Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks
Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end us...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/4/1484 |
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| author | Md Delowar Hossain Tangina Sultana Md Alamgir Hossain Md Imtiaz Hossain Luan N. T. Huynh Junyoung Park Eui-Nam Huh |
| author_facet | Md Delowar Hossain Tangina Sultana Md Alamgir Hossain Md Imtiaz Hossain Luan N. T. Huynh Junyoung Park Eui-Nam Huh |
| author_sort | Md Delowar Hossain |
| collection | DOAJ |
| description | Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and utilizes neighboring edge servers. The main objective of the FTOM scheme is to select the optimal target node for task offloading based on server capacity, latency sensitivity, and the network’s condition. Our proposed scheme can make dynamic decisions where local or nearby MEC servers are preferred for offloading delay-sensitive tasks, and delay-tolerant high resource-demand tasks are offloaded to a remote cloud server. Simulation results affirm that our proposed FTOM scheme significantly improves the rate of successfully executing offloaded tasks by approximately 68.5%, and reduces task completion time by 66.6%, when compared with a local edge offloading (LEO) scheme. The improved and reduced rates are 32.4% and 61.5%, respectively, when compared with a two-tier edge orchestration-based offloading (TTEO) scheme. They are 8.9% and 47.9%, respectively, when compared with a fuzzy orchestration-based load balancing (FOLB) scheme, approximately 3.2% and 49.8%, respectively, when compared with a fuzzy workload orchestration-based task offloading (WOTO) scheme, and approximately 38.6%% and 55%, respectively, when compared with a fuzzy edge-orchestration based collaborative task offloading (FCTO) scheme. |
| first_indexed | 2024-03-09T00:41:09Z |
| format | Article |
| id | doaj.art-ed0bef3881d44967a6cedbc86e2ca32e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T00:41:09Z |
| publishDate | 2021-02-01 |
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| series | Sensors |
| spelling | doaj.art-ed0bef3881d44967a6cedbc86e2ca32e2023-12-11T17:52:03ZengMDPI AGSensors1424-82202021-02-01214148410.3390/s21041484Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled NetworksMd Delowar Hossain0Tangina Sultana1Md Alamgir Hossain2Md Imtiaz Hossain3Luan N. T. Huynh4Junyoung Park5Eui-Nam Huh6Department of Computer Science and Engineering, Kyung Hee University, Global Campus, Yongin-si 17104, KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Global Campus, Yongin-si 17104, KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Global Campus, Yongin-si 17104, KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Global Campus, Yongin-si 17104, KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Global Campus, Yongin-si 17104, KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Global Campus, Yongin-si 17104, KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Global Campus, Yongin-si 17104, KoreaMulti-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and utilizes neighboring edge servers. The main objective of the FTOM scheme is to select the optimal target node for task offloading based on server capacity, latency sensitivity, and the network’s condition. Our proposed scheme can make dynamic decisions where local or nearby MEC servers are preferred for offloading delay-sensitive tasks, and delay-tolerant high resource-demand tasks are offloaded to a remote cloud server. Simulation results affirm that our proposed FTOM scheme significantly improves the rate of successfully executing offloaded tasks by approximately 68.5%, and reduces task completion time by 66.6%, when compared with a local edge offloading (LEO) scheme. The improved and reduced rates are 32.4% and 61.5%, respectively, when compared with a two-tier edge orchestration-based offloading (TTEO) scheme. They are 8.9% and 47.9%, respectively, when compared with a fuzzy orchestration-based load balancing (FOLB) scheme, approximately 3.2% and 49.8%, respectively, when compared with a fuzzy workload orchestration-based task offloading (WOTO) scheme, and approximately 38.6%% and 55%, respectively, when compared with a fuzzy edge-orchestration based collaborative task offloading (FCTO) scheme.https://www.mdpi.com/1424-8220/21/4/1484multi-access edge computingorchestratortask offloadingfuzzy logic5G |
| spellingShingle | Md Delowar Hossain Tangina Sultana Md Alamgir Hossain Md Imtiaz Hossain Luan N. T. Huynh Junyoung Park Eui-Nam Huh Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks Sensors multi-access edge computing orchestrator task offloading fuzzy logic 5G |
| title | Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks |
| title_full | Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks |
| title_fullStr | Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks |
| title_full_unstemmed | Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks |
| title_short | Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks |
| title_sort | fuzzy decision based efficient task offloading management scheme in multi tier mec enabled networks |
| topic | multi-access edge computing orchestrator task offloading fuzzy logic 5G |
| url | https://www.mdpi.com/1424-8220/21/4/1484 |
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