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An intelligent energy-efficient approach for managing IoE tasks in cloud platforms

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Abstract

Today, cloud platforms for Internet of Everything (IoE) are facilitating organizational and industrial growth, and have different requirements based on their different purposes. Usual task scheduling algorithms for distributed environments such as group of clusters, networks, and clouds, focus only on the shortest execution time, regardless of the power consumption. Network energy can be optimized if tasks are properly scheduled to be implemented in virtual machines, thus achieving green computing. In this research, Dynamic Voltage Frequency Dcaling (DVFS) is used in two different ways, to select a suitable candidate for scheduling the tasks with the help of an Artificial Intelligence (AI) approach. First, the GIoTDVFS_SFB method based on sorting processor elements in Cloud has been considered to handle Task Scheduling problem in the Clouds system. Alternatively, the GIoTDVFS_mGA microgenetic method has been used to select suitable candidates. The proposed mGA and SFB methods are compared with SLAbased suggested for Cloud environments, and it is shown that the Makespan and Gain in benchmarks 512 and 1024 are optimized in the proposed method. In addition, the Energy Consumption (EC) of Real PM (RPMs) against the numeral of Tasks has been considered with that of PAFogIoTDVFS and EnergyAwareDVFS methods in this area.

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Acknowledgements

Funding information: Peng Cheng Laboratory Project, Grant/Award Number: PCL2021A02; National Natural Science Foundation of China, Grant/Award Number: 61872110; Shenzhen Science and Technology Research and Development Foundation, Grant/Award Number: JCYJ20190806143418198; Key-Area Research and Development Program of Guangdong Province, Grant/Award Number: 2020B0101360001; National Key Research and Development Program of China, Grant/Award Number: 2020YFB1406902.

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Authors and Affiliations

  1. Department of Computer Science and Technology (Cyberspace Security), Harbin Institute of Technology, Shenzhen, China

    Amir Javadpour & Weizhe Zhang

  2. ADiT-Lab, Electrotechnics and Telecommunications Department, Instituto Politécnico de Viana do Castelo, Porto, Portugal

    Amir Javadpour

  3. Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan

    AmirHossein Nafei

  4. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Forough Ja’fari

  5. Electrotechnics and Telecommunications Department, Instituto Politécnico de Viana do Castelo, Porto, Portugal

    Pedro Pinto

  6. International Graduate Institute of AI, National Yunlin University of Science and Technology, Douliu, Taiwan

    Arun Kumar Sangaiah

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  1. Amir Javadpour
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  2. AmirHossein Nafei
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  3. Forough Ja’fari
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  4. Pedro Pinto
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  5. Weizhe Zhang
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  6. Arun Kumar Sangaiah
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Correspondence to Weizhe Zhang or Arun Kumar Sangaiah.

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Javadpour, A., Nafei, A., Ja’fari, F. et al. An intelligent energy-efficient approach for managing IoE tasks in cloud platforms. J Ambient Intell Human Comput 14, 3963–3979 (2023). https://doi.org/10.1007/s12652-022-04464-x

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