Cost-Efficient Task Scheduling Algorithm for Reducing Energy Consumption and Makespan of Cloud Computing

Document Type : Original Article

Authors

• najme mansouri
• Reyhane ghafari

Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran

10.22067/cke.2022.70000.1008

Abstract

In cloud computing, task scheduling is one of the most important issues that need to be considered for enhancing system performance and user satisfaction. Although there are many task scheduling strategies, available algorithms mainly focus on reducing the execution time while ignoring the profits of service providers. In order to improve provider profitability as well as meet the user requirements, tasks should be executed with minimal cost and without violating Quality of Service (QoS) restrictions. This study presents a Cost and Energy-aware Task Scheduling Algorithm (CETSA) intending to reduce makespan, energy consumption, and cost. The proposed algorithm considers the trade-off between cost, energy consumption, and makespan while considering the load on each virtual machine to prevent virtual machines from overloading. Experimental results with CloudSim show that the CETSA algorithm has better results in terms of energy consumption, waiting time, success rate, cost, improvement ratio, and degree of imbalance compared with MSDE, CPSO, CJS, and FUGE.

Keywords

• Cloud
• Task scheduling
• Cost
• Energy consumption
• Makespan

Main Subjects

• Distributed System

---

References

[1]    N. Mansouri and M. M. Javidi., "A review of data replication based on meta-heuristics approach in cloud computing and data grid", Soft Computing. vol. 24, pp. 19, 2020.

[2]    R. Medara, R. S. Singh, and Amit, "Energy-aware workflow task scheduling in clouds with virtual machine consolidation using discrete water wave optimization", Simulation Modelling Practice and Theory. vol. 110, 2021.

[3]    E. H. Houssein, A. G. Gad, Y. M. Wazery, and P. N. Suganthan., "Task scheduling in cloud computing based on meta-heuristics: Review, Taxonomy, open challenges, and future trends", Swarm and Evolutionary Computation, vol. 62, pp. 1–41, 2021.

[4]    R. Medara and R. S. Singh., "Energy efficient and reliability aware workflow task scheduling in cloud environment", Wireless Personal Communications, 2021.

[5]    M. Sharma and R. Garg., "An artificial neural network based approach for energy efficient task scheduling in cloud data centers", Sustainable Computing: Informatics and Systems. vol. 26, 2020.

[6]    L. A. Barroso, J. Clidaras, and U. Hölzle., "The datacenter as a computer: An introduction to the design of warehouse-scale machines", Synthesis lectures on computer architecture. vol. 8(3), pp. 1–154, 2013.

[7]    A. Uchechukwu, K. Li, and Y. Shen., "Energy consumption in cloud computing data centers", International Journal of Cloud Computing and Services Science (IJ-CLOSER). vol. 3(3), pp. 31–48, 2014.

[8]    B. Whitehead, D. Andrews, A. Shah, and G. Maidment., "Assessing the environmental impact of data centres part 1: Background, energy use and metrics", Building and Environment. vol. 82, pp. 151–159, 2014.

[9]    A. Greenberg, J. Hamilton, D. A. Maltz, and P. Patel. (2008). The cost of a cloud: research problems in data center networks. ACM SIGCOMM Computer Communication Review. vol. 39(1), pp. 68–73, 2008.

 [10] A. Khelifa, T. Hamrouni, R. Mokadem, and F. Ben Charrada. (2020). SLA-aware task scheduling and data replication for enhancing provider profit in clouds. Procedia Computer Science. vol. 176, pp. 3143–3152, 2020.

[11] M. Lavanya, B. Shanthi, and S. Saravanan., "Multi objective task scheduling algorithm based on SLA and processing time suitable for cloud environment", Computer Communications, vol. 151, pp. 183–195, 2020.

[12] A. Asghari, M. K. Sohrabi, and F. Yaghmaee., "Online scheduling of dependent tasks of cloud’s workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based agents", Soft Computing, vol. 24(21), 2020.

 [13] N. Mansouri, R. Ghafari, and B. M. H. Zade., "Cloud computing simulators: A comprehensive review", Simulation Modelling Practice and Theory, vol. 104, pp. 1–101, 2020.

[14] N. Mansouri, B. M. H. Zade, and M. M. Javidi., "A multi-objective optimized replication using fuzzy based self-defense algorithm for cloud computing", Journal of Network and Computer Applications, vol. 171, pp. 1–33, 2020.

[15]  N. K. Biswas, S. Banerjee, U. Biswas, and U. Ghosh., "An approach towards development of new linear regression prediction model for reduced energy consumption and SLA violation in the domain of green cloud computing", Sustainable Energy Technologies and Assessments, vol. 45, 2020.

[16] Z. Tong, X. Deng, H. Chen, and J. Mei., "DDMTS: A novel dynamic load balancing scheduling scheme under SLA constraints in cloud computing", Journal of Parallel and Distributed Computing, vol. 149, pp. 138–148, 2021.

