Energy Aware Multi Objective Algorithm for Task Scheduling on DVFS-Enabled Cloud Datacenters using Fuzzy NSGA-II

Fatehi, Saeed; Motameni, Homayun; Barzegar, Behnam; Golsorkhtabaramiri, Mehdi

doi:10.22075/ijnaa.2020.21625.2283

Energy Aware Multi Objective Algorithm for Task Scheduling on DVFS-Enabled Cloud Datacenters using Fuzzy NSGA-II

Document Type : Research Paper

Authors

Saeed Fatehi ¹

Homayun Motameni ²

Behnam Barzegar ³

Mehdi Golsorkhtabaramiri ³

¹ Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran

² Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran

³ Faculty member, Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran

10.22075/ijnaa.2020.21625.2283

Abstract

Nowadays, energy consumption is curtailed in an effort to further protect the environment as well as to avoid service level agreement (SLA) breach, as critical issues in task scheduling on heterogeneous computing centers. Any reliable task scheduling algorithm should minimize energy consumption, makespan, and cost for cloud users and maximize resource utilization. However, reduction of energy consumption leads to larger makespan and decreases load balancing and customer satisfaction. Therefore, it is essential to obtain a set of non-domination solutions for these multiple, conflicting objectives, as a non-linear, multi-objective, NP-hard problem. This paper formulates the energy efficient task scheduling in green data centers as a multi-objective optimization problem so that fuzzy Non-dominated Sorting Genetic Algorithm 2 (NSGA-II) has been applied using the concept of Dynamic Voltage Frequency Scaling (DVFS). In this procedure, we adopted fuzzy crossover and mutation for optimal convergence of initial solutions. For this purpose, the binary variance function of gene values and the mean variance function of objective values are proposed for fuzzy control of mutation rate, increasing the variation in the optimal Pareto front as well as the correct frequency variance function of the processors engaged in scheduling to control the crossover rate. This serves to add the objective of indirect load balancing to the optimization objectives, thereby to replace the three-objective optimization process with four-objective optimization. In the experiments, the proposed NSGA-II with fuzzy algorithm is compared against the NSGA-II algorithm, involving three scheduling strategies namely Green, Time and Cost Oriented Scheduling Strategy. The simulation results illustrate that the newly method finds better solutions than others considering these objectives and with less iteration. In fact, the optimal Pareto solutions obtained from the proposed method improved the objectives of makespan, cost, energy and load balance by 4%, 17%, 1% and 13%, respectively.

Keywords

Green Computing

Multi Objective Optimization

Pareto solutions

DVFS

Task scheduling

[1] B. Barzegar, H. Motameni and, A. Movaghar, EATSDCD: A green energy-aware scheduling algorithm for parallel
task-based application using clustering, duplication and DVFS technique in cloud datacenters, J. Intel. Fuzzy Syst.
36(6) (2019) 5135–5152.
[2] L. Wang, K. Su, D. Chen, J. Kolodziej, R. Ranjan, C.Z. Xu and A. Zomaya, Energy-aware parallel task scheduling
in a cluster, Future Gener. Comput. Syst. 29(7) (2013) 1661—1670.
[3] H. Lei, R. Wang, T. Zhang, Y. Liu and Y. Zha, A multi-objective co-evolutionary algorithm for energy-efficient
scheduling on a green data center, Comput. Oper. Res. 75 (2016) 103–117.
[4] X. Zhang, T. Wu, M. Chen, T. Wei, J. Zhou, S. Hu and R. Buyya, Energy-aware virtual machine allocation for
cloudwith resource reservation, J. Syst. Softw. 147 (2019) 147-161.
[5] S. Mustafa, B. Nazir, A. Hayat and S.A. Madani, Resource management in cloud computing: taxonomy, prospects,
and challenges, Comput. Electr. Eng. 47 (2015) 186—203.
[6] B. Barzegar, A.M. Rahmani and K. Zamanifar, Gravitational emulation local search algorithm for advanced
reservation and scheduling in grid computing systems, 2009 Fourth Int. Conf. Comput. Sci. Conver.gence Inf.
Tech. (2009) p.1240–1245.
[7] G. Subashini and M.C. Bhuvaneswari, NSGA - II with controlled elitism for scheduling tTasks in heterogeneous
computing systems, Int. J. Open Prob.lems Compt. Math. 4(1) (2011) 1998–6262.
[8] G. subashini, and M.C. Bhuvaneswari, Non dominated particle swarm optimization for scheduling independent
tasks on heterogeneous distributed environments, Int. J. Advance. Soft Comput. Appl. 3(1) (2011).
[9] H. PENG and Q. LI, One kind of improved load balancing algorithm in grid computing, Int. Conf. Network
Comput. Inf. Secur. 2011.
[10] Y. Li, Y. Yang, M. Ma and L. Zhou, A hybrid load balancing strategy of sequential tasks for grid computingenvironments, Future Gener. Comput. Syst. 25 (2009) 819–828.
[11] X. Jin, F. Zhang, L. Fan, Y. Song and Z. Liu, Scheduling for energy minimization on restricted parallel processors,
J. Parallel Distrib. Comput. 81 (2015) 36—46.
[12] W. Pitek, A. Oleksiak and G. Da Costa, Energy and thermal models for simulation of workload and resource
management in computing systems, Simul. Model.Pract. Theory 58 (2015) 40–54.
[13] Y. Ding, X. Qin, L. Liu and T. Wang, Energy efficient scheduling of virtual machines in cloud with deadline
constraint, Future Gener. Comput. Syst. 50 (2015) 62-–74.
[14] Z. Zhou, J. Abawajy, M. Chowdhury, Z. Hu, K. Li, H. Cheng, A.A. Alelaiwi and F. Li, Minimizing SLA violation
and power consumption in Cloud data centers using adaptive energy-aware algorithms, Future Gener. Comput.
Syst. 86 (2018) 836–850.
[15] H. Lei, T. Zhang, Y. Liu, Y. Zha and X. Zhu, SGEESS: smart green energy-efficient scheduling strategy with
dynamic electricity price for data center, J. Syst. Softw. 108 (2015) 23—38.
[16] A. Sathya Sofia, P. GaneshKumar, Multi-objective task scheduling to minimize energy consumption and makespan
of cloud computing using NSGA-II, J. Netw. Syst. Manag. 26 (2018) 463—485.
[17] C.M. Wu, R.S. Chang and H.Y. Chan, A green energy-efficient scheduling algorithm using the DVFS technique
for cloud datacenters, Future Gener. Comput. Syst. 37 (2014) 141—147.
[18] Y. Hu, C. Liu, K. Li, X. Chen, K. Li, Slack allocation algorithm for energy minimization in cluster systems,
Future Gener. Comput. Syst. 74 (2017) 119-–131.
[19] M. Hosseini Shirvani, A.M. Rahmani and A. Sahafi, A survey study on virtual machine migration and server
consolidation techniques in DVFS-enabled cloud datacenter: taxonomy and challenges, J. King Saud UniversityComputer Inf. Sci. 32(3) (2020) 267–286.
[20] M. Hosseini Shirvani, A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systems, Engin. Appl. Artif. Intel. 90 (2020) 1–20.
[21] A. Sathya Sofia and P. GaneshKumar, Multi-objective task scheduling to minimize energy consumption and
makespan of cloud computing using NSGA-II, J. Netw. Syst. Manag. 26 (2018) 463–0485.
[22] Z. Peng, B. Barzegar, M. Yarahmadi, H. Motameni, P. Pirouzmand, Energy-Aware Scheduling of Workflow Using
a Heuristic Method on Green Cloud, Scientific Programming, 2020.
[23] H. Kumar and S.P. Yadav, NSGA-II based fuzzy multi-objective reliability analysis, Int. J. Syst. Assur. Eng.
Manag. 8 (2017) 817-–825.[24] R. Salimi, H. Motameni and H. Omranpour, Task scheduling using NSGA II with fuzzy adaptive operators for
computational grids, J. Par. Distr. Comput. 74(5) (2014) 2333–2350.