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Task Scheduling Optimization in Cloud Computing by Social Group Optimization Algorithm


Affiliations
1 Faculty of Computers and Artificial Intelligence, Department of Computer Science, Sohag University, Sohag, 82524, Egypt
 

In cloud computing systems, task scheduling is crucial. Task scheduling cannot be done based on a single criterion but rather on rules and regulations that may be referred to as an agreement between cloud customers and providers. This agreement is nothing more than the user's desire for the providers to offer the kind of service that they expect. Providing high-quality services to consumers under the deal is a critical duty for providers, who must also manage many responsibilities. The task scheduling problem may be considered the search for an ideal assignment or mapping of a collection of subtasks of distinct tasks across the available set of resources to meet the intended goals for tasks. This paper proposes an efficient scheduling task algorithm based on the social group optimization of cloud computing systems. By applying it to three cases, we evaluate the performance of our algorithm. The findings suggest that the proposed strategy successfully achieved the best solution in Makespan, Speedup, Efficiency, and Throughput.

Keywords

Heterogeneous resources, Social Group Optimization Algorithm, Task scheduling, Cloud Computing
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  • R.M. Singh, S. Paul, A. Kumar, Task Scheduling in Cloud Computing : Review, 5 (2014) 7940–7944.

  • L. Guo, S. Zhao, S. Shen, C. Jiang, Task scheduling optimization in cloud computing based on heuristic Algorithm, J. Networks. 7 (2012) 547–553. https://doi.org/10.4304/jnw.7.3.547-553.

  • S. Kaur, A. Verma, An Efficient Approach to Genetic Algorithm for Task Scheduling in Cloud Computing Environment, Int. J. Inf. Technol. Comput. Sci. 4 (2012) 74–79. https://doi.org/10.5815/ijitcs.2012.10.09.

  • K. Dasgupta, B. Mandal, P. Dutta, J.K. Mandal, S. Dam, A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing, Procedia Technol. 10 (2013) 340–347. https://doi.org/10.1016/j.protcy.2013.12.369.

  • Y. Xu, K. Li, L. He, L. Zhang, K. Li, A Hybrid Chemical Reaction Optimization Scheme for Task Scheduling on Heterogeneous Computing Systems, IEEE Trans. Parallel Distrib. Syst. 26 (2015) 3208– 3222. https://doi.org/10.1109/TPDS.2014.2385698.

  • N. Dordaie, N.J. Navimipour, A hybrid particle swarm optimization and hill climbing algorithm for task scheduling in the cloud environments, ICT Express. 4 (2018) 199–202. https://doi.org/10.1016/j.icte.2017.08.001.

  • L.D. Dhinesh Babu, P. Venkata Krishna, Honey bee behavior inspired load balancing of tasks in cloud computing environments, Appl. Soft Comput. J. 13 (2013) 2292–2303. https://doi.org/10.1016/j.asoc.2013.01.025.

  • A.Y. Hamed, M.H. Alkinani, Task scheduling optimization in cloud computing based on genetic algorithms, Comput. Mater. Contin. 69 (2021) 3289– 3301. https://doi.org/10.32604/cmc.2021.018658.

  • S. Satapathy, A. Naik, Social group optimization (SGO): a new population evolutionary optimization technique, Complex Intell. Syst. 2 (2016) 173–203. https://doi.org/10.1007/s40747-016-0022-8.

  • I. Dubey, M. Gupta, Uniform mutation and SPV rule based optimized PSO algorithm for TSP problem, Proc. 2017 4th Int. Conf. Electron. Commun. Syst. ICECS 2017. 17 (2017) 168–172. https://doi.org/10.1109/ECS.2017.8067862.

  • L. Wang, Q.K. Pan, M.F. Tasgetiren, A hybrid harmony search algorithm for the blocking permutation flow shop scheduling problem, Comput. Ind. Eng. 61 (2011) 76–83. https://doi.org/10.1016/j.cie.2011.02.013.

  • K. Dubey, M. Kumar, S.C. Sharma, Modified HEFT Algorithm for Task Scheduling in Cloud Environment, Procedia Comput. Sci. 125 (2018) 725–732. https://doi.org/10.1016/j.procs.2017.12.093.

  • A. Kamalinia, A. Ghaffari, Hybrid Task Scheduling Method for Cloud Computing by Genetic and DE Algorithms, Wirel. Pers. Commun. 97 (2017) 6301– 6323. https://doi.org/10.1007/s11277-017-4839-2.

  • H. Topcuoglu, S. Hariri, M.Y. Wu, Performanceeffective and low-complexity task scheduling for heterogeneous computing, IEEE Trans. Parallel Distrib. Syst. 13 (2002) 260–274. https://doi.org/10.1109/71.993206.

  • S. Gupta, G. Agarwal, V. Kumar, Task scheduling in multiprocessor system using genetic algorithm, ICMLC 2010 - 2nd Int. Conf. Mach. Learn. Comput. (2010) 267–271. https://doi.org/10.1109/ICMLC.2010.50.


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  • Task Scheduling Optimization in Cloud Computing by Social Group Optimization Algorithm

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Authors

Ahmed Y. Hamed
Faculty of Computers and Artificial Intelligence, Department of Computer Science, Sohag University, Sohag, 82524, Egypt
M. Kh. Elnahary
Faculty of Computers and Artificial Intelligence, Department of Computer Science, Sohag University, Sohag, 82524, Egypt
Hamdy H. El-Sayed
Faculty of Computers and Artificial Intelligence, Department of Computer Science, Sohag University, Sohag, 82524, Egypt

Abstract


In cloud computing systems, task scheduling is crucial. Task scheduling cannot be done based on a single criterion but rather on rules and regulations that may be referred to as an agreement between cloud customers and providers. This agreement is nothing more than the user's desire for the providers to offer the kind of service that they expect. Providing high-quality services to consumers under the deal is a critical duty for providers, who must also manage many responsibilities. The task scheduling problem may be considered the search for an ideal assignment or mapping of a collection of subtasks of distinct tasks across the available set of resources to meet the intended goals for tasks. This paper proposes an efficient scheduling task algorithm based on the social group optimization of cloud computing systems. By applying it to three cases, we evaluate the performance of our algorithm. The findings suggest that the proposed strategy successfully achieved the best solution in Makespan, Speedup, Efficiency, and Throughput.

Keywords


Heterogeneous resources, Social Group Optimization Algorithm, Task scheduling, Cloud Computing

References