A novel approach for Credit-Based Resource Aware Load Balancing algorithm (CB-RALB-SA) for scheduling jobs in cloud computing
In recent years, cloud computing has gained popularity, mainly because of its utility and relevance to current technological trends. It is an arrangement that is highly customizable and encapsulated for providing better computational services to its clients worldwide. In the cloud, computing schedul...
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| Vydané v: | Data & knowledge engineering Ročník 145; s. 102138 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.05.2023
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| ISSN: | 0169-023X |
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| Abstract | In recent years, cloud computing has gained popularity, mainly because of its utility and relevance to current technological trends. It is an arrangement that is highly customizable and encapsulated for providing better computational services to its clients worldwide. In the cloud, computing scheduling plays a pivotal role in optimizing resources. A better scheduling algorithm should be efficient and impartial, reducing the makespan time with proper resource utilization. However, most scheduling algorithms customarily lead to less resource utilization, termed load imbalance. The analysis of the existing papers exhibits better Makespan time but cannot guarantee the load-balanced mapping of jobs with proper resource utilization. Therefore, to eliminate the shortcomings of the prevalent/existing algorithms and enhance the performance, CB-RALB-SA, Credit-based Resource Aware Load Balancing scheduling algorithm has been rendered. The proposed work ensures a balanced distribution of tasks based on the capabilities of the resources, which eventually proves sustainable improvement against the existing scheduling algorithms. Therefore, a novel Credit Based Resource Aware Load Balancing Scheduling algorithm (CB-RALB-SA) is proposed. The tasks weighted by the credit-based scheduling algorithm are then mapped to the resources considering each resource’s load and computing capability using FILL and SPILL functions of Resource Aware and Load using Honey bee optimization heuristic algorithm. With the experimental evaluations and results, it has been proved that the proposed approach provides 48.5% better in Processing Time and 16.90 % better results in makespan time than the Existing CBSA-LB algorithm. Thus, it improves the processor’s efficiency while uplifting the whole system’s performance and has saved memory allocated to tasks and RAM. |
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| AbstractList | In recent years, cloud computing has gained popularity, mainly because of its utility and relevance to current technological trends. It is an arrangement that is highly customizable and encapsulated for providing better computational services to its clients worldwide. In the cloud, computing scheduling plays a pivotal role in optimizing resources. A better scheduling algorithm should be efficient and impartial, reducing the makespan time with proper resource utilization. However, most scheduling algorithms customarily lead to less resource utilization, termed load imbalance. The analysis of the existing papers exhibits better Makespan time but cannot guarantee the load-balanced mapping of jobs with proper resource utilization. Therefore, to eliminate the shortcomings of the prevalent/existing algorithms and enhance the performance, CB-RALB-SA, Credit-based Resource Aware Load Balancing scheduling algorithm has been rendered. The proposed work ensures a balanced distribution of tasks based on the capabilities of the resources, which eventually proves sustainable improvement against the existing scheduling algorithms. Therefore, a novel Credit Based Resource Aware Load Balancing Scheduling algorithm (CB-RALB-SA) is proposed. The tasks weighted by the credit-based scheduling algorithm are then mapped to the resources considering each resource’s load and computing capability using FILL and SPILL functions of Resource Aware and Load using Honey bee optimization heuristic algorithm. With the experimental evaluations and results, it has been proved that the proposed approach provides 48.5% better in Processing Time and 16.90 % better results in makespan time than the Existing CBSA-LB algorithm. Thus, it improves the processor’s efficiency while uplifting the whole system’s performance and has saved memory allocated to tasks and RAM. |
| ArticleNumber | 102138 |
| Author | Narwal, Abhikriti Dhingra, Sunita |
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| Cites_doi | 10.4236/cn.2014.63021 10.26483/ijarcs.v8i8.4752 10.30534/ijatcse/2020/34922020 10.1007/978-3-319-08156-4_5 10.1016/j.fcij.2018.03.004 10.26483/ijarcs.v9i2.5820 |
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| Keywords | Cloud computing Enhanced multiobjective scheduling algorithm Resource aware load balancing Credit based scheduling algorithm Task scheduling Virtual machines Load balancing |
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| References | Sutha, Dr Nawaz (b4) 2016 Srichandan, Kumar, Bibhudatta (b1) 2018 S. Javanmardi, M. Shojafar, D. Amendola, N. Cordeschi, H. Liu, A. Abraham, Hybrid Job Scheduling Algorithm for Cloud Computing Environment, in: Proceedings of the Fifth Intern. Conf. on Innov. in Bio-Inspired Computing. and Applied Science, 2014, pp. 43–52. Vijayalakshmi, Kumar (b2) 2014 Sindhu (b6) 2015 Narwal, Dhingra (b3) 2016; 5 Maqableh, Karajeh (b16) 2014 Narwal, Dhingra (b22) 2018; 9 Raja, Sekar (b14) 2016 Ananth, Chandrasekaran (b12) 2015 Narwal, Dhingra (b20) 2018 Narwal, Dhingra (b23) 2020; 9 Narwal, Dhingra (b10) 2017 Loganathan, Mukherjee (b19) 2015 Kaur, Kaur, Singh (b9) 2017 Bitam (b17) 2012; 18 Hussain, Aleem, Khan, Iqbal, Islam (b7) 2018 Sharma, Tyagi (b5) 2016 Kimpan, Kruekaew (b18) 2016 Lakra, Yadav (b11) 2015 Srichandan, Kumar, Bibhudatta (b15) 2018 Narwal, Dhingra (b21) 2019; 11 Thomas, Krishnalal, Raj (b13) 2015 Bitam (10.1016/j.datak.2022.102138_b17) 2012; 18 Hussain (10.1016/j.datak.2022.102138_b7) 2018 Ananth (10.1016/j.datak.2022.102138_b12) 2015 Maqableh (10.1016/j.datak.2022.102138_b16) 2014 10.1016/j.datak.2022.102138_b8 Narwal (10.1016/j.datak.2022.102138_b22) 2018; 9 Sharma (10.1016/j.datak.2022.102138_b5) 2016 Vijayalakshmi (10.1016/j.datak.2022.102138_b2) 2014 Kaur (10.1016/j.datak.2022.102138_b9) 2017 Narwal (10.1016/j.datak.2022.102138_b3) 2016; 5 Sindhu (10.1016/j.datak.2022.102138_b6) 2015 Sutha (10.1016/j.datak.2022.102138_b4) 2016 Narwal (10.1016/j.datak.2022.102138_b23) 2020; 9 Thomas (10.1016/j.datak.2022.102138_b13) 2015 Srichandan (10.1016/j.datak.2022.102138_b15) 2018 Srichandan (10.1016/j.datak.2022.102138_b1) 2018 Lakra (10.1016/j.datak.2022.102138_b11) 2015 Narwal (10.1016/j.datak.2022.102138_b21) 2019; 11 Narwal (10.1016/j.datak.2022.102138_b10) 2017 Kimpan (10.1016/j.datak.2022.102138_b18) 2016 Raja (10.1016/j.datak.2022.102138_b14) 2016 Loganathan (10.1016/j.datak.2022.102138_b19) 2015 Narwal (10.1016/j.datak.2022.102138_b20) 2018 |
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| SubjectTerms | Cloud computing Credit based scheduling algorithm Enhanced multiobjective scheduling algorithm Load balancing Resource aware load balancing Task scheduling Virtual machines |
| Title | A novel approach for Credit-Based Resource Aware Load Balancing algorithm (CB-RALB-SA) for scheduling jobs in cloud computing |
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