EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework
Cloud-fog computing is a wide range of service environments created to provide quick, flexible services to customers, and the phenomenal growth of the Internet of Things (IoT) has produced an immense amount of data on a daily basis. To complete tasks and meet service-level agreement (SLA) commitment...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/5/2445 |
| _version_ | 1827752138897358848 |
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| author | M. Santhosh Kumar Ganesh Reddy Karri |
| author_facet | M. Santhosh Kumar Ganesh Reddy Karri |
| author_sort | M. Santhosh Kumar |
| collection | DOAJ |
| description | Cloud-fog computing is a wide range of service environments created to provide quick, flexible services to customers, and the phenomenal growth of the Internet of Things (IoT) has produced an immense amount of data on a daily basis. To complete tasks and meet service-level agreement (SLA) commitments, the provider assigns appropriate resources and employs scheduling techniques to efficiently manage the execution of received IoT tasks in fog or cloud systems. The effectiveness of cloud services is directly impacted by some other important criteria, such as energy usage and cost, which are not taken into account by many of the existing methodologies. To resolve the aforementioned problems, an effective scheduling algorithm is required to schedule the heterogeneous workload and enhance the quality of service (QoS). Therefore, a nature-inspired multi-objective task scheduling algorithm called the electric earthworm optimization algorithm (EEOA) is proposed in this paper for IoT requests in a cloud-fog framework. This method was created using the combination of the earthworm optimization algorithm (EOA) and the electric fish optimization algorithm (EFO) to improve EFO’s potential to be exploited while looking for the best solution to the problem at hand. Concerning execution time, cost, makespan, and energy consumption, the suggested scheduling technique’s performance was assessed using significant instances of real-world workloads such as CEA-CURIE and HPC2N. Based on simulation results, our proposed approach improves efficiency by 89%, energy consumption by 94%, and total cost by 87% over existing algorithms for the scenarios considered using different benchmarks. Detailed simulations demonstrate that the suggested approach provides a superior scheduling scheme with better results than the existing scheduling techniques. |
| first_indexed | 2024-03-11T07:11:31Z |
| format | Article |
| id | doaj.art-82e58c9104b84eca862e920668238e13 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T07:11:31Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-82e58c9104b84eca862e920668238e132023-11-17T08:34:52ZengMDPI AGSensors1424-82202023-02-01235244510.3390/s23052445EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog FrameworkM. Santhosh Kumar0Ganesh Reddy Karri1School of Computer Science and Engineering, VIT-AP University, Amaravathi 522237, Andhra Pradesh, IndiaSchool of Computer Science and Engineering, VIT-AP University, Amaravathi 522237, Andhra Pradesh, IndiaCloud-fog computing is a wide range of service environments created to provide quick, flexible services to customers, and the phenomenal growth of the Internet of Things (IoT) has produced an immense amount of data on a daily basis. To complete tasks and meet service-level agreement (SLA) commitments, the provider assigns appropriate resources and employs scheduling techniques to efficiently manage the execution of received IoT tasks in fog or cloud systems. The effectiveness of cloud services is directly impacted by some other important criteria, such as energy usage and cost, which are not taken into account by many of the existing methodologies. To resolve the aforementioned problems, an effective scheduling algorithm is required to schedule the heterogeneous workload and enhance the quality of service (QoS). Therefore, a nature-inspired multi-objective task scheduling algorithm called the electric earthworm optimization algorithm (EEOA) is proposed in this paper for IoT requests in a cloud-fog framework. This method was created using the combination of the earthworm optimization algorithm (EOA) and the electric fish optimization algorithm (EFO) to improve EFO’s potential to be exploited while looking for the best solution to the problem at hand. Concerning execution time, cost, makespan, and energy consumption, the suggested scheduling technique’s performance was assessed using significant instances of real-world workloads such as CEA-CURIE and HPC2N. Based on simulation results, our proposed approach improves efficiency by 89%, energy consumption by 94%, and total cost by 87% over existing algorithms for the scenarios considered using different benchmarks. Detailed simulations demonstrate that the suggested approach provides a superior scheduling scheme with better results than the existing scheduling techniques.https://www.mdpi.com/1424-8220/23/5/2445electric fish optimizationearthworm optimization algorithminternet of thingsHPC2NCEA-CURIE |
| spellingShingle | M. Santhosh Kumar Ganesh Reddy Karri EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework Sensors electric fish optimization earthworm optimization algorithm internet of things HPC2N CEA-CURIE |
| title | EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework |
| title_full | EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework |
| title_fullStr | EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework |
| title_full_unstemmed | EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework |
| title_short | EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework |
| title_sort | eeoa cost and energy efficient task scheduling in a cloud fog framework |
| topic | electric fish optimization earthworm optimization algorithm internet of things HPC2N CEA-CURIE |
| url | https://www.mdpi.com/1424-8220/23/5/2445 |
| work_keys_str_mv | AT msanthoshkumar eeoacostandenergyefficienttaskschedulinginacloudfogframework AT ganeshreddykarri eeoacostandenergyefficienttaskschedulinginacloudfogframework |