Hybrid DRL-Enhanced ACO-WWO for Efficient Resource Allocation and Load-Balancing in Cloud Computing
Abstract The growing complexity of cloud computing necessitates astute workload allocation and adaptive resource management to enhance performance while minimizing expenses and energy consumption. Conventional optimization methods, including Improved Ant Colony Optimization (IACO) and Water Wave Opt...
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| Format: | Article |
| Language: | English |
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Springer
2025-06-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://doi.org/10.1007/s44196-025-00882-9 |
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| author | Umesh Kumar Lilhore Sarita Simaiya K. B. V. Brahma Rao V. V. R. Maheswara Rao Yogesh Kumar Sharma Roobaea Alroobaea Hamed Alsufyani Majed Alsafyani M. D. Monish Khan |
| author_facet | Umesh Kumar Lilhore Sarita Simaiya K. B. V. Brahma Rao V. V. R. Maheswara Rao Yogesh Kumar Sharma Roobaea Alroobaea Hamed Alsufyani Majed Alsafyani M. D. Monish Khan |
| author_sort | Umesh Kumar Lilhore |
| collection | DOAJ |
| description | Abstract The growing complexity of cloud computing necessitates astute workload allocation and adaptive resource management to enhance performance while minimizing expenses and energy consumption. Conventional optimization methods, including Improved Ant Colony Optimization (IACO) and Water Wave Optimization (IWWO), face challenges in real-time adaptability, exhibit slow convergence, and are inadequate for managing rapidly varying workloads. Although IACO enhances local search efficiency and IWWO specializes in global exploration, neither adequately resolves the complexities of dynamic cloud environments. To address this gap, we propose a Hybrid DRL-IACO-IWWO model, a novel hybrid model that combines DRL with advanced iterations of IACO and IWWO. The model presents an adaptive dual-phase optimization strategy, wherein IACO conducts initial task scheduling, and IWWO enhances global optimization, informed by real-time DRL feedback. Furthermore, DRL dynamically adjusts its heuristic parameters to improve operational cost and energy efficiency, ensuring real-time adaptability. To expedite convergence, our model utilizes a wavelet transformation-based perturbation in WWO, thereby preventing premature convergence and promoting a more balanced equilibrium between exploration and exploitation. An energy-efficient scheduling mechanism is integrated to reduce energy consumption and improve cloud sustainability. The proposed model was evaluated using the workflow dataset, considering constraints, such as task deadlines, resource availability, and cost efficiency. The results indicate that our methodology outperforms leading hybrid techniques, such as ACO-GA, ACO-SMO, and WWO-GA. The proposed model achieved a scheduling duration of 1.25 s, compared to 1.75 s for ACO-GA and 1.68 s for WWO-GA, while reducing operational expenses to $23.80, lowering energy consumption to 15.6 kWh, and achieving a resource utilization score of 0.92. These findings underscore the transformative capacity of our Enhanced ACO-WWO with DRL, offering a highly efficient, cost-effective, and adaptive solution for next-generation cloud resource management. |
| format | Article |
| id | doaj-art-e70fec3ffd854294abdbcb6d87ea22d6 |
| institution | DOAJ |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
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| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-e70fec3ffd854294abdbcb6d87ea22d62025-08-20T02:39:44ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-06-0118114010.1007/s44196-025-00882-9Hybrid DRL-Enhanced ACO-WWO for Efficient Resource Allocation and Load-Balancing in Cloud ComputingUmesh Kumar Lilhore0Sarita Simaiya1K. B. V. Brahma Rao2V. V. R. Maheswara Rao3Yogesh Kumar Sharma4Roobaea Alroobaea5Hamed Alsufyani6Majed Alsafyani7M. D. Monish Khan8School of Computing Science and Engineering, Galgotias UniversitySchool of Computing Science and Engineering, Galgotias UniversityDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education FoundationDepartment of Computer Science and Engineering, Shri