A Deep Q-Learning Framework for Enhanced QoE and Energy Optimization in Fog Computing
Fog computing, an evolution of cloud computing, has become increasingly popular for its ability to lessen the burden of such a centralized computing paradigm by distributing tasks generated by IoT across fog layers. Effectively managing real-time, delay-sensitive, and diverse IoT applications to enh...
| Main Authors: | , , , , , |
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| Format: | Conference Paper |
| Published: |
2024
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| Online Access: | http://hdl.handle.net/20.500.11937/97523 |
| _version_ | 1848766290524635136 |
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| author | Sumona, S.T. Sabbir Hasan, S. Tamzid, A.Y. Roy, P. Razzaque, M.A. Mahmud, Md Redowan |
| author_facet | Sumona, S.T. Sabbir Hasan, S. Tamzid, A.Y. Roy, P. Razzaque, M.A. Mahmud, Md Redowan |
| author_sort | Sumona, S.T. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Fog computing, an evolution of cloud computing, has become increasingly popular for its ability to lessen the burden of such a centralized computing paradigm by distributing tasks generated by IoT across fog layers. Effectively managing real-time, delay-sensitive, and diverse IoT applications to enhance the Quality-of-Experience (QoE) presents significant challenges due to the dispersed nature and limited resources of fog nodes. Previous studies in fog computing task offloading have typically focused on either energy consumption or service delay. This paper introduces an optimization framework for task offloading within fog computing environments that aims to balance improved user QoE with reduced energy consumption, employing Mixed-Integer Linear Programming (MILP). Given the NP-hard nature of this framework, we have devised a Deep Q-Learning (DQL) based model for task offloading, termed ELTO-DQL, which aims for near-optimal solutions in polynomial time. Experimental results indicate that the ELTO-DQL model enhances energy efficiency and QoE by up to 19% and 15% respectively, outperforming contemporary benchmarks. |
| first_indexed | 2025-11-14T11:48:48Z |
| format | Conference Paper |
| id | curtin-20.500.11937-97523 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:48:48Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-975232025-07-08T07:43:53Z A Deep Q-Learning Framework for Enhanced QoE and Energy Optimization in Fog Computing Sumona, S.T. Sabbir Hasan, S. Tamzid, A.Y. Roy, P. Razzaque, M.A. Mahmud, Md Redowan Fog computing, an evolution of cloud computing, has become increasingly popular for its ability to lessen the burden of such a centralized computing paradigm by distributing tasks generated by IoT across fog layers. Effectively managing real-time, delay-sensitive, and diverse IoT applications to enhance the Quality-of-Experience (QoE) presents significant challenges due to the dispersed nature and limited resources of fog nodes. Previous studies in fog computing task offloading have typically focused on either energy consumption or service delay. This paper introduces an optimization framework for task offloading within fog computing environments that aims to balance improved user QoE with reduced energy consumption, employing Mixed-Integer Linear Programming (MILP). Given the NP-hard nature of this framework, we have devised a Deep Q-Learning (DQL) based model for task offloading, termed ELTO-DQL, which aims for near-optimal solutions in polynomial time. Experimental results indicate that the ELTO-DQL model enhances energy efficiency and QoE by up to 19% and 15% respectively, outperforming contemporary benchmarks. 2024 Conference Paper http://hdl.handle.net/20.500.11937/97523 10.1109/DCOSS-IoT61029.2024.00104 restricted |
| spellingShingle | Sumona, S.T. Sabbir Hasan, S. Tamzid, A.Y. Roy, P. Razzaque, M.A. Mahmud, Md Redowan A Deep Q-Learning Framework for Enhanced QoE and Energy Optimization in Fog Computing |
| title | A Deep Q-Learning Framework for Enhanced QoE and Energy Optimization in Fog Computing |
| title_full | A Deep Q-Learning Framework for Enhanced QoE and Energy Optimization in Fog Computing |
| title_fullStr | A Deep Q-Learning Framework for Enhanced QoE and Energy Optimization in Fog Computing |
| title_full_unstemmed | A Deep Q-Learning Framework for Enhanced QoE and Energy Optimization in Fog Computing |
| title_short | A Deep Q-Learning Framework for Enhanced QoE and Energy Optimization in Fog Computing |
| title_sort | deep q-learning framework for enhanced qoe and energy optimization in fog computing |
| url | http://hdl.handle.net/20.500.11937/97523 |