International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 17, No. 4, 8 Aug. 2025
Cover page and Table of Contents: PDF (size: 2087KB)
An Effective Hybrid HBA-MAO for Task Scheduling with a Hybrid Fault-Tolerant Approach in Cloud Environment
PDF (2087KB), PP.68-86
Views: 0 Downloads: 0
Author(s)
Manoj Kumar Malik
1,2,*
Vineet Kumar Goel
1
Abhishek Swaroop
3
Index Terms
Mexican Axolotl Optimization (MAO), Hybrid Honey Badger Optimization Algorithm (HBA), Cloud computing
Abstract
"Cloud computing" refers to internet-based computing on demand and describes an incredibly scalable technology used by working-class and non-working individuals globally. Fault-tolerant task scheduling is an essential tool used by end users and cloud suppliers. Finding the best resource for the specified input task presents a key challenge for fault-tolerant task schedulers. The studies that have already been done have attempted to address each of these complex issues independently. Still, it is tricky to optimize resources and provide fault tolerance at the same time. In this paper, an effective hybrid HBA-MAO and hybrid fault-tolerant mechanism in cloud computing are designed to appropriate task scheduling in VMs without delay and failure. Various tasks submitted by users and virtual machines are taken as input for the proposed approach. Hybrid Honey Badger Optimization Algorithm (HBA) and Mexican Axolotl Optimization (MAO) are used in this proposed for priority based optimal task scheduling. These scheduled tasks are assigned to the VM for execution. A fault-tolerant mechanism is immediately carried out if the tasks are not completed successfully. The hybrid reactive and proactive fault-tolerant mechanism is used in this proposed approach for a high level of fault tolerance. The proposed approach attains better performance, like 70 sec of response time, 13% of resource utilization and 95% success rate. This approach uses resources efficiently by reducing resource consumption, so it is the best choice for fault-tolerant aware task scheduling.
Cite This Paper
Manoj Kumar Malik, Vineet Kumar Goel, Abhishek Swaroop, "An Effective Hybrid HBA-MAO for Task Scheduling with a Hybrid Fault-Tolerant Approach in Cloud Environment", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.17, No.4, pp. 68-86, 2025. DOI:10.5815/ijigsp.2025.04.05
Reference
[1]S. A. Bello, L. O. Oyedele, O. O. Akinade, M. Bilal, J. M. Delgado, L. A. Akanbi, A. O. Ajayi, and H. A. Owolabi, “Cloud computing in construction industry: Use cases, benefits and challenges,” Automation in Construction. vol. 122, pp. 103441, 2021.
[2]A. Khayer, M. S. Talukder, Y. Bao, and M. N. Hossain, “Cloud computing adoption and its impact on SMEs’ performance for cloud supported operations: A dual-stage analytical approach,” Technology in Society. vol. 60, pp. 101225, 2020.
[3]M. Jesi, A. Appathurai, M. Kumaran, and A. Kumar, “Load Balancing In Cloud Computing Via Mayfly Optimization Algorithm,” Revue Roumaine Des Sciences Techniques — Série Électrotechnique Et Énergétique, vol. 69, no. 1, pp. 79-84, 2024.
[4]D. Saxena, I. Gupta, J. Kumar, A. K. Singh, and X. Wen, “A secure and multiobjective virtual machine placement framework for cloud data center,” IEEE Systems Journal. vol. 16, no. 2, pp. 3163-74, 2021.
[5]S. Karthick, and N. Muthukumaran, “Deep Regression Network for the Single Image Super Resolution of Multimedia Text Image,” 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA). pp. 394-399, 2023.
[6]Z. Li, V. Chang, H. Hu, H. Hu, C. Li, and J. Ge, “Real-time and dynamic fault-tolerant scheduling for scientific workflows in clouds,” Information Sciences. vol. 568, pp. 13-39, 2021.
[7]B. Kada, and H. Kalla, “An efficient fault-tolerant scheduling approach with energy minimization for hard real-time embedded systems,” InInternational Workshop on Distributed Computing for Emerging Smart Networks. pp. 102-117, 2019. Cham: Springer International Publishing.
