Volume 13, Issue 4 (12-2021)                   itrc 2021, 13(4): 28-35 | Back to browse issues page

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Jalalian Z, Sharifi M. A Survey on Task Scheduling Algorithms in Cloud Computing for Fast Big Data Processing. itrc. 2021; 13 (4) :28-35
URL: http://ijict.itrc.ac.ir/article-1-496-en.html
1- School of Computer Engineering Iran University of Science and Technology Tehran, Iran
2- School of Computer Engineering Iran University of Science and Technology Tehran, Iran , msharifi@iust.ac.ir
Abstract:   (425 Views)
The recent explosion of data of all kinds (persistent and short-lived) have imposed processing speed constraints on big data processing systems (BDPSs). One such constraint on running these systems in Cloud computing environments is to utilize as many parallel processors as required to process data fast. Consequently, the nodes in a Cloud environment encounter highly crowded clusters of computational units. To properly cater for high degree of parallelism to process data fast, efficient task and resource allocation schemes are required. These schemes must distribute tasks on the nodes in a way to yield highest resource utilization as possible. Such scheduling has proved even more complex in the case of processing of short-lived data. Task scheduling is vital not only to handle big data but also to provide fast processing of data to satisfy modern time data processing constraints. To this end, this paper reviews the most recently published (2020-2021) task scheduling schemes and their deployed algorithms from the fast data processing perspective.
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1. 1- The Combination of historical information of system nodes and the predicted resource requirements with the knowledge of system about capacities of the nodes 2- Dividing the total tasks to a set of subtasks using MRQFLDA exploiting map-reduce framework and whale optimization algorithm 3- MOTS method in conjunction with K-means algorithm accompanied by load balancing strategy to produce the clusters of tasks as an initial population for cluster optimization algorithm called Differential Evolutionary algorithm 4- Hybrid task scheduling algorithm, HTSTC, using task clustering to integrate similar tasks meeting one task cluster’s features 5- Executing tasks on edge devices to reach an overall optimum performance exploiting Catastrophic Genetic Algorithm 6- Utilizing fitness function and optimized operators of mutation and crossover by roulette selection strategy to task run time quantification 7- Modified Henry Gas Solubility Optimization in conjunction with comprehensive opposition-based learning 8- Cost effective resource scheduling Machine Learning based optimization using computation process transfer and computing infrastructure distribution in edge nodes REFERENCES [1] Abdel-Basset, Mohamed, et al. "EA-MSCA: An effective energy-aware multi-objective modified sine-cosine algorithm for real-time task scheduling in multiprocessor systems: Methods and analysis." Expert systems with applications 173 2021: pp. 114699.‏ [2] Liang, Bin, et al. "A low-power task scheduling algorithm for heterogeneous Cloud computing." The Journal of Supercomputing 2020: pp. 1-25.‏ [3] Kalia, Khushboo, and Neeraj Gupta. "Analysis of hadoop MapReduce scheduling in heterogeneous environment." Ain Shams Engineering Journal 12.1 2021: pp. 1101-1110.‏ [4] Hussain, Mehboob, et al. "Energy and performance-efficient task scheduling in heterogeneous virtualized Cloud computing." Sustainable Computing: Informatics and Systems 30 2021: pp. 100517.‏ [5] Sulaiman, Muhammad, et al. "A hybrid list-based task scheduling scheme for heterogeneous computing." The Journal of Supercomputing 2021: pp. 1-37.‏ [6] Wang, Bo, et al. "Security-aware task scheduling with deadline constraints on heterogeneous hybrid Clouds." Journal of Parallel and Distributed Computing 153 2021: pp. 15-28.‏ [7] Sulaiman, Muhammad, et al. "An evolutionary computing-based efficient hybrid task scheduling approach for heterogeneous computing environment." Journal of Grid Computing 19.1 2021: pp. 1-31.