A Compact Analytical Survey on Task Scheduling in Cloud Computing Environment

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2021 by IJETT Journal
Volume-69 Issue-2
Year of Publication : 2021
Authors : Prashant B. Jawade, D Sai Kumar, S. Ramachandram
DOI :  10.14445/22315381/IJETT-V69I2P225

Citation 

MLA Style: Prashant B. Jawade, D Sai Kumar, S. Ramachandram  "A Compact Analytical Survey on Task Scheduling in Cloud Computing Environment" International Journal of Engineering Trends and Technology 69.2(2021):178-187. 

APA Style:Prashant B. Jawade, D Sai Kumar, S. Ramachandram. A Compact Analytical Survey on Task Scheduling in Cloud Computing Environment. International Journal of Engineering Trends and Technology, 69(2), 178-187.

Abstract
A computing environment is conveyed by Cloud computing, in which diverse resources are being conveyed via the internet as services to the users or the numerous occupants. In a cloud computing environment, task scheduling is said to be the basic as well as the most significant one. The task scheduling is mainly utilized to designate certain assignments to specific resources at a specific time occasion. Numerous strategies have been proposed to take care of the issues of task scheduling in the cloud environment. Typically, Task scheduling improves the productive use of assets and yields less response time with the goal that the execution of submitted tasks happens inside a potential least time. This paper talks about the investigation of different task scheduling algorithms in a distributed computing condition. This review provides a clear view of different techniques utilized for task scheduling. Further, the security-based task scheduling works are also analyzed. The performance evaluation of different task scheduling techniques is analyzed, and finally, the research gaps and challenges of different task scheduling models.

