A Multi-Objective Differential Evolutionary Algorithm-based Approach for Resource Allocation in Cloud Computing Environment
Subject Areas :Saeed Bakhtiari 1 , Mahan Khosroshahi 2
1 - پلیس فتا
2 -
Keywords: Cloud computing, Scheduling, Allocation, Multi-objective differential evolution algorithm (DEA), Migration.,
Abstract :
In recent years, the cloud computing model has received a lot of attention due to its high scalability, reliability, information sharing and low cost compared to separate machines. In the cloud environment, scheduling and optimal allocation of tasks affects the effective use of system resources. Currently, common methods for scheduling in the cloud computing environment are performed using traditional methods such as Min-Min and meta-heuristic methods such as ant colony optimization algorithm (ACO). The above methods focused on optimizing one goal and do not estimate multiple goals at the same time. The main purpose of this research is to consider several objectives (total execution time, service level agreement and energy consumption) in cloud data centers with scheduling and optimal allocation of tasks. In this research, multi-objective differential evolution algorithm (DEA) is used due to its simple structure features and less adjustable parameters. In the proposed method, a new approach based on DEA to solve the problem of allocation in cloud space is presented which we try to be effective in improving resource efficiency and considering goals such as time, migration and energy by defining a multi-objective function and considering mutation and crossover vectors. The proposed method has been evaluated through a CloudSim simulator by testing the workload of more than a thousand virtual machines on Planet Lab. The results of simulation show that the proposed method in comparison with IqrMc, LrMmt and FA algorithms, in energy consumption by an average of 23%, number of migrations by an average of 29%, total execution time by an average of 29% and service level agreement violation (SLAV) by an average of 1% has been improved. In this case, use of the proposed approach in cloud centers will lead to better and appropriate services to customers of these centers in various fields such as education, engineering, manufacturing, services, etc.
[1] H. Yuan, J. Bi, and M. Zhou, "Spatial task scheduling for cost minimization in distributed green cloud data centers," IEEE Transactions on Automation Science and Engineering, vol. 16, no. 2, pp. 729-740, 2018.
[2] A. Arunarani, D. Manjula, and V. Sugumaran, "Task scheduling techniques in cloud computing: A literature survey," Future Generation Computer Systems, vol. 91, pp. 407-415, 2019.
[3] Y. Li, S. Wang, X. Hong, and Y. Li, "Multi-objective task scheduling optimization in cloud computing based on genetic algorithm and differential evolution algorithm," in 2018 37th Chinese Control Conference (CCC), 2018: IEEE, pp. 4489-4494.
[4] 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, p. 100841, 2021.
[5] E. Arianyan, H. Taheri, and V. Khoshdel, "Novel fuzzy multi objective DVFS-aware consolidation heuristics for energy and SLA efficient resource management in cloud data centers," Journal of Network and Computer Applications, vol. 78, pp. 43-61, 2017.
[6] D. Maharana, B. Sahoo, and S. Sethi, "Energy-efficient real-time tasks scheduling in cloud data centers," International Journal of Science Engineering and Advance Technology, IJSEAT, vol. 4, no. 12, pp. 768-773, 2017.
[7] H. Wang and H. Tianfield, "Energy-aware dynamic virtual machine consolidation for cloud datacenters," IEEE Access, vol. 6, pp. 15259-15273, 2018.
[8] Y. Li, S. Wang, X. Hong, and Y. Li, "Multi-objective task scheduling optimization in cloud computing based on genetic algorithm and differential evolution algorithm." pp. 4489-4494.
[9] S. Srichandan, T. A. Kumar, and S. Bibhudatta, "Task scheduling for cloud computing using multi-objective hybrid bacteria foraging algorithm," Future Computing and Informatics Journal, vol. 3, no. 2, pp. 210-230, 2018.
[10] J. Li, J. Liu, and J. Wang, "An improved differential evolution task scheduling algorithm based on cloud computing," in 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), 2018: IEEE, pp. 30-35.
[11] X. Zhang et al., "Energy-aware virtual machine allocation for cloud with resource reservation," Journal of Systems and Software, vol. 147, pp. 147-161, 2019.
[12] A. Rehman, S. S. Hussain, Z. ur Rehman, S. Zia, and S. Shamshirband, "Multi‐objective approach of energy efficient workflow scheduling in cloud environments," Concurrency and Computation: Practice and Experience, vol. 31, no. 8, p. e4949, 2019.
[13] K. Naik, G. Meera Gandhi, and S. Patil, "Multiobjective virtual machine selection for task scheduling in cloud computing," in Computational Intelligence: theories, applications and future directions-volume I: Springer, 2019, pp. 319-331.
[14] E. Aloboud and H. Kurdi, "Cuckoo-inspired job scheduling algorithm for cloud computing," Procedia Computer Science, vol. 151, pp. 1078-1083, 2019.
[15] M. Aruna, D. Bhanu, and S. Karthik, "An improved load balanced metaheuristic scheduling in cloud," Cluster Computing, vol. 22, no. 5, pp. 10873-10881, 2019.
[16] N. Mc Donnell, E. Howley, and J. Duggan, "Dynamic virtual machine consolidation using a multi-agent system to optimise energy efficiency in cloud computing," Future Generation Computer Systems, vol. 108, pp. 288-301, 2020.
[17] N. Khattar, J. Singh, and J. Sidhu, "An energy efficient and adaptive threshold VM consolidation framework for cloud environment," Wireless Personal Communications, vol. 113, no. 1, pp. 349-367, 2020.
[18] R. Shaw, E. Howley, and E. Barrett, "Applying reinforcement learning towards automating energy efficient virtual machine consolidation in cloud data centers," Information Systems, vol. 107, p. 101722, 2022.
[19] S. Mustafa, K. Bilal, S. U. R. Malik, and S. A. Madani, "SLA-aware energy efficient resource management for cloud environments," IEEE Access, vol. 6, pp. 15004-15020, 2018.
[20] A. Beloglazov and R. Buyya, "Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers," Concurrency and Computation: Practice and Experience, vol. 24, no. 13, pp. 1397-1420, 2012.
[21] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. 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, no. 1, pp. 23-50, 2011.
[22] Standard Performance Evaluation Corporation. https://www.spec.org/power_ssj2008/results/
[23] Amazon EC2 Instance Types. https://aws.amazon.com/ec2/instance-types/
[24] K. Park and V. S. Pai, "CoMon: a mostly-scalable monitoring system for PlanetLab," ACM SIGOPS Operating Systems Review, vol. 40, no. 1, pp. 65-74, 2006.