Type-2-Soft-Set Based Uncertainty Aware Task Offloading Framework for Fog Computing Using Apprenticeship Learning
Fog computing is one of the emerging forms of cloud computing which aims to satisfy the ever-increasing computation demands of the mobile applications. Effective offloading of tasks leads to increased efficiency of the fog network, but at the same time it suffers from various uncertainty issues with...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2023-03-01
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| Series: | Cybernetics and Information Technologies |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/cait-2023-0002 |
| _version_ | 1797815438562820096 |
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| author | Bhargavi K. Sathish Babu B. Shiva Sajjan G. |
| author_facet | Bhargavi K. Sathish Babu B. Shiva Sajjan G. |
| author_sort | Bhargavi K. |
| collection | DOAJ |
| description | Fog computing is one of the emerging forms of cloud computing which aims to satisfy the ever-increasing computation demands of the mobile applications. Effective offloading of tasks leads to increased efficiency of the fog network, but at the same time it suffers from various uncertainty issues with respect to task demands, fog node capabilities, information asymmetry, missing information, low trust, transaction failures, and so on. Several machine learning techniques have been proposed for the task offloading in fog environments, but they lack efficiency. In this paper, a novel uncertainty proof Type-2-Soft-Set (T2SS) enabled apprenticeship learning based task offloading framework is proposed which formulates the optimal task offloading policies. The performance of the proposed T2SS based apprenticeship learning is compared and found to be better than Q-learning and State-Action-Reward-State-Action (SARSA) learning techniques with respect to performance parameters such as total execution time, throughput, learning rate, and response time. |
| first_indexed | 2024-03-13T08:22:42Z |
| format | Article |
| id | doaj.art-714a8f535b1d4f0a8ed503e14d418cb1 |
| institution | Directory Open Access Journal |
| issn | 1314-4081 |
| language | English |
| last_indexed | 2024-03-13T08:22:42Z |
| publishDate | 2023-03-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Cybernetics and Information Technologies |
| spelling | doaj.art-714a8f535b1d4f0a8ed503e14d418cb12023-05-31T06:59:53ZengSciendoCybernetics and Information Technologies1314-40812023-03-01231385810.2478/cait-2023-0002Type-2-Soft-Set Based Uncertainty Aware Task Offloading Framework for Fog Computing Using Apprenticeship LearningBhargavi K.0Sathish Babu B.1Shiva Sajjan G.21Department of CSE, Siddaganga Institute of Technology, Tumakuru, Karnataka, India2Department of AI and ML, R V College of Engineering, Bengaluru, Karnataka, India3Department of CS, University of Memphis, Memphis, Tennessee, USAFog computing is one of the emerging forms of cloud computing which aims to satisfy the ever-increasing computation demands of the mobile applications. Effective offloading of tasks leads to increased efficiency of the fog network, but at the same time it suffers from various uncertainty issues with respect to task demands, fog node capabilities, information asymmetry, missing information, low trust, transaction failures, and so on. Several machine learning techniques have been proposed for the task offloading in fog environments, but they lack efficiency. In this paper, a novel uncertainty proof Type-2-Soft-Set (T2SS) enabled apprenticeship learning based task offloading framework is proposed which formulates the optimal task offloading policies. The performance of the proposed T2SS based apprenticeship learning is compared and found to be better than Q-learning and State-Action-Reward-State-Action (SARSA) learning techniques with respect to performance parameters such as total execution time, throughput, learning rate, and response time.https://doi.org/10.2478/cait-2023-0002task offloadinguncertaintyapprenticeship learningfog computingq-learningsarsa learning |
| spellingShingle | Bhargavi K. Sathish Babu B. Shiva Sajjan G. Type-2-Soft-Set Based Uncertainty Aware Task Offloading Framework for Fog Computing Using Apprenticeship Learning Cybernetics and Information Technologies task offloading uncertainty apprenticeship learning fog computing q-learning sarsa learning |
| title | Type-2-Soft-Set Based Uncertainty Aware Task Offloading Framework for Fog Computing Using Apprenticeship Learning |
| title_full | Type-2-Soft-Set Based Uncertainty Aware Task Offloading Framework for Fog Computing Using Apprenticeship Learning |
| title_fullStr | Type-2-Soft-Set Based Uncertainty Aware Task Offloading Framework for Fog Computing Using Apprenticeship Learning |
| title_full_unstemmed | Type-2-Soft-Set Based Uncertainty Aware Task Offloading Framework for Fog Computing Using Apprenticeship Learning |
| title_short | Type-2-Soft-Set Based Uncertainty Aware Task Offloading Framework for Fog Computing Using Apprenticeship Learning |
| title_sort | type 2 soft set based uncertainty aware task offloading framework for fog computing using apprenticeship learning |
| topic | task offloading uncertainty apprenticeship learning fog computing q-learning sarsa learning |
| url | https://doi.org/10.2478/cait-2023-0002 |
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