Reference
[1] Ding Dinga, Xiaocong Fan, Yihuan Zhaoa, Kaixuan Kanga, Qian Yina, Jing Zenga, Q-Learning Based Dynamic Task Scheduling for Energy-Efficient Cloud Computing, Future Generation Computer Systems, (2020).
[2] M.Lavanya, B. Shanthi, S.Saravanan, multi-objective task scheduling algorithm based on sla and processing time suitable for the cloud environment, Computer Communications, (2019).
[3] Randa M. Abdelmoneema, Abderrahim Benslimane, Eman Shaabana, Mobility-aware task scheduling in cloud-Fog IoT-based healthcare architectures, Computer Networks, 179(2020).
[4] Sharma M, Garg R, An artificial neural network-based approach for energy-efficient task scheduling in cloud data centers, Sustainable Computing: Informatics and Systems,(2020).
[5] Gaith Rjoub, Jamal Bentahar, Omar Abdel Wahab, BigTrustScheduling: Trust-aware big data task scheduling approach in cloud computing environments, Future Generation Computer Systems, November (2019).
[6] Longxin Zhang, Liqian Zhou, Ahmad Salahb, Efficient scientific workflow scheduling for deadline-constrained parallel tasks in cloud computing environments, Information Sciences, 531(2020) 31-46.
[7] Andrzej Wilczynski, Joanna Ko lodziej, Modelling and Simulation of Security-aware Task Scheduling in Cloud Computing Based on Blockchain Technology, Simulation Modelling Practice and Theory, (2019).
[8] Shreshth Tuli, Rajinder Sandhu, Rajkumar Buyya, Shared data-aware dynamic resource provisioning and task scheduling for data-intensive applications on hybrid clouds using Aneka, Future Generation Computer Systems, 106(2020) 596-606.
[9] Mohamed Abd Elaziz, Shengwu Xiong, K.P.N. Jayasena, Lin Li, Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution, Knowledge-Based Systems, (2019).
[10] Mohan Sharma and Ritu Garg, HIGA: Harmony-inspired genetic algorithm for rack-aware energy-efficient task scheduling in cloud data centers, Engineering Science and Technology, an International Journal,(2019).
[11] Najme Mansouri, Behnam Mohammad Hasani Zade, and Mohammad Masoud Javidi, Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory, Computers & Industrial Engineering, 130(2019) 597-633.
[12] Li Mao, Yin Li, Gaofeng Peng, Xiyao Xu, Weiwei Lin, A Multi-Resource Task Scheduling Algorithm for Energy-Performance Trade-offs in Green Clouds, Sustainable Computing: Informatics and Systems,(2018).
[13] Srichandan Sobhanayak, Ashok Kumar Turuk, Bibhudatta Sahoo, Task scheduling for cloud computing using multi-objective hybrid bacteria foraging algorithm, Future Computing and Informatics Journal, March (2018).
[14] Tingming Wu, Haifeng Gu, Junlong Zhou, Tongquan Wei, Xiao Liu, Mingsong Chen, Soft Error-Aware Energy-EfÞcient Task Scheduling for Workßow Applications in DVFS-Enabled Cloud, Journal of Systems Architecture, (2018).
[15] Hui Yan, Xiaomin Zhu, Huangke Chen, Hui Guo, Wen Zhou, Weidong Bao, DEFT: Dynamic Fault-Tolerant Elastic Scheduling for Tasks with Uncertain Runtime in Cloud, Information Sciences, (2018).
[16] Suvendu Chandan Nayak, Chitaranjan Tripathy, Deadline based task scheduling using multi-criteria decision-making in a cloud environment, Ain Shams Engineering Journal, (2017).
[17] Shanchen Pang, Wenhao Li1, Hua He, Zhiguang Shan, And Xun Wang, An EDA-GA Hybrid Algorithm for Multi-Objective Task Scheduling in Cloud Computing, IEEE Access, (2019).
[18] Yonghua Xiong, Suzhen Huang, Min Wu, Jinhua She and Keyuan Jiang, A Johnson’sRule-Based Genetic Algorithm for Two-Stage-Task Scheduling Problem in Data-Centers of Cloud Computing, JOURNAL OF LATEX CLASS FILES, 13(9)(2014).
[19] Liyun Zuo1, Lei Shu, Shoubin Dong, Chunsheng Zhu, And Takahiro Hara, A Multi-Objective Optimization Scheduling Method Based on the Ant Colony Algorithm in Cloud Computing, IEEE Access, (2015).
[20] Wei Lu, Ping Lu, Quanying Sun, Shui Yu, And Zuqing Zhu, Profit-Aware Distributed Online Scheduling for Data-Oriented Tasks in Cloud Datacenters, IEEE Access, (2018).
[21] Sanjaya K. Panda, Prasanta K. Jana, Uncertainty-Based QoS Min–Min Algorithm for Heterogeneous Multi-cloud Environment, Arab J Sci Eng, (2016).
[22] Sahar Adabi, Ali Movaghar, Amir Masoud Rahmani, Bi-level fuzzy-based advanced reservation of Cloud workflow applications on distributed Grid resources, J Supercomput, (2013).
