Joint Optimization of Computation Offloading, Data Compression, Energy Harvesting, and Application Scenarios in Fog Computing
Fog computing is considered to be an effective method to solve the problem of high latency and high energy consumption of IoT devices. A suitable computation offloading strategy can provide a low offloading cost to the user device. Most researches on computation offloading in fog computing focus on...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9382296/ |
| _version_ | 1811273978990297088 |
|---|---|
| author | Wenle Bai Ziyang Ma Yulong Han Menglong Wu Zhongyuan Zhao Mengkun Li Chengcai Wang |
| author_facet | Wenle Bai Ziyang Ma Yulong Han Menglong Wu Zhongyuan Zhao Mengkun Li Chengcai Wang |
| author_sort | Wenle Bai |
| collection | DOAJ |
| description | Fog computing is considered to be an effective method to solve the problem of high latency and high energy consumption of IoT devices. A suitable computation offloading strategy can provide a low offloading cost to the user device. Most researches on computation offloading in fog computing focus on one or two targets to improve system performance, however, the actual system needs to meet a comprehensive demand. Therefore, the joint optimization of multi-objective in multiple scenarios is a very meaningful problem. Inspired by this, the paper highlights the joint optimization research for fog computing, which proposes a Joint Computation offloading, Data compression, Energy harvesting, and Application scenarios (JCDEA) algorithm. The related mathematical model is constructed and the cost expressions of local computing, fog computing, and cloud computing are derived. Through the proposed algorithm, solving the computation offloading strategy is transformed into solving the minimum cost and is simplified by controlling strategy factors. Moreover, five simulation experiments are conducted and the meaningful conclusions are drawn, which contain that (1) the cost of fog computing is lower than that of local and cloud computing in most time slots and cloud computing can compensate for fog computing in complex environments; (2) the cost increases approximately linear with the amount of offloaded data; (3) the number of user devices and the compression ratio affect the fog-to-cloud ratio (FCR), while the FCR affects the cost; and (4) the related offloading strategy distribution and the cost are obtained for different scenarios. The JCDEA algorithm always outperforms than that of the random selection algorithm in all scenarios. |
| first_indexed | 2024-04-12T23:09:20Z |
| format | Article |
| id | doaj.art-5a4a969b655d4df1858df7ab6afaf530 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T23:09:20Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5a4a969b655d4df1858df7ab6afaf5302022-12-22T03:12:50ZengIEEEIEEE Access2169-35362021-01-019454624547310.1109/ACCESS.2021.30677029382296Joint Optimization of Computation Offloading, Data Compression, Energy Harvesting, and Application Scenarios in Fog ComputingWenle Bai0https://orcid.org/0000-0001-8889-2530Ziyang Ma1https://orcid.org/0000-0003-4360-7024Yulong Han2Menglong Wu3https://orcid.org/0000-0003-1818-9019Zhongyuan Zhao4https://orcid.org/0000-0002-8218-7723Mengkun Li5Chengcai Wang6https://orcid.org/0000-0002-8937-6199Institute of Information, North China University of Technology, Beijing, ChinaInstitute of Information, North China University of Technology, Beijing, ChinaInstitute of Information, North China University of Technology, Beijing, ChinaInstitute of Information, North China University of Technology, Beijing, ChinaInstitute of Information and Communication, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Management, Capital Normal University, Beijing, ChinaChina Academy of Electronics and Information Technology, Beijing, ChinaFog computing is considered to be an effective method to solve the problem of high latency and high energy consumption of IoT devices. A suitable computation offloading strategy can provide a low offloading cost to the user device. Most researches on computation offloading in fog computing focus on one or two targets to improve system performance, however, the actual system needs to meet a comprehensive demand. Therefore, the joint optimization of multi-objective in multiple scenarios is a very meaningful problem. Inspired by this, the paper highlights the joint optimization research for fog computing, which proposes a Joint Computation offloading, Data compression, Energy harvesting, and Application scenarios (JCDEA) algorithm. The related mathematical model is constructed and the cost expressions of local computing, fog computing, and cloud computing are derived. Through the proposed algorithm, solving the computation offloading strategy is transformed into solving the minimum cost and is simplified by controlling strategy factors. Moreover, five simulation experiments are conducted and the meaningful conclusions are drawn, which contain that (1) the cost of fog computing is lower than that of local and cloud computing in most time slots and cloud computing can compensate for fog computing in complex environments; (2) the cost increases approximately linear with the amount of offloaded data; (3) the number of user devices and the compression ratio affect the fog-to-cloud ratio (FCR), while the FCR affects the cost; and (4) the related offloading strategy distribution and the cost are obtained for different scenarios. The JCDEA algorithm always outperforms than that of the random selection algorithm in all scenarios.https://ieeexplore.ieee.org/document/9382296/Fog computingoffloading strategyenergy harvestingcompression ratios |
| spellingShingle | Wenle Bai Ziyang Ma Yulong Han Menglong Wu Zhongyuan Zhao Mengkun Li Chengcai Wang Joint Optimization of Computation Offloading, Data Compression, Energy Harvesting, and Application Scenarios in Fog Computing IEEE Access Fog computing offloading strategy energy harvesting compression ratios |
| title | Joint Optimization of Computation Offloading, Data Compression, Energy Harvesting, and Application Scenarios in Fog Computing |
| title_full | Joint Optimization of Computation Offloading, Data Compression, Energy Harvesting, and Application Scenarios in Fog Computing |
| title_fullStr | Joint Optimization of Computation Offloading, Data Compression, Energy Harvesting, and Application Scenarios in Fog Computing |
| title_full_unstemmed | Joint Optimization of Computation Offloading, Data Compression, Energy Harvesting, and Application Scenarios in Fog Computing |
| title_short | Joint Optimization of Computation Offloading, Data Compression, Energy Harvesting, and Application Scenarios in Fog Computing |
| title_sort | joint optimization of computation offloading data compression energy harvesting and application scenarios in fog computing |
| topic | Fog computing offloading strategy energy harvesting compression ratios |
| url | https://ieeexplore.ieee.org/document/9382296/ |
| work_keys_str_mv | AT wenlebai jointoptimizationofcomputationoffloadingdatacompressionenergyharvestingandapplicationscenariosinfogcomputing AT ziyangma jointoptimizationofcomputationoffloadingdatacompressionenergyharvestingandapplicationscenariosinfogcomputing AT yulonghan jointoptimizationofcomputationoffloadingdatacompressionenergyharvestingandapplicationscenariosinfogcomputing AT menglongwu jointoptimizationofcomputationoffloadingdatacompressionenergyharvestingandapplicationscenariosinfogcomputing AT zhongyuanzhao jointoptimizationofcomputationoffloadingdatacompressionenergyharvestingandapplicationscenariosinfogcomputing AT mengkunli jointoptimizationofcomputationoffloadingdatacompressionenergyharvestingandapplicationscenariosinfogcomputing AT chengcaiwang jointoptimizationofcomputationoffloadingdatacompressionenergyharvestingandapplicationscenariosinfogcomputing |