Green Fog Planning for Optimal Internet-of-Thing Task Scheduling
The incoming 5G technology is expected to proliferate tremendous internet-of-thing (IoT) services with real-time and mobility requirements, which are quite different from the legacy cloud services. Due to the centralized management relying on distant datacenters, cloud computing is short of satisfyi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8941132/ |
| _version_ | 1818856900499865600 |
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| author | Zhiming He Yin Zhang Byungchul Tak Limei Peng |
| author_facet | Zhiming He Yin Zhang Byungchul Tak Limei Peng |
| author_sort | Zhiming He |
| collection | DOAJ |
| description | The incoming 5G technology is expected to proliferate tremendous internet-of-thing (IoT) services with real-time and mobility requirements, which are quite different from the legacy cloud services. Due to the centralized management relying on distant datacenters, cloud computing is short of satisfying the stringent IoT requirements, such as ultra-low latency, mobility, etc. Instead, distributed edge computing, such as fog computing has been coined as a promising approach and has received enormous attention in recent years. In this paper, to optimally provision the huge volume of IoT services with significant diversity, we propose to efficiently organize the leisure network devices in the network edge to form fog networks (fogs), which are then integrated with the cloud to provide storage and computing resources. Specifically, we propose two Integer Linear Programming (ILP) models to solve the fog planning issue under the integrated Cloud-Fog (iCloudFog) framework. In the first ILP model, the objective is to minimize the CAPEX cost caused by planning fogs and the OPEX cost caused by utilizing the planned fogs. In the second ILP model, the objective is to minimize the power consumption while maximizing the number of successfully provisioned IoT tasks on the planned fogs. The proposed ILP models are numerically evaluated by considering different IoT task requirements, such as real-time and mobility. The numerical results show that efficiently planned fogs can help to reduce the planning overhead while satisfying diverse IoT task requirements. |
| first_indexed | 2024-12-19T08:31:51Z |
| format | Article |
| id | doaj.art-5630cd2bb5f74d778af2f6ddf8d9d1e8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:31:51Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5630cd2bb5f74d778af2f6ddf8d9d1e82022-12-21T20:29:10ZengIEEEIEEE Access2169-35362020-01-0181224123410.1109/ACCESS.2019.29619528941132Green Fog Planning for Optimal Internet-of-Thing Task SchedulingZhiming He0https://orcid.org/0000-0003-4504-6275Yin Zhang1https://orcid.org/0000-0002-1772-0763Byungchul Tak2https://orcid.org/0000-0002-8204-6816Limei Peng3https://orcid.org/0000-0001-9984-9861School of Computer Science and Engineering, Kyungpook National University, Daegu, South KoreaSchool of Information and Safety Engineering, Zhongnan University of Economics and Law (ZUEL), Wuhan, ChinaSchool of Computer Science and Engineering, Kyungpook National University, Daegu, South KoreaSchool of Computer Science and Engineering, Kyungpook National University, Daegu, South KoreaThe incoming 5G technology is expected to proliferate tremendous internet-of-thing (IoT) services with real-time and mobility requirements, which are quite different from the legacy cloud services. Due to the centralized management relying on distant datacenters, cloud computing is short of satisfying the stringent IoT requirements, such as ultra-low latency, mobility, etc. Instead, distributed edge computing, such as fog computing has been coined as a promising approach and has received enormous attention in recent years. In this paper, to optimally provision the huge volume of IoT services with significant diversity, we propose to efficiently organize the leisure network devices in the network edge to form fog networks (fogs), which are then integrated with the cloud to provide storage and computing resources. Specifically, we propose two Integer Linear Programming (ILP) models to solve the fog planning issue under the integrated Cloud-Fog (iCloudFog) framework. In the first ILP model, the objective is to minimize the CAPEX cost caused by planning fogs and the OPEX cost caused by utilizing the planned fogs. In the second ILP model, the objective is to minimize the power consumption while maximizing the number of successfully provisioned IoT tasks on the planned fogs. The proposed ILP models are numerically evaluated by considering different IoT task requirements, such as real-time and mobility. The numerical results show that efficiently planned fogs can help to reduce the planning overhead while satisfying diverse IoT task requirements.https://ieeexplore.ieee.org/document/8941132/Fog computingcloud computingIoTnetwork planningenergy efficiency |
| spellingShingle | Zhiming He Yin Zhang Byungchul Tak Limei Peng Green Fog Planning for Optimal Internet-of-Thing Task Scheduling IEEE Access Fog computing cloud computing IoT network planning energy efficiency |
| title | Green Fog Planning for Optimal Internet-of-Thing Task Scheduling |
| title_full | Green Fog Planning for Optimal Internet-of-Thing Task Scheduling |
| title_fullStr | Green Fog Planning for Optimal Internet-of-Thing Task Scheduling |
| title_full_unstemmed | Green Fog Planning for Optimal Internet-of-Thing Task Scheduling |
| title_short | Green Fog Planning for Optimal Internet-of-Thing Task Scheduling |
| title_sort | green fog planning for optimal internet of thing task scheduling |
| topic | Fog computing cloud computing IoT network planning energy efficiency |
| url | https://ieeexplore.ieee.org/document/8941132/ |
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