Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing
The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks need...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-01-01
|
| Series: | Symmetry |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2073-8994/11/1/58 |
| _version_ | 1811184930458173440 |
|---|---|
| author | Xuan-Qui Pham Tien-Dung Nguyen VanDung Nguyen Eui-Nam Huh |
| author_facet | Xuan-Qui Pham Tien-Dung Nguyen VanDung Nguyen Eui-Nam Huh |
| author_sort | Xuan-Qui Pham |
| collection | DOAJ |
| description | The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devices and reduce the cost of using computing resources. In the SVMEC paradigm, a MEC provider can execute its users’ tasks by choosing one of three ways: (i) Do itself on local MEC, (ii) offload to the remote cloud, and (iii) offload to the MVNs. We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem, whose major objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources. In order to solve it, we adopt alternative optimization techniques by decomposing the original problem into two sub-problems: Resource allocation sub-problem and node selection sub-problem. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total computation overhead. |
| first_indexed | 2024-04-11T13:20:44Z |
| format | Article |
| id | doaj.art-d6fb7ecc382c4f6bbec2e87b68bf2e84 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T13:20:44Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-d6fb7ecc382c4f6bbec2e87b68bf2e842022-12-22T04:22:12ZengMDPI AGSymmetry2073-89942019-01-011115810.3390/sym11010058sym11010058Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge ComputingXuan-Qui Pham0Tien-Dung Nguyen1VanDung Nguyen2Eui-Nam Huh3Department of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, KoreaThe resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devices and reduce the cost of using computing resources. In the SVMEC paradigm, a MEC provider can execute its users’ tasks by choosing one of three ways: (i) Do itself on local MEC, (ii) offload to the remote cloud, and (iii) offload to the MVNs. We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem, whose major objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources. In order to solve it, we adopt alternative optimization techniques by decomposing the original problem into two sub-problems: Resource allocation sub-problem and node selection sub-problem. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total computation overhead.http://www.mdpi.com/2073-8994/11/1/58task offloadingresource allocationmobile cloud computingmulti-access edge computingvehicular cloudInternet of Things |
| spellingShingle | Xuan-Qui Pham Tien-Dung Nguyen VanDung Nguyen Eui-Nam Huh Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing Symmetry task offloading resource allocation mobile cloud computing multi-access edge computing vehicular cloud Internet of Things |
| title | Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing |
| title_full | Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing |
| title_fullStr | Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing |
| title_full_unstemmed | Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing |
| title_short | Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing |
| title_sort | joint node selection and resource allocation for task offloading in scalable vehicle assisted multi access edge computing |
| topic | task offloading resource allocation mobile cloud computing multi-access edge computing vehicular cloud Internet of Things |
| url | http://www.mdpi.com/2073-8994/11/1/58 |
| work_keys_str_mv | AT xuanquipham jointnodeselectionandresourceallocationfortaskoffloadinginscalablevehicleassistedmultiaccessedgecomputing AT tiendungnguyen jointnodeselectionandresourceallocationfortaskoffloadinginscalablevehicleassistedmultiaccessedgecomputing AT vandungnguyen jointnodeselectionandresourceallocationfortaskoffloadinginscalablevehicleassistedmultiaccessedgecomputing AT euinamhuh jointnodeselectionandresourceallocationfortaskoffloadinginscalablevehicleassistedmultiaccessedgecomputing |