Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing
<p dir="ltr">Advancements in the Industrial Internet of Things (IIoT) have yielded massive volumes of data, taxing the capabilities of cloud computing infrastructure. Allocating limited computing resources to numerous incoming requests is crucial for cloud computing and referred to a...
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2024
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| _version_ | 1864513527057743872 |
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| author | Ahmed G. Gad (11381409) |
| author2 | Essam H. Houssein (17984092) MengChu Zhou (17984095) Ponnuthurai Nagaratnam Suganthan (11274636) Yaser M. Wazery (17984098) |
| author2_role | author author author author |
| author_facet | Ahmed G. Gad (11381409) Essam H. Houssein (17984092) MengChu Zhou (17984095) Ponnuthurai Nagaratnam Suganthan (11274636) Yaser M. Wazery (17984098) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ahmed G. Gad (11381409) Essam H. Houssein (17984092) MengChu Zhou (17984095) Ponnuthurai Nagaratnam Suganthan (11274636) Yaser M. Wazery (17984098) |
| dc.date.none.fl_str_mv | 2024-07-03T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/jiot.2023.3291367 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Damping-Assisted_Evolutionary_Swarm_Intelligence_for_Industrial_IoT_Task_Scheduling_in_Cloud_Computing/25239772 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Communications engineering Information and computing sciences Information systems Cloud computing Industrial Internet of Things Task analysis Optimization Job shop scheduling Particle swarm optimization Processor scheduling Cloud task scheduling evolutionary computation power consumption simulated annealing (SA) swarm intelligence (SI) system throughput |
| dc.title.none.fl_str_mv | Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Advancements in the Industrial Internet of Things (IIoT) have yielded massive volumes of data, taxing the capabilities of cloud computing infrastructure. Allocating limited computing resources to numerous incoming requests is crucial for cloud computing and referred to as a task-scheduling-in-cloud-computing (TSCC) problem. In order to ameliorate the performance of a particle swarm optimizer (PSO) and broaden its application to TSCC, this article introduces an opposition-based simulated annealing particle swarm optimizer (OSAPSO) to address PSO’s premature convergence issue, particularly when tackling high-dimensional complex problems like TSCC. OSAPSO is a novel combination of opposition-based learning (OBL), evolution strategy, simulated annealing (SA), and swarm intelligence. At its initial stage, a swarm is formed at random by using OBL to guarantee its diversity with a light computational burden. A multiway tournament selection approach is then utilized to pick parents to produce a new offspring swarm by using two novel evolutionary operators, namely, damping-based mutation and inversion–scrambling-based crossover. OSAPSO is given a powerful exploration capacity by adopting the survivor probabilistic selection of SA, which accepts subpar solutions with a certain probability. Finally, PSO itself kicks in, making a good tradeoff between solution diversity and convergence speed of OSAPSO. Due to the nonconvex discontinuous nature of TSCC, OSAPSO is modified to clone it into a discrete optimization problem. Within a heterogeneous cloud computing environment, OSAPSO and eight well-regarded competitors are examined on a set of multiscale IIoT heterogeneous task groups. In terms of power consumption, monetary cost, service makespan, and system throughput, experimental results reveal that OSAPSO beats its peers in IIoT task scheduling of cloud systems.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Internet of Things Journal<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/jiot.2023.3291367" target="_blank">https://dx.doi.org/10.1109/jiot.2023.3291367</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_808f76db90c1fbc9ba61a087125e2884 |
| identifier_str_mv | 10.1109/jiot.2023.3291367 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25239772 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud ComputingAhmed G. Gad (11381409)Essam H. Houssein (17984092)MengChu Zhou (17984095)Ponnuthurai Nagaratnam Suganthan (11274636)Yaser M. Wazery (17984098)EngineeringCommunications engineeringInformation and computing sciencesInformation systemsCloud computingIndustrial Internet of ThingsTask analysisOptimizationJob shop schedulingParticle swarm optimizationProcessor schedulingCloud task schedulingevolutionary computationpower consumptionsimulated annealing (SA)swarm intelligence (SI)system throughput<p dir="ltr">Advancements in the Industrial Internet of Things (IIoT) have yielded massive volumes of data, taxing the capabilities of cloud computing infrastructure. Allocating limited computing resources to numerous incoming requests is crucial for cloud computing and referred to as a task-scheduling-in-cloud-computing (TSCC) problem. In order to ameliorate the performance of a particle swarm optimizer (PSO) and broaden its application to TSCC, this article introduces an opposition-based simulated annealing particle swarm optimizer (OSAPSO) to address PSO’s premature convergence issue, particularly when tackling high-dimensional complex problems like TSCC. OSAPSO is a novel combination of opposition-based learning (OBL), evolution strategy, simulated annealing (SA), and swarm intelligence. At its initial stage, a swarm is formed at random by using OBL to guarantee its diversity with a light computational burden. A multiway tournament selection approach is then utilized to pick parents to produce a new offspring swarm by using two novel evolutionary operators, namely, damping-based mutation and inversion–scrambling-based crossover. OSAPSO is given a powerful exploration capacity by adopting the survivor probabilistic selection of SA, which accepts subpar solutions with a certain probability. Finally, PSO itself kicks in, making a good tradeoff between solution diversity and convergence speed of OSAPSO. Due to the nonconvex discontinuous nature of TSCC, OSAPSO is modified to clone it into a discrete optimization problem. Within a heterogeneous cloud computing environment, OSAPSO and eight well-regarded competitors are examined on a set of multiscale IIoT heterogeneous task groups. In terms of power consumption, monetary cost, service makespan, and system throughput, experimental results reveal that OSAPSO beats its peers in IIoT task scheduling of cloud systems.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Internet of Things Journal<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/jiot.2023.3291367" target="_blank">https://dx.doi.org/10.1109/jiot.2023.3291367</a></p>2024-07-03T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/jiot.2023.3291367https://figshare.com/articles/journal_contribution/Damping-Assisted_Evolutionary_Swarm_Intelligence_for_Industrial_IoT_Task_Scheduling_in_Cloud_Computing/25239772CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252397722024-07-03T09:00:00Z |
| spellingShingle | Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing Ahmed G. Gad (11381409) Engineering Communications engineering Information and computing sciences Information systems Cloud computing Industrial Internet of Things Task analysis Optimization Job shop scheduling Particle swarm optimization Processor scheduling Cloud task scheduling evolutionary computation power consumption simulated annealing (SA) swarm intelligence (SI) system throughput |
| status_str | publishedVersion |
| title | Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing |
| title_full | Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing |
| title_fullStr | Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing |
| title_full_unstemmed | Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing |
| title_short | Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing |
| title_sort | Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing |
| topic | Engineering Communications engineering Information and computing sciences Information systems Cloud computing Industrial Internet of Things Task analysis Optimization Job shop scheduling Particle swarm optimization Processor scheduling Cloud task scheduling evolutionary computation power consumption simulated annealing (SA) swarm intelligence (SI) system throughput |