[8]S. Paul, N. Chatterjee, and P. Ghosal, “A permanent fault tolerant dynamic task allocation approach for Network-on-Chip based multicore systems,” Journal of Systems Architecture. vol. 97, pp. 287-303, 2019.
[9]M. Nanjappan, G. Natesan, and P. Krishnadoss, “An adaptive neuro-fuzzy inference system and black widow optimization approach for optimal resource utilization and task scheduling in a cloud environment,” Wireless Personal Communications. vol. 121 no. 3, pp. 1891-1916, 2021.
[10]S. Kanwal, Z. Iqbal, F. Al-Turjman, A. Irtaza, and M. A. Khan, “Multiphase fault tolerance genetic algorithm for vm and task scheduling in datacenter,” Information Processing & Management. vol. 58, no. 5, pp. 102676, 2021.
[11]P. Kumari, and P. Kaur, “Topology-aware virtual machine replication for fault tolerance in cloud computing systems,” Multiagent and Grid Systems. vol. 16(2), pp. 193-206, 2020.
[12]E. B. Edwin, P. Umamaheswari, and M. R. Thanka, “An efficient and improved multi-objective optimized replication management with dynamic and cost aware strategies in cloud computing data center,” Cluster Computing. vol. 22, pp. 11119-11128, 2019.
[13]T. Tamilvizhi, and B. Parvathavarthini, “A novel method for adaptive fault tolerance during load balancing in cloud computing,” Cluster Computing. vol. 22, pp. 10425-10438, 2019.
[14]V. M. Sivagami, and K. S. Easwarakumar, “An improved dynamic fault tolerant management algorithm during VM migration in cloud data center,” Future Generation Computer Systems. vol. 98, pp. 35-43, 2019.
[15]P. Gupta, P. Rawat, R. P. Tripathi, A. Mundra, S. Mundra, M. K. Goyal, M. Kaur, and R. Agarwal, “Nature inspired fault tolerant task allocation in cloud computing using neural network model,” Journal of Intelligent & Fuzzy Systems. vol. 43, no. 2, pp. 1959-1968, 2022.
[16]V. Sathiyamoorthi, P. Keerthika, P. Suresh, Z. J. Zhang, A. P. Rao, and K. Logeswaran, “Adaptive fault tolerant resource allocation scheme for cloud computing environments,” Journal of Organizational and End User Computing (JOEUC). vol. 33, no. 5, pp. 135-152, 2021.
[17]A. Marahatta, Y. Wang, F. Zhang, A. K. Sangaiah, S. K. Tyagi, and Z. Liu, “Energy-aware fault-tolerant dynamic task scheduling scheme for virtualized cloud data centers,” Mobile Networks and Applications. vol. 24, pp. 1063-1077, 2019.
[18]R. El-Sehiemy, A. Shaheen, A. Ginidi, and M. Elhosseini, “A honey badger optimization for minimizing the pollutant environmental emissions-based economic dispatch model integrating combined heat and power units,” Energies. vol. 15, no. 20, pp. 7603, 2022.
[19]C. S. Rao, A. Pandian, C. R. Reddy, F. Aymen, M. Alqarni, and M. M. Alharthi, “Location determination of electric vehicles parking lot with distribution system by Mexican AXOLOTL optimization and wild horse optimizer,” IEEE Access. vol. 10, pp. 55408-55427, 2022.
[20]E. AbdElfattah, M. Elkawkagy, and A. El-Sisi, “Hybrid Fault Tolerance Approach Based on Replication Impact Heuristic for Cloud Computing,” In2019 29th International Conference on Computer Theory and Applications (ICCTA). pp. 60-67, 2019.
[21]A. Pradhan, and S.K. Bisoy, “A novel load balancing technique for cloud computing platform based on PSO,” Journal of King Saud University-Computer and Information Sciences. vol. 34, no. 7, pp. 3988-3995, 2022.
[22]S. Nabi, M. Ahmad, M. Ibrahim, and H. Hamam, “AdPSO: adaptive PSO-based task scheduling approach for cloud computing,” Sensors, vol. 22, no. 3, p. 920, 2022.
[23]G. Saravanan, S. Neelakandan, P. Ezhumalai, and S. Maurya, “Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing,” Journal of Cloud Computing, vol. 12, no. 1, p. 24, 2023.