Enhanced Task Scheduling Algorithm Using Harris Hawks Optimization Algorithm for Cloud Computing
-д хадгалсан:
| -д хэвлэсэн: | International Journal of Advanced Computer Science and Applications vol. 16, no. 1 (2025) |
|---|---|
| Үндсэн зохиолч: | |
| Хэвлэсэн: |
Science and Information (SAI) Organization Limited
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| Нөхцлүүд: | |
| Онлайн хандалт: | Citation/Abstract Full Text - PDF |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3168740308 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2158-107X | ||
| 022 | |a 2156-5570 | ||
| 024 | 7 | |a 10.14569/IJACSA.2025.0160189 |2 doi | |
| 035 | |a 3168740308 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a PDF | |
| 245 | 1 | |a Enhanced Task Scheduling Algorithm Using Harris Hawks Optimization Algorithm for Cloud Computing | |
| 260 | |b Science and Information (SAI) Organization Limited |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Amongst the most transformational technologies nowadays, cloud computing can provide resources such as CPU, memory, and storage over secure internet connections. Due to its flexibility and resource availability with guaranteed QoS, cloud computing allows comprehensive business and research adoptions. Despite the rapid development, resource management remains one of the significant challenges, especially handling task scheduling efficiently in this environment. Task scheduling strategically assigns tasks to available resources so that Quality of Service (QoS) metrics are effectively related to response time and throughput. This paper proposes an Enhanced Harris Hawks Optimization (EHHO) algorithm for scheduling cloud tasks to mitigate the common limitations found in existing algorithms. EHHO integrates a dynamic random walk strategy, enhancing exploration capabilities to avoid premature convergence and significantly improving scalability and resource allocation efficiency. Simulation outcomes reveal that EHHO minimizes makespan by up to 75%, memory usage by up to 60%, execution time by up to 39%, and cost by up to 66% compared to state-of-the-art algorithms. These benefits demonstrate that EHHO can optimize resource allocation while being highly scalable and reliable. Consistent performance over various stacks such as Kafka, Spark, Flink, and Storm further evidences the superiority of EHHO in handling complex scheduling challenges in dynamic cloud computing environments. | |
| 653 | |a Task scheduling | ||
| 653 | |a Computer memory | ||
| 653 | |a Cloud computing | ||
| 653 | |a Random walk | ||
| 653 | |a Optimization | ||
| 653 | |a Resource allocation | ||
| 653 | |a Resource scheduling | ||
| 653 | |a Algorithms | ||
| 653 | |a Availability | ||
| 653 | |a Resource management | ||
| 653 | |a Response time (computers) | ||
| 653 | |a Scheduling | ||
| 653 | |a Simulation | ||
| 653 | |a Response time | ||
| 653 | |a Computer science | ||
| 653 | |a Communication | ||
| 653 | |a Software services | ||
| 653 | |a Quality of service | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Energy consumption | ||
| 653 | |a Internet of Things | ||
| 653 | |a Efficiency | ||
| 653 | |a Business metrics | ||
| 773 | 0 | |t International Journal of Advanced Computer Science and Applications |g vol. 16, no. 1 (2025) | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3168740308/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3168740308/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |