Task Scheduling is one of the most challenging problems in cloud computing. It is an
NP-Hard and plays an important role in optimizing the use of available resources.
Recently, Multi-Objectives Genetic Algorithm (MOGA) is proposed for cloud tasks
scheduling. However, the execution time of the GA is higher than Particle Swarm
Optimization (PSO), and the convergence is slower. PSO converges fast because it can be
implemented without too many parameters and operators. In this paper, Multi-Objectives PSO
(MOPSO) and MOPSO with Importance Strategy (IS) (MOPSO_IS) algorithms are proposed. MOPSO
algorithm is integrated with the IS to select the global best leader. Furthermore,
incorporating a mutation operator in MOPSO_IS resolved the problem of premature
convergence to the local Pareto-optimal front. The performance of the proposed algorithms
was compared with MOGA and produced better results. The results of the experiments showed
that the proposed MOPSO and MOPSO_IS significantly minimized the total task time and
average task time and obtained better distribution for tasks on the available resources in
a minimal time.
ZhanZ.-H., LiuX.-F., GongY.-J., ZhangJ.etal., Cloud computing resource scheduling and a survey of
its evolutionary approaches, ACM Computing Surveys47(4) (2015),
1–33.
2.
AbdullahM., LuK.,
WiederP. and YahyapourR.,
A heuristic-based approach for dynamic VMs consolidation in cloud data
centers, Arabian Journal for Science and Engineering42(8) (2017),
3535–3549.
3.
BuyyaR., BrobergJ. and GoscinskiA.M.,
Cloud computing: Principles and paradigms, John Wiley & Sons,
87, 2010.
4.
BuyyaR., YeoC.S., VenugopalS. and
BrandicJ.I.,
Cloud computing and emerging it platforms: Vision, hype, and reality for
delivering computing as the 5th utility, Future Generation
Computer Systems25(6) (2009),
599–616.
5.
LinY.-T., WenM.-L., JouM.
and WuD.-W., A
cloud-based learning environment for developing student reflection
abilities, Computers in Human Behavior32 (2014),
244–252.
6.
BarleyD.,
Cloud computing’s effect on enterprises-in terms of cost and
security, Lund University,
2011.
7.
ZahraP., MohammadS., BahmanJ. and AjithA., Using
imperialist competition algorithm for independent task scheduling in grid
computing, Journal of Intelligent & Fuzzy Systems27(1) (2014),
187–199.
8.
PaciniE., MateosC. and GarinoC.G.,
Distributed job scheduling based on Swarm Intelligence: A
survey, Computers & Electrical Engineering40(1) (2014),
252–269.
9.
KeshanchiB. and
NavimipourN.J.,
Priority-based task scheduling in the cloud systems using a memetic
algorithm, Journal of Circuits, Systems and Computers25(10) (2016),
1–33.
10.
RamezaniF., LuJ.
and HussainF.,
Task scheduling optimization in cloud computing applying multi-objective
particle swarm optimization, Proceeding of the International
Conference on Service-Oriented Computing, Springer, 2013, pp.
237–251.
11.
XuZ., XuX.
and ZhaoX., Task
scheduling based on multiobjective genetic algorithm in cloud computing,
Journal of Information & Computational Science12(4) (2015),
1429–1438.
12.
FengM., WangX.,
ZhangY. and LiJ.,
Multi-objective particle swarm optimization for resource allocation in
cloud computing, Proceedings of the IEEE 2nd International
Conference on Cloud Computing and Intelligent Systems3 (2012),
1161–1165.
13.
ChenH. and GuoW.,
Real-time task scheduling algorithm for cloud computing based on particle
swarm optimization, Proceedings of the International Conference
on Cloud Computing and Big Data in Asia, Springer, 2015, pp.
141–152.
14.
AwadA., El-HefnawyN. and
AbdelH.,
kader, Enhanced particle swarm optimization for task scheduling in cloud
computing environments, Procedia Computer Science65 (2015),
920–929.
15.
BoloorK., ChirkovaR., SaloT.
and ViniotisY.,
Heuristic-based request scheduling subject to a percentile response time
SLA in a distributed cloud, Proceedings of the IEEE Global
Telecommunications Conference (GLOBECOM 2010) 2010,
pp.1–6.
16.
