Offloading Decision and Resource Allocation in Mobile Edge Computing for Cost and Latency Efficiencies in Real-Time IoT
Internet of Things (IoT) devices can integrate with applications requiring intensive contextual data processing, intelligent vehicle control, healthcare remote sensing, VR, data mining, traffic management, and interactive applications. However, there are computationally intensive tasks that need to...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/13/7/1218 |
| _version_ | 1797212685555728384 |
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| author | Chanthol Eang Seyha Ros Seungwoo Kang Inseok Song Prohim Tam Sa Math Seokhoon Kim |
| author_facet | Chanthol Eang Seyha Ros Seungwoo Kang Inseok Song Prohim Tam Sa Math Seokhoon Kim |
| author_sort | Chanthol Eang |
| collection | DOAJ |
| description | Internet of Things (IoT) devices can integrate with applications requiring intensive contextual data processing, intelligent vehicle control, healthcare remote sensing, VR, data mining, traffic management, and interactive applications. However, there are computationally intensive tasks that need to be completed quickly within the time constraints of IoT devices. To address this challenge, researchers have proposed computation offloading, where computing tasks are sent to edge servers instead of being executed locally on user devices. This approach involves using edge servers located near users in cellular network base stations, and also known as Mobile Edge Computing (MEC). The goal is to offload tasks to edge servers, optimizing both latency and energy consumption. The main objective of this paper mentioned in the summary is to design an algorithm for time- and energy-optimized task offloading decision-making in MEC environments. Therefore, we developed a Lagrange Duality Resource Optimization Algorithm (LDROA) to optimize for both decision offloading and resource allocation for tasks, whether to locally execute or offload to an edge server. The LDROA technique produces superior simulation outcomes in terms of task offloading, with improved performance in computation latency and cost usage compared to conventional methods like Random Offloading, Load Balancing, and the Greedy Latency Offloading scheme. |
| first_indexed | 2024-04-24T10:46:19Z |
| format | Article |
| id | doaj.art-242a8868e09e40b4b89b967ce3cc1f1d |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-24T10:46:19Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-242a8868e09e40b4b89b967ce3cc1f1d2024-04-12T13:17:06ZengMDPI AGElectronics2079-92922024-03-01137121810.3390/electronics13071218Offloading Decision and Resource Allocation in Mobile Edge Computing for Cost and Latency Efficiencies in Real-Time IoTChanthol Eang0Seyha Ros1Seungwoo Kang2Inseok Song3Prohim Tam4Sa Math5Seokhoon Kim6Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of KoreaDepartment of Software Convergence, Soonchunhyang University, Asan 31538, Republic of KoreaDepartment of Software Convergence, Soonchunhyang University, Asan 31538, Republic of KoreaDepartment of Software Convergence, Soonchunhyang University, Asan 31538, Republic of KoreaDepartment of Software Convergence, Soonchunhyang University, Asan 31538, Republic of KoreaDepartment of Telecommunication and Electronic Engineering, Royal University of Phnom Penh, Phnom Penh 12156, CambodiaDepartment of Software Convergence, Soonchunhyang University, Asan 31538, Republic of KoreaInternet of Things (IoT) devices can integrate with applications requiring intensive contextual data processing, intelligent vehicle control, healthcare remote sensing, VR, data mining, traffic management, and interactive applications. However, there are computationally intensive tasks that need to be completed quickly within the time constraints of IoT devices. To address this challenge, researchers have proposed computation offloading, where computing tasks are sent to edge servers instead of being executed locally on user devices. This approach involves using edge servers located near users in cellular network base stations, and also known as Mobile Edge Computing (MEC). The goal is to offload tasks to edge servers, optimizing both latency and energy consumption. The main objective of this paper mentioned in the summary is to design an algorithm for time- and energy-optimized task offloading decision-making in MEC environments. Therefore, we developed a Lagrange Duality Resource Optimization Algorithm (LDROA) to optimize for both decision offloading and resource allocation for tasks, whether to locally execute or offload to an edge server. The LDROA technique produces superior simulation outcomes in terms of task offloading, with improved performance in computation latency and cost usage compared to conventional methods like Random Offloading, Load Balancing, and the Greedy Latency Offloading scheme.https://www.mdpi.com/2079-9292/13/7/1218offloading decisionLagrange duality optimizationmobile edge computingreal-time IoTresource allocation |
| spellingShingle | Chanthol Eang Seyha Ros Seungwoo Kang Inseok Song Prohim Tam Sa Math Seokhoon Kim Offloading Decision and Resource Allocation in Mobile Edge Computing for Cost and Latency Efficiencies in Real-Time IoT Electronics offloading decision Lagrange duality optimization mobile edge computing real-time IoT resource allocation |
| title | Offloading Decision and Resource Allocation in Mobile Edge Computing for Cost and Latency Efficiencies in Real-Time IoT |
| title_full | Offloading Decision and Resource Allocation in Mobile Edge Computing for Cost and Latency Efficiencies in Real-Time IoT |
| title_fullStr | Offloading Decision and Resource Allocation in Mobile Edge Computing for Cost and Latency Efficiencies in Real-Time IoT |
| title_full_unstemmed | Offloading Decision and Resource Allocation in Mobile Edge Computing for Cost and Latency Efficiencies in Real-Time IoT |
| title_short | Offloading Decision and Resource Allocation in Mobile Edge Computing for Cost and Latency Efficiencies in Real-Time IoT |
| title_sort | offloading decision and resource allocation in mobile edge computing for cost and latency efficiencies in real time iot |
| topic | offloading decision Lagrange duality optimization mobile edge computing real-time IoT resource allocation |
| url | https://www.mdpi.com/2079-9292/13/7/1218 |
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