Workload prioritization and optimal task scheduling in cloud: introduction to hybrid optimization algorithm
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| Publicat a: | Wireless Networks vol. 31, no. 1 (Jan 2025), p. 945 |
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| Publicat: |
Springer Nature B.V.
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| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text - PDF |
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| 022 | |a 1022-0038 | ||
| 022 | |a 1572-8196 | ||
| 024 | 7 | |a 10.1007/s11276-024-03793-3 |2 doi | |
| 035 | |a 3163362273 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
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| 245 | 1 | |a Workload prioritization and optimal task scheduling in cloud: introduction to hybrid optimization algorithm | |
| 260 | |b Springer Nature B.V. |c Jan 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Cloud computing represents an evolved form of cluster, client server, and grid computing, enabling users to seamlessly access resources over the internet. The quality and reliability of the cloud computing services are depends on the specific tasks undertaken by the users. Task Scheduling emerges as a pivotal factor in enhancing the efficiency and reliability of a cloud environment, aiming to optimize resource utilization. Furthermore, efficient task scheduling holds a prime importance in achieving superior performance, minimizing response time, reducing energy consumption and maximizing throughput. Assigning work to essential resources is a challenging process to achieve better performance. However, this paper plans to propose a novel workload prioritization and optimal task scheduling in the cloud with two steps. At first, the ranks are allotted to the tasks with Analytical Hierarchy Process based ranking process that uses a k-means clustering strategy to group the workloads. Then, the tasks are scheduled under the consideration of constraints like makespan, utilization cost, and migration cost and risk probability; based on priority. Accordingly, the task scheduling is done optimally by the proposed hybrid optimization Blue Updated Jellyfish Search Optimization that combines algorithms like Blue Monkey Optimization and Jelly fish Search Optimization algorithms. The performance of the proposed scheduling process is validated and proved over the conventional methods. | |
| 653 | |a Scheduling | ||
| 653 | |a Task scheduling | ||
| 653 | |a Analytic hierarchy process | ||
| 653 | |a Cluster analysis | ||
| 653 | |a Clustering | ||
| 653 | |a Reliability | ||
| 653 | |a Cloud computing | ||
| 653 | |a Computational grids | ||
| 653 | |a Optimization | ||
| 653 | |a Resource scheduling | ||
| 653 | |a Workload | ||
| 653 | |a Algorithms | ||
| 653 | |a Resource utilization | ||
| 653 | |a Energy consumption | ||
| 653 | |a Workloads | ||
| 653 | |a Response time (computers) | ||
| 653 | |a Vector quantization | ||
| 653 | |a Client servers | ||
| 653 | |a Priority scheduling | ||
| 773 | 0 | |t Wireless Networks |g vol. 31, no. 1 (Jan 2025), p. 945 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3163362273/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3163362273/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |