Bi-Objective simplified swarm optimization for fog computing task scheduling
In the face of burgeoning data volumes, latency issues present a formidable challenge to cloud computing. This problem has been strategically tackled through the advent of fog computing, shifting computations from central cloud data centers to local fog devices. This process minimizes data...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Growing Science
2023-01-01
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| Series: | International Journal of Industrial Engineering Computations |
| Online Access: | http://www.growingscience.com/ijiec/Vol14/IJIEC_2023_29.pdf |
| _version_ | 1797683976339456000 |
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| author | Wei-Chang Yeh Zhenyao Liu Kuan-Cheng Tseng |
| author_facet | Wei-Chang Yeh Zhenyao Liu Kuan-Cheng Tseng |
| author_sort | Wei-Chang Yeh |
| collection | DOAJ |
| description | In the face of burgeoning data volumes, latency issues present a formidable challenge to cloud computing. This problem has been strategically tackled through the advent of fog computing, shifting computations from central cloud data centers to local fog devices. This process minimizes data transmission to distant servers, resulting in significant cost savings and instantaneous responses for users. Despite the urgency of many fog computing applications, existing research falls short in providing time-effective and tailored algorithms for fog computing task scheduling. To bridge this gap, we introduce a unique local search mechanism, Card Sorting Local Search (CSLS), that augments the non-dominated solutions found by the Bi-objective Simplified Swarm Optimization (BSSO). We further propose Fast Elite Selecting (FES), a ground-breaking one-front non-dominated sorting method that curtails the time complexity of non-dominated sorting processes. By integrating BSSO, CSLS, and FES, we are unveiling a novel algorithm, Elite Swarm Simplified Optimization (EliteSSO), specifically developed to conquer time-efficiency and non-dominated solution issues, predominantly in large-scale fog computing task scheduling conundrums. Computational evidence reveals that our proposed algorithm is both highly efficient in terms of time and exceedingly effective, outstripping other algorithms on a significant scale. |
| first_indexed | 2024-03-12T00:23:39Z |
| format | Article |
| id | doaj.art-f97d86ecb7214f38a325a9fb91983016 |
| institution | Directory Open Access Journal |
| issn | 1923-2926 1923-2934 |
| language | English |
| last_indexed | 2024-03-12T00:23:39Z |
| publishDate | 2023-01-01 |
| publisher | Growing Science |
| record_format | Article |
| series | International Journal of Industrial Engineering Computations |
| spelling | doaj.art-f97d86ecb7214f38a325a9fb919830162023-09-15T12:02:20ZengGrowing ScienceInternational Journal of Industrial Engineering Computations1923-29261923-29342023-01-0114472374810.5267/j.ijiec.2023.7.004Bi-Objective simplified swarm optimization for fog computing task schedulingWei-Chang YehZhenyao Liu Kuan-Cheng Tseng In the face of burgeoning data volumes, latency issues present a formidable challenge to cloud computing. This problem has been strategically tackled through the advent of fog computing, shifting computations from central cloud data centers to local fog devices. This process minimizes data transmission to distant servers, resulting in significant cost savings and instantaneous responses for users. Despite the urgency of many fog computing applications, existing research falls short in providing time-effective and tailored algorithms for fog computing task scheduling. To bridge this gap, we introduce a unique local search mechanism, Card Sorting Local Search (CSLS), that augments the non-dominated solutions found by the Bi-objective Simplified Swarm Optimization (BSSO). We further propose Fast Elite Selecting (FES), a ground-breaking one-front non-dominated sorting method that curtails the time complexity of non-dominated sorting processes. By integrating BSSO, CSLS, and FES, we are unveiling a novel algorithm, Elite Swarm Simplified Optimization (EliteSSO), specifically developed to conquer time-efficiency and non-dominated solution issues, predominantly in large-scale fog computing task scheduling conundrums. Computational evidence reveals that our proposed algorithm is both highly efficient in terms of time and exceedingly effective, outstripping other algorithms on a significant scale.http://www.growingscience.com/ijiec/Vol14/IJIEC_2023_29.pdf |
| spellingShingle | Wei-Chang Yeh Zhenyao Liu Kuan-Cheng Tseng Bi-Objective simplified swarm optimization for fog computing task scheduling International Journal of Industrial Engineering Computations |
| title | Bi-Objective simplified swarm optimization for fog computing task scheduling |
| title_full | Bi-Objective simplified swarm optimization for fog computing task scheduling |
| title_fullStr | Bi-Objective simplified swarm optimization for fog computing task scheduling |
| title_full_unstemmed | Bi-Objective simplified swarm optimization for fog computing task scheduling |
| title_short | Bi-Objective simplified swarm optimization for fog computing task scheduling |
| title_sort | bi objective simplified swarm optimization for fog computing task scheduling |
| url | http://www.growingscience.com/ijiec/Vol14/IJIEC_2023_29.pdf |
| work_keys_str_mv | AT weichangyeh biobjectivesimplifiedswarmoptimizationforfogcomputingtaskscheduling AT zhenyaoliu biobjectivesimplifiedswarmoptimizationforfogcomputingtaskscheduling AT kuanchengtseng biobjectivesimplifiedswarmoptimizationforfogcomputingtaskscheduling |