Task Scheduling Mechanism for MapReduce Framework using Inertia Weight based Grey Wolf Optimization
Keywords:
Cloud Computing, Grey Wolf Optimization, Inertia Weight, MapReduce Framework, Task SchedulingAbstract
MapReduce is an admirable Hadoop technique for processing enormous information in cloud computing which runs programs and instructions in equivalent employing computers or processors. It simplifies complex data processing tasks by breaking them down into two basic operations mapping and reducing. Numerous scheduling algorithms are established for the Hadoop-MR model which varies in behavior and design, managing various issues like user share impartiality, and data locality along with resource consciousness. This paper proposed an innovative optimization algorithm such as Inertia Weight-based Grey Wolf Optimization (IW-GWO) Algorithm. The inertia weight strategy is utilized for controlling the exploration and population growth capability which effectively enhances the algorithm search capability and balance the connection between local development and global exploration. The obtained result shows that the IW-GWO attains better results in terms of makespan, cost, execution time, and throughput of 2.82s, 59.75 tasks/s, 52$ and 381.62ms for 200 nodes compared with existing algorithms like MOABCQ_LJF, HWACO, and IMOMVO.
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T. Dreibholz and S. Mazumdar, “Towards a lightweight task scheduling framework for cloud and edge platform,” Internet Things, vol. 21, p. 100651, Apr. 2023. https://doi.org/10.1016/j.iot.2022.100651
R. Nath and A. Nagaraju, “Genetic algorithm based on-arrival task scheduling on distributed computing platform,” Int. J. Comput. Appl., vol. 44, number 9, pp. 887–896, 2022. https://doi.org/10.1080/1206212X.2021.1974751
Z. Peng, P. Pirozmand, M. Motevalli, and A. Esmaeili, “Genetic Algorithm-Based Task Scheduling in Cloud Computing Using MapReduce Framework,” Math. Probl. Eng., vol. 2022, p. 4290382, Sep. 2022. https://doi.org/10.1155/2022/4290382
A.G. Gad, E.H. Houssein, M. Zhou, P.N. Suganthan, and Y.M. Wazery, “Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing,” IEEE Internet Things J., vol. 11, number 1, pp. 1698-1710, Jan. 2024. DOI: 10.1109/JIOT.2023.3291367
M. Subramanian, M. Narayanan, B. Bhasker, S. Gnanavel, M.H. Rahman, and C.H.P. Reddy, “Hybrid electro search with ant colony optimization algorithm for task scheduling in a sensor cloud environment for agriculture irrigation control system,” Complexity, vol. 2022, p. 4525220, Oct. 2022. https://doi.org/10.1155/2022/4525220
R. NoorianTalouki, M.H. Shirvani, and H. Motameni, “A heuristic-based task scheduling algorithm for scientific workflows in heterogeneous cloud computing platforms,” J. King Saud Univ. Comput. Inf. Sci., vol. 34, number 8A, pp. 4902–4913, Sep. 2022. https://doi.org/10.1016/j.jksuci.2021.05.011
M. Manikandan, R. Subramanian, M.S. Kavitha, and S. Karthik, “Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment,” Computer Systems Science & Engineering, vol. 42, number 3, pp. 935–948, Feb. 2022. DOI: 10.32604/csse.2022.021816
A.-N. Zhang, S.-C. Chu, P.-C. Song, H. Wang, and J.-S. Pan, “Task Scheduling in Cloud Computing Environment Using Advanced Phasmatodea Population Evolution Algorithms,” Electronics, vol. 11, number 9, p. 1451, Apr. 2022. https://doi.org/10.3390/electronics11091451
C. Li, C. Zhang, B. Ma, and Y. Luo, “Efficient multi-attribute precedence-based task scheduling for edge computing in geo-distributed cloud environment,” Knowl. Inf. Syst., vol. 64, number 1, pp. 175–205, Jan. 2022. https://doi.org/10.1007/s10115-021-01627-8
S. Liu, X. Ma, Y. Jia, and Y. Liu, “An energy-saving task scheduling model via greedy strategy under cloud environment,” Wireless Commun. Mobile Comput., vol. 2022, p. 8769674, Apr. 2022. https://doi.org/10.1155/2022/8769674