[17] D.A. Shafiq, N.Z. Jhanjhi, A. Abdullah., "Load balancing techniques in cloud computing environment: A review", Journal of King Saud University, 2021.

[18]  K. Dubey and S. C. Sharma., "A hybrid multi-faceted task scheduling algorithm for cloud computing environment", International Journal of System Assurance Engineering and Management, 2021.

[19]  A. Khelifa, T. Hamrouni, R. Mokadem, and F. Ben Charrada., "Combining task scheduling and data replication for SLA compliance and enhancement of provider profit in clouds", Applied Intelligence, 2021.

[20]  G. Sreenivasulu and I. Paramasivam., "Hybrid optimization algorithm for task scheduling and virtual machine allocation in cloud computing", Evolutionary Intelligence, 2020.

[21]  B. Wang, C. Wang, W. Huang, Y. Song, and X. Qin., "Security-aware task scheduling with deadline constraints on heterogeneous hybrid clouds", Journal of Parallel and Distributed Computing, vol. 153, pp. 15–28, 2021.

[22] A. Pradhan, S. K. Bisoy, and A. Das. (2020). A survey on PSO based meta-heuristic scheduling mechanism in cloud computing environment. Journal of King Saud University-Computer and Information Sciences, 2020.

 [23] R. Jia, Y. Yang, J. Grundy, J. Keung, and L. Hao. (2021). A systematic review of scheduling approaches on multi-tenancy cloud platforms. Information and Software Technology. vol. 132, pp. 1–55, 2021.

[24] M. Hosseinzadeh, M. Y. Ghafour, H. K. Hama, B. Vo, and A. Khoshnevis., "Multi-objective task and workflow scheduling approaches in cloud computing: a comprehensive review", Journal of Grid Computing, vol. 18(3), pp. 327–356, 2020.

[25] P. Han, C. Du, J. Chen, F. Ling, and X. Du., "Cost and makespan scheduling of workflows in clouds using list multiobjective optimization technique", Journal of Systems Architecture, vol. 112, 2021.

[26] N. Rizvi, R. Dharavath, and D. R. Edla., "Cost and makespan aware workflow scheduling in IaaS clouds using hybrid spider monkey optimization", Simulation Modelling Practice and Theory, vol. 110, 2021.

[27] C. K. Swain, B. Gupta, and A. Sahu., "Constraint aware profit maximization scheduling of tasks in heterogeneous datacenters", Computing, vol. 102(10), pp. 2229–2255, 2020.

[28] M. Sohaib Ajmal, Z. Iqbal, F. Zeeshan Khan, M. Bilal, and R. Majid Mehmood., "Cost-based energy efficient scheduling technique for dynamic voltage and frequency scaling system in cloud computing", Sustainable Energy Technologies and Assessments, 2021.

[29] M. Hussain, L.-F. Wei, A. Lakhan, S. Wali, S. Ali, and A. Hussain., "Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing", Sustainable Computing: Informatics and Systems, vol. 30, pp. 1–12, 2021.

[30] M. Sharma and R. Garg., "HIGA: Harmony-inspired genetic algorithm for rack-aware energy-efficient task scheduling in cloud data centers", Engineering Science and Technology, an International Journal, vol. 23(1), pp. 211–224, 2020.

[31] D. Ding, X. Fan, Y. Zhao, K. Kang, Q. Yin, and J. Zeng., "Q-learning based dynamic task scheduling for energy-efficient cloud computing", Future Generation Computer Systems, vol. 108, pp. 361–371, 2020.

[32] D. K. Shukla, D. Kumar, and D. S. Kushwaha., "Task scheduling to reduce energy consumption and makespan of cloud computing using NSGA-II", Materials Today: Proceedings, 2021.

[33]  A. A. Khan and M. Zakarya., "Energy, performance and cost efficient cloud datacentres: A survey", Computer Science Review, vol. 40, 2021.

[34] H. Yuan, H. Liu, J. Bi, and M. Zhou., "Revenue and energy cost-optimized biobjective task scheduling for green cloud data centers", IEEE Transactions on Automation Science and Engineering, vol. 18(2), pp. 817–830, 2020.

[35]  W. Jing, C. Zhao, Q. Miao, H. Song, and G. Chen., "QoS-DPSO: QoS-aware task scheduling for cloud computing system", Journal of Network and Systems Management, vol. 29(1), pp.1 –29, 2020.

[36]  J. Kumar Samriya and N. Kumar., "An optimal SLA based task scheduling aid of hybrid fuzzy TOPSIS-PSO algorithm in cloud environment", Materials Today: Proceedings, 2020.

[37]  H. Krishnaveni and V. S. J. Prakash., "Execution time based sufferage algorithm for static task scheduling in cloud", Presented at Advances in Big Data and Cloud Computing. pp. 61–70, 2019.