[23] Yong Ju Moon, HeonChang Yu, Joon?Min Gil, and JongBeom Lim, A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments, Human- centric computing a dn information sciences, (2017).
[24] Nuttapong Netjinda, Booncharoen Sirinaovaku, Tiranee Achalaku, Cost optimal scheduling in IaaS for dependent workload with particle swarm optimization, J Supercomput, (2014).
[25] Shafi’i Muhammad Abdulhamid, Muhammad Shafie Abd Latiff, Syed Hamid Hussain Madni, Mohammed Abdullahi, Fault tolerance aware scheduling technique for cloud computing environment using a dynamic clustering algorithm, Neural Comput & Application, 29(2018) 279-293.
[26] Sanjaya Kumar Panda, Sohan Kumar Pande, Satyabrata Das, Task Partitioning Scheduling Algorithms for Heterogeneous Multi-Cloud Environment, Arab J Sci Eng, (2017).
[27] Sanjaya K. Pandaa, Indrajeet Gupta and Prasanta K. Jana,: Allocation-Aware Task Scheduling for Heterogeneous Multi-Cloud Systems, Procedia Computer Science, 50(2015) 176 – 184.
[28] Minggang Dong, Lili Fan, Chao Jing, ECOS: An efficient task-clustering based cost-effective aware scheduling algorithm for scientific workflows execution on heterogeneous cloud systems, The Journal of Systems and Software, 158(2019).
[29] Tamanna Jena and J. R. Mohanty, GA-Based Customer-Conscious Resource Allocation and Task Scheduling in Multi-cloud Computing, Arab J Sci Eng, (2017).
[30] Karnam Sreenu & Sreelatha Malempati, MFGMTS: Epsilon Constraint-Based Modified Fractional Grey Wolf Optimizer for Multi-Objective Task Scheduling in Cloud Computing, IETE JOURNAL OF RESEARCH, (2017).
[31] Somula Ramasubbareddy & R. Sasikala, RTTSMCE: a response time aware task scheduling in the multi-cloudlet environment, International Journal of Computers and Applications, (2019).
[32] Sanaj MS, Joe Prathap P M & Valanto Alappatt,Profit maximization based task scheduling in hybrid clouds using whale optimization technique, Information Security Journal: A Global Perspective,(2020).
[33] R. Valarmathi, T. Sheela, Ranging and tuning based particle swarm optimization with bat algorithm for task scheduling in cloud computing, Cluster Computing, (2017).
[34] Shadi Torabi, Faramarz Safi-Esfahani, A dynamic task scheduling framework based on chicken swarm and improved raven roosting optimization methods in cloud computing, J Supercomput, (2018).
[35] Farinaz Hemasian?Etefagh, Faramarz Safi?Esfahani, Dynamic scheduling applying new population grouping of whales meta?heuristic in cloud computing," The Journal of Supercomputing, (2019).
[36] M. Roshni Thanka, P. Uma Maheswari & E. Bijolin Edwin, An improved efficient: Artificial Bee Colony algorithm for security and QoS aware scheduling in the cloud computing environment, Cluster Computing, 22(2019) 10905–10913.
[37] Angela Jennifa Sujana, J, Revathi, T., Joshua Rajanayagam, S., Fuzzy-based Security-Driven Optimistic Scheduling of Scientific Workflows in Cloud Computing, IETE Journal of Research, 66(2)(2020).
[38] Zhongjin Li, Jidong Ge, Hongji Yang, Liguo Huang, Haiyang Hu, Hao Hu, Bin Luo, A security and cost-aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds, Future Generation Computer Systems, 65(2016) 140-152.
[39] Yi Zhang, Yu Liu, Junlong Zhou, Jin Sun, Keqin Li, Slow-movement particle swarm optimization algorithms for scheduling security-critical tasks in resource-limited mobile edge computing, Future Generation Computer Systems, 112(2020) 148-161.
[40] Henrique Yoshikazu Shishido, Júlio Cezar Estrella, Claudio Fabiano Motta Toledo, and Marcio Silva Arantes, Genetic-based algorithms applied to a workflow scheduling algorithm with security and deadline constraints in clouds, Computers and Electrical Engineering, (2017).
[41] Langfang Zeng, Bharadwaj Veeravalli, Xiaorong Li, SABA: A security-aware and budget-aware workflow scheduling strategy in clouds, J. Parallel Distrib. Comput., (2014).
[42] Daniel Grzonka, Agnieszka Jakóbik, Joanna Ko?odziej, Sabri Pllana, Using a multi-agent system and artificial intelligence for monitoring and improving the cloud performance and security, Future Generation Computer Systems, (2017).
[43] Waleed Abd Elkhalik, Ahmad Salah, Ibrahim El-Henawy A Survey on Cloud Computing Scheduling Algorithms, International Journal of Engineering Trends and Technology (IJETT), V60(1) 65-70 (2018).

Keywords
Cloud Computing; Task Scheduling Algorithms; Mode of Scheduling; Performance Parameters; Research Gaps and Challenges.