Dynamic scheduling strategies for cloud-based load balancing in parallel and distributed systems
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| Veröffentlicht in: | Journal of Cloud Computing vol. 14, no. 1 (Dec 2025), p. 33 |
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Springer Nature B.V.
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| 022 | |a 2192-113X | ||
| 024 | 7 | |a 10.1186/s13677-025-00757-6 |2 doi | |
| 035 | |a 3226011203 | ||
| 045 | 2 | |b d20251201 |b d20251231 | |
| 084 | |a 243929 |2 nlm | ||
| 100 | 1 | |a Albalawi, Nasser S. |u Northern Border University, Department of Computer Sciences, Rafha, Saudi Arabia (GRID:grid.449533.c) (ISNI:0000 0004 1757 2152) | |
| 245 | 1 | |a Dynamic scheduling strategies for cloud-based load balancing in parallel and distributed systems | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Actual load balancing in parallel and distributed systems ruins a serious task due to workloads’ dynamic nature and resource availability. Existing scheduling procedures continually fail to regulate real-time alterations, leading to suboptimal performance and resource underutilization. Our study validates dynamic and effective load distribution by combining novel systems and optimization techniques to handle these issues. We utilize a comprehensive dynamic scheduling approach in this work to provide efficient load balancing in distributed and parallel systems. In this example, we start by using Round-Robin Allocation with Sunflower Whale Optimization (RRA-SWO) to perform an allocation procedure. The allocation step is followed by the Hybrid Ant Genetic Algorithm (HAGA), which is used to schedule tasks in parallel. The Least Response Time (LRT) technique for the Load Monitoring procedures will be developed once the job scheduling is complete. The Harmony Search Algorithm with Linear Regression (LR-HSA) is then used to do Distributed Computing-based Load Prediction and Adjustment. Alongside ongoing observation, this is carried out. Finally, we use the Least Recently Used (LRU) technique to do dynamic load balancing. Performance evaluations are using CloudSim and NetBeans 12.3, metrics like Packet Delivery Ratio at 98 (%), Average Response Time at 65 (s), Task Success Rate at 95 (%), Memory Utilization Rate at 80 (%), and Throughput at 97 (%) are all analyzed to validate our strategy. | |
| 653 | |a Scheduling | ||
| 653 | |a Software | ||
| 653 | |a Task scheduling | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Dynamic loads | ||
| 653 | |a Response time | ||
| 653 | |a Regression analysis | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Optimization | ||
| 653 | |a Resource scheduling | ||
| 653 | |a Search algorithms | ||
| 653 | |a Real time | ||
| 653 | |a Cloud computing | ||
| 653 | |a Workloads | ||
| 653 | |a Energy consumption | ||
| 653 | |a Distributed processing | ||
| 653 | |a Load balancing | ||
| 653 | |a Computer networks | ||
| 653 | |a Resource management | ||
| 653 | |a Efficiency | ||
| 773 | 0 | |t Journal of Cloud Computing |g vol. 14, no. 1 (Dec 2025), p. 33 | |
| 786 | 0 | |d ProQuest |t Research Library | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3226011203/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3226011203/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3226011203/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |