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Modified Fire Hawks Gazelle Optimization (MFHGO) Algorithm Based Optimized Approach to Improve the QoS Provisioning in Cloud Computing Environment


Affiliations
1 Department of Computer Science and Engineering, IFTM University, Moradabad, Uttar Pradesh., India
2 Department of Computer Science and Engineering, Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand., India
 

This work introduces a method that focuses on enhancing resource allocation in cloud computing environments by considering Quality of Service (QoS) factors. Since resource allocation plays a crucial role in determining the QoS of cloud services, it is important to consider indicators like response time, throughput, waiting time, and makespan. The primary difficulty in cloud computing lies in resource allocation, which can be tackled by proposing a novel algorithm known as Modified Fire Hawks Gazelle Optimization (MFHGO). The proposed approach involves the hybridization of the modified fire hawks algorithm with gazelle optimization to facilitate efficient resource allocation. It aims to optimize several objectives, such as resource utilization, degree of imbalance, completion time, throughput, relative error, and response time. To achieve this, an optimal resource allocation is achieved using the Partitioning around K-medoids (PAKM) clustering approach. The proposed model extends the K-means clustering method. For simulation purposes, the GWA-T-12 Bitbrains dataset is utilized, while the JAVA tool is employed for exploratory analysis. The effectiveness of the proposed resource allocation and clustering approach is demonstrated by comparing it with existing schemes. The proposed work's makespan is 1.45 seconds for 50 tasks, 3.6 seconds for 100 tasks, 3.67 seconds for 150 tasks, and 5.34 seconds for 200 jobs. As a result, the proposed model achieves the smallest makespan value when compared to the previous approaches. The proposed work yielded response times of 105ms for a task length of 100, 376ms for 200, 555ms for 300, 624ms for 400, and 1014ms for 500. These results indicate that the proposed model outperforms current approaches by achieving a faster response time and also attains a bandwidth utilization of 0.80%, 0.90%, and 0.97% for 4, 6, and 16 tasks, respectively, indicating better bandwidth utilization than the other approaches.

Keywords

Cloud Computing, Resource Allocation, Throughput, Response Time, Bandwidth Utilization, Time Consumption, Resource Utilization.
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  • Pradhan, Pandaba, Prafulla Ku. Behera, and B.N.B. Ray. “Modified Round Robin Algorithm for Resource Allocation in Cloud Computing.” Procedia Computer Science 85 (2016): 878–90. https://doi.org/10.1016/j.procs.2016.05.278.

  • Kinger, Kushagra, Ajeet Singh, and Sanjaya Kumar Panda. “Priority-Aware Resource Allocation Algorithm for Cloud Computing.” Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing, 2022. https://doi.org/10.1145/3549206.3549236.

  • Ashawa, Moses, Oyakhire Douglas, Jude Osamor, and Riley Jackie. “Improving Cloud Efficiency through Optimized Resource Allocation Technique for Load Balancing Using LSTM Machine Learning Algorithm.” Journal of Cloud Computing 11, no. 1 (2022). https://doi.org/10.1186/s13677-022-00362-x.

  • Akintoye, Samson Busuyi, and Antoine Bagula. "Improving quality-of-service in cloud/fog computing through efficient resource allocation." Sensors 19, no. 6 (2019): 1267.

  • Vaibhav Sharma, Gola, K.K. (2016). ASCCS: Architecture for Secure Communication Using Cloud Services. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-10-0451-3_3

  • Devarasetty, Prasad, and Satyananda Reddy. "Genetic algorithm for quality of service based resource allocation in cloud computing." Evolutionary Intelligence 14, no. 2 (2021): 381-387.

  • Shrimali, B., & Patel, H. (2020). Multi-objective optimization oriented policy for performance and energy efficient resource allocation in Cloud environment. Journal of King Saud University-Computer and Information Sciences, 32(7), 860-869.

  • Wei, G., Vasilakos, A.V., Zheng, Y. et al. A game-theoretic method of fair resource allocation for cloud computing services. J Supercomput 54, 252–269 (2010). https://doi.org/10.1007/s11227-009-0318-1

  • Zhao, Junhui, Qiuping Li, Yi Gong, and Ke Zhang. "Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks." IEEE Transactions on Vehicular Technology 68, no. 8 (2019): 7944-7956.

  • C. S. Pawar and R. B. Wagh, "Priority Based Dynamic Resource Allocation in Cloud Computing," 2012 International Symposium on Cloud and Services Computing, Mangalore, India, 2012, pp. 1-6, doi: 10.1109/ISCOS.2012.14.

  • Belgacem, Ali, Kadda Beghdad-Bey, Hassina Nacer, and Sofiane Bouznad. "Efficient dynamic resource allocation method for cloud computing environment." Cluster Computing 23, no. 4 (2020): 2871-2889.

  • Muthulakshmi, B., and Krishnan Somasundaram. "A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment." Cluster Computing 22, no. 5 (2019): 10769-10777.

  • Ramasamy, Vadivel, and SudalaiMuthu Thalavai Pillai. "An effective HPSO-MGA optimization algorithm for dynamic resource allocation in cloud environment." Cluster Computing 23, no. 3 (2020): 1711-1724.

  • Gao, Xiangqiang, Rongke Liu, and Aryan Kaushik. "Hierarchical multi-agent optimization for resource allocation in cloud computing." IEEE Transactions on Parallel and Distributed Systems 32, no. 3 (2020): 692-707.

  • Samriya, J. K. ., & Kumar, N. (2022). Spider Monkey Optimization based Energy-Efficient Resource Allocation in Cloud Environment. Trends in Sciences, 19(1), 1710. https://doi.org/10.48048/tis.2022.1710

  • A. Thakur and M. S. Goraya, “RAFL: A hybrid metaheuristic based resource allocation framework for load balancing in cloud computing environment,” Simulation Modelling Practice and Theory, vol. 116, p. 102485, 2022.

  • Raed Abdulkareem HASAN * , Muamer N. MOHAMMED, A Krill Herd Behaviour Inspired Load Balancing of Tasks in Cloud Computing, Studies in Informatics and Control, ISSN 1220-1766, vol. 26(4), pp. 413-424, 2017.

  • Ramasamy, V., Thalavai Pillai, S. An effective HPSO-MGA optimization algorithm for dynamic resource allocation in cloud environment. Cluster Comput 23, 1711–1724 (2020). https://doi.org/10.1007/s10586-020-03118-x.

  • K. K. Gola, B. M. Singh, B. Gupta, N. Chaurasia, and S. Arya, “multi‐objective hybrid capuchin search with genetic algorithm based hierarchical resource allocation scheme with Clustering Model in cloud computing environment,” Concurrency and Computation: Practice and Experience, vol. 35, no. 7, 2023.

  • Heidari, Ali Asghar, Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah, Majdi Mafarja, and Huiling Chen. “Harris Hawks Optimization: Algorithm and Applications.” Future Generation Computer Systems 97 (2019): 849–72. https://doi.org/10.1016/j.future.2019.02.028.

  • Agushaka, Jeffrey O., Absalom E. Ezugwu, and Laith Abualigah. “Gazelle Optimization Algorithm: A Novel Nature-Inspired Metaheuristic Optimizer.” Neural Computing and Applications 35, no. 5 (2022): 4099–4131. https://doi.org/10.1007/s00521-022-07854-6.


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  • Modified Fire Hawks Gazelle Optimization (MFHGO) Algorithm Based Optimized Approach to Improve the QoS Provisioning in Cloud Computing Environment

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Authors

Manila Gupta
Department of Computer Science and Engineering, IFTM University, Moradabad, Uttar Pradesh., India
Devendra Singh
Department of Computer Science and Engineering, IFTM University, Moradabad, Uttar Pradesh., India
Bhumika Gupta
Department of Computer Science and Engineering, Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand., India

Abstract


This work introduces a method that focuses on enhancing resource allocation in cloud computing environments by considering Quality of Service (QoS) factors. Since resource allocation plays a crucial role in determining the QoS of cloud services, it is important to consider indicators like response time, throughput, waiting time, and makespan. The primary difficulty in cloud computing lies in resource allocation, which can be tackled by proposing a novel algorithm known as Modified Fire Hawks Gazelle Optimization (MFHGO). The proposed approach involves the hybridization of the modified fire hawks algorithm with gazelle optimization to facilitate efficient resource allocation. It aims to optimize several objectives, such as resource utilization, degree of imbalance, completion time, throughput, relative error, and response time. To achieve this, an optimal resource allocation is achieved using the Partitioning around K-medoids (PAKM) clustering approach. The proposed model extends the K-means clustering method. For simulation purposes, the GWA-T-12 Bitbrains dataset is utilized, while the JAVA tool is employed for exploratory analysis. The effectiveness of the proposed resource allocation and clustering approach is demonstrated by comparing it with existing schemes. The proposed work's makespan is 1.45 seconds for 50 tasks, 3.6 seconds for 100 tasks, 3.67 seconds for 150 tasks, and 5.34 seconds for 200 jobs. As a result, the proposed model achieves the smallest makespan value when compared to the previous approaches. The proposed work yielded response times of 105ms for a task length of 100, 376ms for 200, 555ms for 300, 624ms for 400, and 1014ms for 500. These results indicate that the proposed model outperforms current approaches by achieving a faster response time and also attains a bandwidth utilization of 0.80%, 0.90%, and 0.97% for 4, 6, and 16 tasks, respectively, indicating better bandwidth utilization than the other approaches.

Keywords


Cloud Computing, Resource Allocation, Throughput, Response Time, Bandwidth Utilization, Time Consumption, Resource Utilization.

References





DOI: https://doi.org/10.22247/ijcna%2F2023%2F221896