Energy-Efficient Offloading Based on Efficient Cognitive Energy Management Scheme in Edge Computing Device with Energy Optimization
Edge devices and their associated computing techniques require energy efficiency to improve sustainability over time. The operating edge devices are timed to swap between different states to achieve stabilized energy efficiency. This article introduces a Cognitive Energy Management Scheme (CEMS) by...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/21/8273 |
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| author | Vishnu Kumar Kaliappan Aravind Babu Lalpet Ranganathan Selvaraju Periasamy Padmapriya Thirumalai Tuan Anh Nguyen Sangwoo Jeon Dugki Min Enumi Choi |
| author_facet | Vishnu Kumar Kaliappan Aravind Babu Lalpet Ranganathan Selvaraju Periasamy Padmapriya Thirumalai Tuan Anh Nguyen Sangwoo Jeon Dugki Min Enumi Choi |
| author_sort | Vishnu Kumar Kaliappan |
| collection | DOAJ |
| description | Edge devices and their associated computing techniques require energy efficiency to improve sustainability over time. The operating edge devices are timed to swap between different states to achieve stabilized energy efficiency. This article introduces a Cognitive Energy Management Scheme (CEMS) by considering the offloading and computational states for energy efficacy. The proposed scheme employs state learning for swapping the computing intervals for scheduling or offloading depending on the load. The edge devices are distributed at the time of scheduling and organized for first come, first serve for offloading features. In state learning, the reward is allocated for successful scheduling over offloading to prevent device exhaustion. The computation is therefore swapped for energy-reserved scheduling or offloading based on the previous computed reward. This cognitive management induces device allocation based on energy availability and computing time to prevent energy convergence. Cognitive management is limited in recent works due to non-linear swapping and missing features. The proposed CEMS addresses this issue through precise scheduling and earlier device exhaustion identification. The convergence issue is addressed using rewards assigned to post the state transitions. In the transition process, multiple device energy levels are considered. This consideration prevents early detection of exhaustive devices, unlike conventional wireless networks. The proposed scheme’s performance is compared using the metrics computing rate and time, energy efficacy, offloading ratio, and scheduling failures. The experimental results show that this scheme improves the computing rate and energy efficacy by 7.2% and 9.32%, respectively, for the varying edge devices. It reduces the offloading ratio, scheduling failures, and computing time by 14.97%, 7.27%, and 14.48%, respectively. |
| first_indexed | 2024-03-09T19:06:21Z |
| format | Article |
| id | doaj.art-5aaec1a657ea4d368eca007093ea4bfd |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T19:06:21Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-5aaec1a657ea4d368eca007093ea4bfd2023-11-24T04:34:34ZengMDPI AGEnergies1996-10732022-11-011521827310.3390/en15218273Energy-Efficient Offloading Based on Efficient Cognitive Energy Management Scheme in Edge Computing Device with Energy OptimizationVishnu Kumar Kaliappan0Aravind Babu Lalpet Ranganathan1Selvaraju Periasamy2Padmapriya Thirumalai3Tuan Anh Nguyen4Sangwoo Jeon5Dugki Min6Enumi Choi7Konkuk Aerospace Design Airworthiness Institute, Koknkuk University, Seoul 05029, KoreaDepartment of Computer and Information Science, Annamalai University, Chidambaram 608002, IndiaDepartment of Mathematics, Rajalakshmi Institute of Technology, Chennai 600124, IndiaMelange Academic Research Associates, Puducherry 605004, IndiaKonkuk Aerospace Design Airworthiness Institute, Koknkuk University, Seoul 05029, KoreaDepartment of Computer Science and Engineering, Konkuk University, Seoul 05029, KoreaDepartment of Computer Science and Engineering, Konkuk University, Seoul 05029, KoreaDepartment of Computer Science and Engineering, Kookmin University, Seoul 05029, KoreaEdge devices and their associated computing techniques require energy efficiency to improve sustainability over time. The operating edge devices are timed to swap between different states to achieve stabilized energy efficiency. This article introduces a Cognitive Energy Management Scheme (CEMS) by considering the offloading and computational states for energy efficacy. The proposed scheme employs state learning for swapping the computing intervals for scheduling or offloading depending on the load. The edge devices are distributed at the time of scheduling and organized for first come, first serve for offloading features. In state learning, the reward is allocated for successful scheduling over offloading to prevent device exhaustion. The computation is therefore swapped for energy-reserved scheduling or offloading based on the previous computed reward. This cognitive management induces device allocation based on energy availability and computing time to prevent energy convergence. Cognitive management is limited in recent works due to non-linear swapping and missing features. The proposed CEMS addresses this issue through precise scheduling and earlier device exhaustion identification. The convergence issue is addressed using rewards assigned to post the state transitions. In the transition process, multiple device energy levels are considered. This consideration prevents early detection of exhaustive devices, unlike conventional wireless networks. The proposed scheme’s performance is compared using the metrics computing rate and time, energy efficacy, offloading ratio, and scheduling failures. The experimental results show that this scheme improves the computing rate and energy efficacy by 7.2% and 9.32%, respectively, for the varying edge devices. It reduces the offloading ratio, scheduling failures, and computing time by 14.97%, 7.27%, and 14.48%, respectively.https://www.mdpi.com/1996-1073/15/21/8273edge computingenergy efficiencyreward functionstate learning |
| spellingShingle | Vishnu Kumar Kaliappan Aravind Babu Lalpet Ranganathan Selvaraju Periasamy Padmapriya Thirumalai Tuan Anh Nguyen Sangwoo Jeon Dugki Min Enumi Choi Energy-Efficient Offloading Based on Efficient Cognitive Energy Management Scheme in Edge Computing Device with Energy Optimization Energies edge computing energy efficiency reward function state learning |
| title | Energy-Efficient Offloading Based on Efficient Cognitive Energy Management Scheme in Edge Computing Device with Energy Optimization |
| title_full | Energy-Efficient Offloading Based on Efficient Cognitive Energy Management Scheme in Edge Computing Device with Energy Optimization |
| title_fullStr | Energy-Efficient Offloading Based on Efficient Cognitive Energy Management Scheme in Edge Computing Device with Energy Optimization |
| title_full_unstemmed | Energy-Efficient Offloading Based on Efficient Cognitive Energy Management Scheme in Edge Computing Device with Energy Optimization |
| title_short | Energy-Efficient Offloading Based on Efficient Cognitive Energy Management Scheme in Edge Computing Device with Energy Optimization |
| title_sort | energy efficient offloading based on efficient cognitive energy management scheme in edge computing device with energy optimization |
| topic | edge computing energy efficiency reward function state learning |
| url | https://www.mdpi.com/1996-1073/15/21/8273 |
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