[38]  X. Chen, L. Cheng, C. Liu, Q. Liu, J. Liu, Y. Mao, J. Murphy., "A woa-based optimization approach for task scheduling in cloud computing systems", IEEE Systems Journal, vol. 14(3), pp. 3117–3128, 2020.

[39]  S. Mirjalili and A. Lewis., "The whale optimization algorithm", Advances in engineering software, vol. 95, pp. 51–67, 2016.

[40]  J. Kennedy and R. Eberhart., "Particle swarm optimization", Presented at Proceedings of ICNN’95-international conference on neural networks, vol. 4, pp. 1942–1948, 1995.

[41]  M. Dorigo, V. Maniezzo, and A. Colorni., "Ant system: optimization by a colony of cooperating agents", IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 26(1), pp. 29–41, 1996.

[42]  M. Abd Elaziz, S. Xiong, K. P. N. Jayasena, and L. Li., "Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution", Knowledge-Based Systems, vol. 169, pp. 39–52, 2019.

[43]  G.-G. Wang., "Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems", Memetic Computing, vol. 10(2), pp. 151–164, 2018.

[44] R. Storn and K. Price., "Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces", Journal of global optimization, vol. 11(4), pp. 341–359, 1997.

[45] J. H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, 1992.

[46] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya., "CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms", Software: Practice and experience, vol. 41(1), pp. 23–50, 2011.

[47] T. P. Jacob and K. Pradeep., "A multi-objective optimal task scheduling in cloud environment using cuckoo particle swarm optimization", Wireless Personal Communications, vol. 109(1), pp. 315–331, 2019.

[48] X.-S. Yang and S. Deb., "Cuckoo search via Lévy flights", Presented at 2009 World congress on nature & biologically inspired computing (NaBIC), pp. 210–214, 2009.

[49] K. Dubey, M. Kumar, and S. C. Sharma., "Modified HEFT algorithm for task scheduling in cloud environment", Procedia Computer Science, vol. 125, pp. 725–732, 2018.

[50] H. Topcuoglu, S. Hariri, and M.-Y. Wu., "Performance-effective and low-complexity task scheduling for heterogeneous computing", IEEE transactions on parallel and distributed systems, vol. 13(3), pp. 260–274, 2002.

[51] B. L. Pan, Y. P. Wang, H. X. Li, and J. Qian., "Task scheduling and resource allocation of cloud computing based on QoS", Advanced Materials Research, vol. 915, pp. 1382–1385, 2014.

[52] N. Mansouri and M. M. Javidi., "Cost-based job scheduling strategy in cloud computing environments", Distributed and Parallel Databases, pp. 1–36, 2019.

[53] M. Shojafar, S. Javanmardi, S. Abolfazli, and N. Cordeschi., "FUGE: A joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method", Cluster Computing, vol. 18(2), pp. 829–844, 2015.

[54] P. Vas, Artificial-intelligence-based electrical machines and drives: application of fuzzy, neural, fuzzy-neural, and genetic-algorithm-based techniques. 45. Oxford university press, 1999.

[55] H. Zhao, G. Qi, Q. Wang, J. Wang, P. Yang, and L. Qiao., "Energy-efficient task scheduling for heterogeneous cloud computing systems", Presented at 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17^th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). pp. 952–959, 2019.

[56] X. Wei., "Task scheduling optimization strategy using improved ant colony optimization algorithm in cloud computing", Journal of Ambient Intelligence and Humanized Computing, 2020.

[57] U. K. Jena, P. K. Das, and M. R. Kabat., "Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment", Journal of King Saud University-Computer and Information Sciences, 2020.

[58] A. Gupta, H. S. Bhadauria, and A. Singh., "Load balancing based hyper heuristic algorithm for cloud task scheduling", Journal of Ambient Intelligence and Humanized Computing. pp. 1–8, 2020.

[59] I. Bambrik., "A survey on cloud computing simulation and modeling", SN Computer Science, vol. 1(5), pp. –34, 2020.

[60] S. R. Jena, R. Shanmugam, K. Saini, and S. Kumar., "Cloud computing tools: inside views and analysis", Procedia Computer Science, vol. 173, pp. 382–391, 2020.

[61] M. Tawfeek, A. El-Sisi, A. Keshk, F. Torkey., "Cloud task scheduling based on ant colony optimization", The International Arab Journal of Information Technology. vol. 12, pp. 129-137, 2015.

[62] D. Gabi, A. S. Ismail, A. Zainal, Z. Zakaria, and A. Al-Khasawneh., "Cloud scalable multi-objective task scheduling algorithm for cloud computing using cat swarm optimization and simulated annealing", Presented at 2017 8th International Conference on Information Technology (ICIT). pp. 599–604, 2017.