GeJ.W. and YuanY.S.,
Research of cloud computing task scheduling algorithm based on improved
genetic algorithm, Applied Mechanics and Materials347 (2013),
2426–2429.
17.
KennedyJ. and EberhartR.,
Particle swarm optimization, Proceedings of the
IEEE International Conference on Neural Networks (ICNN 95)4 (1995),
1942–1948.
18.
GuoL., ZhaoS.,
ShenS. and JiangC., Task
scheduling optimization in cloud computing based on heuristic algorithm,
Journal of Networks7(3) (2012),
547–553.
19.
PandeyS., WuL.,
GuruS.M. and BuyyaR., A
particle swarm optimization-based heuristic for scheduling workflow applications in
cloud computing environments, Proceedings of the 24th IEEE
International Conference on Advanced Information Networking and Applications (AINA),
2010, pp. 400–407.
20.
MezmazM., LeeY.C., MelabN., TalbiE.-G. and ZomayaA.Y.,
A bi-objective hybrid genetic algorithm to minimize energy consumption
and makespan for precce-constrained applications using dynamic voltage
scaling, Proceedings of the IEEE Congress on Evolutionary
Computation (CEC), 2010, pp. 1–8eden.
21.
SomasundaramT.S. and
GovindarajanK.,
CLOUDRB: A framework for scheduling and anaging High Performance
Computing (HPC) applications in science cloud, Future Generation
Computer System34 (2014),
47–65.
22.
JenaR.,
Multi objective task scheduling in cloud environment using nested PSO
framework, Procedia Computer Science57 (2015),
1219–1227.
23.
LashkariM. and MoattarM.H.,
The improved k-means clustering algorithm using the proposed extended PSO
algorithm, Proceedings of the International Congress on
Technology, Communication and Knowledge (ICTCK), 2015, pp.
429–434.
24.
GuptaR., GajeraV., JanaP.K.et al., An effective multiobjective workow scheduling in
cloud computing: A PSO based approach, Proceedings of the IEEE
9th International Conference on Contemporary Computing, 2016, pp.
1–6.
25.
ChiregiM. and NavimipourN.J.,
Trusted services identification in the cloud environment using the
topological metrics, Karbala International Journal of Modern
Science2(3) (2016),
203–210.
26.
KeshanchiB., SouriA.
and NavimipourN.J.,
An improved genetic algorithm for task scheduling in the cloud
environments using the priority queues: Formal verification simulation, and statistical
testing, Journal of Systems and Software124 (2017),
1–21.
27.
RazaqueA., VennapusaN.R., SoniN.et al., Task scheduling in cloud computing,
Proceedings of the Conference of Long Island Systems, Applications and
Technology, 2016, pp. 1–5.
28.
MooreJ. and ChapmanR.,
Department of Computer Science and Software Engineering, Auburn
University, Application of particle swarm to multiobjective
optimization1999.
29.
KumarV. and MinzS.,
Multi-objective particle swarm optimization: An
introduction, SmartCR4(5) (2014),
335–353.
30.
CoelloC.A.C.,
PulidoG.T. and
LechugaM.S.,
Handling multiple objectives with particle swarm
optimization, IEEE Transactions on Evolutionary
Computation8(3) (2004),
256–279.
31.
SalazarM.,
Lechuga, Ph.D. thesis, University of Birmingham,
Multi-objective optimisation using sharing in swarm optimisation
algorithms2009.
32.
SheikholeslamiF. and
NavimipourN.J.,
Service allocation in the cloud environments using multi-objective
particle swarm optimization algorithm based on crowding distance,
Swarm and Evolutionary Computation35 (2017),
53–64.
33.
Reyes-SierraM. and
CoelloC.C.,
Multi-objective particle swarm optimizers: A survey of the
state-of-the-art, Int J Comput Intell Res2(3) (2006),
287–308.
34.
RuiF., LixinW., XinL.
and ZiyuH., A
novel multiobjective PSO algorithm based on completionchecking,
Journal of Intelligent & Fuzzy Systems34(1) (2018),
321–333.
35.
NZanywayingomaF. and
YangY.,
Effective task scheduling and dynamic resource optimization based on
heuristic algorithms in cloud computing environment, KSII
Transaction on Internet and Information Systems11(12) (2017),
5780–5802.