Vishnu Engineering College for Women (A)Department of Computer Science and Engineering, Koneru Lakshmaiah Education FoundationDepartment of Computer Science, College of Computers and Information Technology, Taif UniversityDepartment of Computer Science, College of Computing and Informatics, Saudi Electronic UniversityDepartment of Computer Science, College of Computers and Information Technology, Taif UniversityArba Minch UniversityAbstract The growing complexity of cloud computing necessitates astute workload allocation and adaptive resource management to enhance performance while minimizing expenses and energy consumption. Conventional optimization methods, including Improved Ant Colony Optimization (IACO) and Water Wave Optimization (IWWO), face challenges in real-time adaptability, exhibit slow convergence, and are inadequate for managing rapidly varying workloads. Although IACO enhances local search efficiency and IWWO specializes in global exploration, neither adequately resolves the complexities of dynamic cloud environments. To address this gap, we propose a Hybrid DRL-IACO-IWWO model, a novel hybrid model that combines DRL with advanced iterations of IACO and IWWO. The model presents an adaptive dual-phase optimization strategy, wherein IACO conducts initial task scheduling, and IWWO enhances global optimization, informed by real-time DRL feedback. Furthermore, DRL dynamically adjusts its heuristic parameters to improve operational cost and energy efficiency, ensuring real-time adaptability. To expedite convergence, our model utilizes a wavelet transformation-based perturbation in WWO, thereby preventing premature convergence and promoting a more balanced equilibrium between exploration and exploitation. An energy-efficient scheduling mechanism is integrated to reduce energy consumption and improve cloud sustainability. The proposed model was evaluated using the workflow dataset, considering constraints, such as task deadlines, resource availability, and cost efficiency. The results indicate that our methodology outperforms leading hybrid techniques, such as ACO-GA, ACO-SMO, and WWO-GA. The proposed model achieved a scheduling duration of 1.25 s, compared to 1.75 s for ACO-GA and 1.68 s for WWO-GA, while reducing operational expenses to $23.80, lowering energy consumption to 15.6 kWh, and achieving a resource utilization score of 0.92. These findings underscore the transformative capacity of our Enhanced ACO-WWO with DRL, offering a highly efficient, cost-effective, and adaptive solution for next-generation cloud resource management.https://doi.org/10.1007/s44196-025-00882-9Improved ACO-WWODeep reinforcement learningCloud resource optimizationAdaptive schedulingEnergy-efficient computingHybrid metaheuristics |
| spellingShingle | Umesh Kumar Lilhore Sarita Simaiya K. B. V. Brahma Rao V. V. R. Maheswara Rao Yogesh Kumar Sharma Roobaea Alroobaea Hamed Alsufyani Majed Alsafyani M. D. Monish Khan Hybrid DRL-Enhanced ACO-WWO for Efficient Resource Allocation and Load-Balancing in Cloud Computing International Journal of Computational Intelligence Systems Improved ACO-WWO Deep reinforcement learning Cloud resource optimization Adaptive scheduling Energy-efficient computing Hybrid metaheuristics |
| title | Hybrid DRL-Enhanced ACO-WWO for Efficient Resource Allocation and Load-Balancing in Cloud Computing |
| title_full | Hybrid DRL-Enhanced ACO-WWO for Efficient Resource Allocation and Load-Balancing in Cloud Computing |
| title_fullStr | Hybrid DRL-Enhanced ACO-WWO for Efficient Resource Allocation and Load-Balancing in Cloud Computing |
| title_full_unstemmed | Hybrid DRL-Enhanced ACO-WWO for Efficient Resource Allocation and Load-Balancing in Cloud Computing |
| title_short | Hybrid DRL-Enhanced ACO-WWO for Efficient Resource Allocation and Load-Balancing in Cloud Computing |
| title_sort | hybrid drl enhanced aco wwo for efficient resource allocation and load balancing in cloud computing |
| topic | Improved ACO-WWO Deep reinforcement learning Cloud resource optimization Adaptive scheduling Energy-efficient computing Hybrid metaheuristics |
| url | https://doi.org/10.1007/s44196-025-00882-9 |
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