‏ [8] Deng, Zexi, et al. "Task scheduling on heterogeneous multiprocessor systems through coherent data allocation." Concurrency and Computation: Practice and Experience 33.10 2021: e6183.‏ [9] Silva, Eduardo Cassiano da. "Representações de Algoritmos Genéticos para o Problema de Escalonamento Estático de Tarefas em Multiprocessadores." 2020.‏ [10] da Silva, Eduardo C., and Paulo HR Gabriel. "A comprehensive review of evolutionary algorithms for multiprocessor DAG scheduling." Computation 8.2 2020: pp. 26.‏ [11] Ismayilov, Goshgar, and Haluk Rahmi Topcuoglu. "Neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in Cloud computing." Future Generation computer systems 102 2020: pp. 307-322.‏ [12] Abdel-Basset, Mohamed, et al. "EA-MSCA: An effective energy-aware multi-objective modified sine-cosine algorithm for real-time task scheduling in multiprocessor systems: Methods and analysis." Expert systems with applications 173 2021: pp. 114699.‏ [13] Alboaneen, Dabiah, et al. "A metaheuristic method for joint task scheduling and virtual machine placement in Cloud data centers." Future Generation Computer Systems 115 2021: pp. 201-212.‏ [14] Lohi, Shantanu A., et al. "Analysis and review of effectiveness of metaheuristics in task scheduling process with delineating machine learning as suitable alternative." 2020 International Conference on Innovative Trends in Information Technology (ICITIIT). IEEE, 2020.‏ [16] Houssein, Essam H., et al. "Task scheduling in Cloud computing based on meta-heuristics: Review, taxonomy, open challenges, and future trends." Swarm and Evolutionary Computation 2021: pp. 100841.‏ [17] Abualigah, Laith, and Ali Diabat. "A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in Cloud computing environments." Cluster Computing 24.1 2021: pp. 205-223.‏ [18] Lavanya, M., B. Shanthi, and S. Saravanan. "Multi objective task scheduling algorithm based on SLA and processing time suitable for Cloud environment." Computer Communications 151, 2020: pp. 183-195.‏ [19] Medara, Rambabu, and Ravi Shankar Singh. "Energy efficient and reliability aware workflow task scheduling in Cloud environment." Wireless Personal Communications 2021: pp. 1-20.‏ [20] Wang, Shudong, et al. "A task scheduling strategy in edge-Cloud collaborative scenario based on deadline." Scientific Programming 2020 [21] Baniata, H., Anaqreh, A., & Kertesz, A. “PF-BTS: A Privacy-Aware Fog-enhanced Blockchain-assisted task scheduling” Information Processing & Management, vol. 58, no. 1,‏ 2021 [22] Narayanan, Deepak, et al. "Heterogeneity-aware cluster scheduling policies for deep learning workloads." 14th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 20). 2020.‏ [23] Li, Jingbo, et al. "OKCM: improving parallel task scheduling in high-performance computing systems using online learning." The Journal of Supercomputing 77.6 2021: pp. 5960-5983.‏ [24] Asghari, Ali, Mohammad Karim Sohrabi, and Farzin Yaghmaee. "Task scheduling, resource provisioning, and load balancing on scientific workflows using parallel SARSA reinforcement learning agents and genetic algorithm." The Journal of Supercomputing 77.3 2021: pp. 2800-2828.‏ [25] Sheng, Shuran, et al. "Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing." Sensors 21.5 2021: 1666.‏ [26] Noorian, Hosseini and Motameni, “A heuristic-based task scheduling algorithm for scientific workflows in heterogeneous Cloud computing platforms”, Journal of King Saud University- Computer and Information Sciences, doi.org/10.1016/j.jksuci.2021.05.011. [27] Hajisami, Abolfazl, et al. "Elastic resource provisioning for increased energy efficiency and resource utilization in Cloud-RANs." Computer Networks 172, 2020: pp. 107170.‏ [28] Sharma, Mohan, and Ritu Garg. "An artificial neural network based approach for energy efficient task scheduling in Cloud data centers." Sustainable Computing: Informatics and Systems 26, 2020: 100373.‏ [29] Abd Elaziz, Mohamed, and Ibrahim Attiya. "An improved Henry gas solubility optimization algorithm for task scheduling in Cloud computing." Artificial Intelligence Review 54.5, 2021: pp. 3599-3637.‏ [30] Shneiderman, Ben. "Human-centered artificial intelligence: Reliable, safe & trustworthy." International Journal of Human–Computer Interaction 36.6, 2020: pp. 495-504.‏ [31] Praveenchandar, J., and A. Tamilarasi. "Dynamic resource allocation with optimized task scheduling and improved power management in Cloud computing." Journal of Ambient Intelligence and Humanized Computing 12.3, 2021: pp. 4147-4159.‏ [32] Aziza, Hatem, and Saoussen Krichen. "A hybrid genetic algorithm for scientific workflow scheduling in Cloud environment." Neural Computing & Applications 32.18 2020 [33] Sanaj, M. S., and PM Joe Prathap. "An efficient approach to the map-reduce framework and genetic algorithm based whale optimization algorithm for task scheduling in Cloud computing environment." Materials Today: Proceedings 37, 2021: pp. 3199-3208.‏ [34] Alsaidy, Seema A., Amenah D. Abbood, and Mouayad A. Sahib. "Heuristic initialization of PSO task scheduling algorithm in Cloud computing." Journal of King Saud University-Computer and Information Sciences, 2020 [35] Balusamy, Jeevanantham, and Manivannan Karunakaran. "Hybridization of immune with particle swarm optimization in task scheduling on smart devices." Distributed and Parallel Databases, 2021: pp. 1-23.‏ [36] Jing, Weipeng, et al. "QoS-DPSO: QoS-aware Task Scheduling for Cloud Computing System." Journal of Network and Systems Management 29.1, 2021: pp. 1-29.‏ [37] Attiya, Ibrahim, Mohamed Abd Elaziz, and Shengwu Xiong. "Job scheduling in Cloud computing using a modified harris hawks optimization and simulated annealing algorithm." Computational intelligence and neuroscience 2020 [38] Jalalian, Zahra, and Mohsen Sharifi. "A hierarchical multi-objective task scheduling approach for fast big data processing." The Journal of Supercomputing, 2021: pp. 1-30.‏ [39] Jalalian, Zahra, and Mohsen Sharifi. "Autonomous Task Scheduling for Fast Big Data Processing." Big Data and HPC: Ecosystem and Convergence. IOS Press, 2018. Pp. 137-154.‏ [40] Mostafa, Samih M., and Hirofumi Amano. "Dynamic round robin CPU scheduling algorithm based on K-means clustering technique." Applied Sciences 10.15, 2020: 5134.‏ [41] Ullah, Ihsan, and Hee Yong Youn. "Task classification and scheduling based on K-means clustering for edge computing." Wireless Personal Communications 113.4, 2020: pp. 2611-2624.‏ [42] Tian, Qiao, et al. "A hybrid task scheduling algorithm based on task clustering." Mobile Networks and Applications 25.4, 2020: pp. 1518-1527.‏ [43] Li, Chunlin, et al. "Adaptive priority-based data placement and multi-task scheduling in geo-distributed Cloud systems." Knowledge-Based Systems 224, 2021: 107050.‏ [44] Javanmardi, Saeed, et al. "FUPE: A security driven task scheduling approach for SDN-based IoT–Fog networks." Journal of Information Security and Applications 60, 2021: 102853.‏ [45] Boveiri, Hamid Reza, Reza Javidan, and Raouf Khayami. "An intelligent hybrid approach for task scheduling in cluster computing environments as an infrastructure for biomedical applications." Expert Systems 38.1, 2021: e12536.‏ [46] Tsai, Jung-Fa, Chun-Hua Huang, and Ming-Hua Lin. "An Optimal Task Assignment Strategy in Cloud-Fog Computing Environment." Applied Sciences 11.4, 2021: 1909.‏ [47] Guevara, Judy C., and Nelson LS da Fonseca. "Task scheduling in Cloud-Fog computing systems." Peer-to-Peer Networking and Applications 14.2, 2021: pp. 962-977.‏ [48] Yuan, Haitao, et al. "Biobjective task scheduling for distributed green data centers." IEEE Transactions on Automation Science and Engineering 18.2, 2020: pp. 731-742.‏ [49] Ibrahim, Ibrahim Mahmood. "Task scheduling algorithms in Cloud computing: A review." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12.4, 2021: pp. 1041-1053.‏ [50] Mirtaheri and Grandinetti,”Optimized load balancing in high-performance computing in big data analytics”, Concurrency Computat Pract Exper, doi: 10.1002/cpe.6265, 2021. [51] Aghdashi and Mirtaheri, “Novel dynamic load balancing algorithm for Cloud-based big data analytics”, The Journal of Supercomputing, doi.org/10.1007/s11227-021-04024-8,2021. [52] Amini, Movaghar and Rahmani, “ A new reliability-based task scheduling algorithm in Cloud computing”,International Journal of Communication Systems,doi.org/10.1002/dac.5022,2021.

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