V. Seethalakshmi, V. Govindasamy, and V. Akila, “Real-coded multi-objective genetic algorithm with effective queuing model for efficient job scheduling in heterogeneous Hadoop environment,” J. King Saud Univ. Comput. Inf. Sci., vol. 34, number 6B, pp. 3178–3190, Jun. 2022. https://doi.org/10.1016/j.jksuci.2020.08.003
S. Gupta, S. Iyer, G. Agarwal, P. Manoharan, A.D. Algarni, G. Aldehim, and K. Raahemifar, “Efficient Prioritization and Processor Selection Schemes for HEFT Algorithm: A Makespan Optimizer for Task Scheduling in Cloud Environment,” Electronics, vol. 11, number 16, p. 2557, Aug. 2022. https://doi.org/10.3390/electronics11162557
M. Yadav and A. Mishra, “An enhanced ordinal optimization with lower scheduling overhead based novel approach for task scheduling in cloud computing environment,” J. Cloud Comput., vol. 12, p. 8, Jan. 2023. https://doi.org/10.1186/s13677-023-00392-z
Y. Lu, L. Liu, J. Gu, J. Panneerselvam, and B. Yuan, “EA-DFPSO: An intelligent energy-efficient scheduling algorithm for mobile edge networks,” Digital Commun. Networks, vol. 8, number 3, pp. 237–246, Jun. 2022. https://doi.org/10.1016/j.dcan.2021.09.011
G. Saravanan, S. Neelakandan, P. Ezhumalai, and S. Maurya, “Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing,” J. Cloud Comput., vol. 12, p. 24, Feb. 2023. https://doi.org/10.1186/s13677-023-00401-1
X. Wang, C. Wang, M. Bai, Q. Ma, and G. Li, “HTD: heterogeneous throughput-driven task scheduling algorithm in MapReduce,” Distrib. Parallel Databases, vol. 40, number 1, pp. 135–163, Mar. 2022. https://doi.org/10.1007/s10619-021-07375-6
N. Sharma, Sonal, and P. Garg, “Ant colony based optimization model for QoS-Based task scheduling in cloud computing environment,” Meas.: Sens., vol. 24, p. 100531, Dec. 2022. https://doi.org/10.1016/j.measen.2022.100531
R. Jeyaraj and A. Paul, “Optimizing MapReduce Task Scheduling on Virtualized Heterogeneous Environments Using Ant Colony Optimization,” IEEE Access, vol. 10, pp. 55842–55855, May 2022. DOI: 10.1109/ACCESS.2022.3176729
S. Mangalampalli, G.R. Karri, and M. Kumar, “Multi objective task scheduling algorithm in cloud computing using grey wolf optimization,” Cluster Comput., vol. 26, number 6, pp. 3803–3822, Dec. 2023. https://doi.org/10.1007/s10586-022-03786-x
S.P. Praveen, H. Ghasempoor, N. Shahabi, and F. Izanloo, “A hybrid gravitational emulation local search-based algorithm for task scheduling in cloud computing,” Math. Probl. Eng., vol. 2023, p. 6516482, Feb. 2023. https://doi.org/10.1155/2023/6516482
B. Kruekaew and W. Kimpan, “Multi-objective task scheduling optimization for load balancing in cloud computing environment using hybrid artificial bee colony algorithm with reinforcement learning,” IEEE Access, vol. 10, pp. 17803–17818, Feb. 2022. doi: 10.1109/ACCESS.2022.3149955
C. Chandrashekar, P. Krishnadoss, V.K. Poornachary, B. Ananthakrishnan, and K. Rangasamy, “HWACOA Scheduler: Hybrid Weighted Ant Colony Optimization Algorithm for Task Scheduling in Cloud Computing,” Appl. Sci., vol. 13, number 6, p. 3433, Mar. 2023. https://doi.org/10.3390/app13063433
M. Otair, A. Alhmoud, H. Jia, M. Altalhi, A.M. Hussein, and L. Abualigah, “Optimized task scheduling in cloud computing using improved multi-verse optimizer,” Cluster Comput., vol. 25, number 6, pp. 4221–4232, Dec. 2022. https://doi.org/10.1007/s10586-022-03650-y
W. Long, T. Wu, M. Xu, M. Tang, and S. Cai, “Parameters identification of photovoltaic models by using an enhanced adaptive butterfly optimization algorithm,” Energy, vol. 229, p. 120750, Aug. 2021. https://doi.org/10.1016/j.energy.2021.120750
K. Li, S. Li, Z. Huang, M. Zhang, and Z. Xu, “Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy,” Sci. Rep., vol. 12, p. 18961, Nov. 2022. https://doi.org/10.1038/s41598-022-23713-9
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