4, December.
    20. Yueqiang Xu & Petri Ahokangas & Jean-Nicolas Louis & Eva Pongrácz, 2019. "Electricity Market Empowered by Artificial Intelligence: A Platform Approach," Energies, MDPI, vol. 12(21), pages 1-21, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:8:p:2192-:d:222135. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.