Statistical-based Heuristic for Tasks Scheduling in Cloud Computing Environment.

Ahmad AlQerem; Ala Hamarsheh

Author(s)

Ahmad AlQerem Department of CIS, Zarqa University, Zarqa, Jordan

Ala Hamarsheh Faculty of Engineering and Information Technology, Arab American University, Jenin, Palestine,

Abstract

Cloud computing is an emerging and innovative technology that has taken the business information systems to wider extent with the fast sharing of vast web resources over the internet. It considers as an extension to distributed and parallel computing. Additionally, it enables sharing, organizing and aggregation of computational machines to satisfy the user demands. Utilizing the resources efficiently is the main challenge of cloud service provider. Task scheduling in cloud computing plays the main role in decreasing the execution time and cost and hence, increasing the profit. This paper addresses the problem independent tasks scheduling over different virtual machines in computational cloud environment. It introduces two batch mode heuristics algorithms for scheduling independent task: high mean absolute deviation first heuristic and QoS Guided Sufferage-HMADF heuristic. Besides, the paper presented other existing batch mode heuristics such as, Min-Min, Max-Min and Sufferage. The four heuristic modes are simulated and the experimental results are discussed using two performance measures, makespan and machine resource utilization.

Publication Details

Page(s) 358-365

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 10, Issue: 2, Year: 2018

Request the full text from PASTIC

References

V.Manglani,A.Jain,V.Prasad, 2017.,” Int. J. Adv. Res. Comput. Sci. 8 -

L.Liu,Z.Qiu, 2016.A survey on virtual machine scheduling in cloud computing,” in 2016 2nd IEEE International Conference on Computer and Communications (ICCC) 2717 -2721

D. P.Chandrashekar, 2015.Robust and fault-tolerant scheduling for scientific workflows in cloud computing environments, -

NagaRaju,Dasari , Saritha,V, 2016.Architecture for Fault Tolerance in Mobile Cloud Computing using Disease Resistance Approach”,International Journal of Communication Networks and Information Security (IJCNIS) 2 -

PradeepSingh,Rawat, 2015.Grid resource computing environment simulation using GridSim toolkit”,2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom) -

I.Foster,C.Kesselman, 1999.Blueprint for a New Computing Infrastructure”, -

SanthoshB,Manjaiah D H, 2015.A hybrid AvgTask-Min and Max-Min algorithm for scheduling tasks in cloud computing,2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) -

M.Murshed,R.Buyya, 2001.GridSim: A Toolkit for the Modeling and Simulation of Global Grids”,Technical Report -

FatosXhafa,Issue4,April2010, .Natures Heuristics for Scheduling Jobs on Computational Grids”,Future Generation Computer Systems 26 608 -621

MarekR.,Ogiela, February 2017.New paradigms for information and services management in grid and pervasive computing”,Future Generation Computer Systems 67 227 -229

MiesoK.,Denko,Issue3, 2011.Trust management in ubiquitous computing: A Bayesian approach”,, 15 March 34 398 -406

R.Buyya, 2008., -

D.Zhu,J.Fan,Knowledge,Grid, 2006., 86 -87

D.Zhu,J.Fan, 2007.,Proceedings of the IEEE International Conference on Information Technology (ICIT) 357 -364

R.Buyya,S.Venugopal, 2005.A Gentle Introduction to Grid Computing and Technologies”,Computer Society of India Communications 29 9 -19

M.Maheswaran,S.Ali,H. J.Siegel,D.Hensgen,R. F.Freund, 1999.Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems”,Journal of Parallel and Distributed Computing 59 107 -131

TracyD.,Braun,Pages, 2016.Performance of Various Mapping Algorithms is Independent of Sizable Variances in Run-time Predictions, Stochasticbased robust dynamic resource allocation for independent tasks in a heterogeneous computing system”,Journal of Parallel and Distributed Computing 97 111 -

T. D. Braun , H. J.,Siegel , N.,Beck , L. L.,Boloni , M.,Maheswaran , A. I.,Reuther , J. P.,Robertson , M. D.,Theys and B.,Yao, 2001.A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems”,Journal of Parallel and Distributed Computing 61 810 -837

F.Dong,J.Luo,L.Gao,L.Ge, 2006.A Grid Task Scheduling Algorithm Based on QoS Priority Grouping”,Proceedings of the Fifth International Conference on Grid and Cooperative Computing (GCC) 58 -61

S.Abuelenin, 2012.Trust Based Grid Batch Mode Scheduling Algorithms”,The 8th International Conference on INFOrmatics and Systems (INFO) 46 -54

QinmaKang,Issue1,Pages, February 2011.A novel discrete particle swarm optimization algorithm for meta-task assignment in heterogeneous computing systems”,Microprocessors and Microsystems 35 17 -

H.Xiaoshan,S.Xianhe,G. V.Laszewski, 2003.QoS Guided Min-Min Heuristic for Grid Task Scheduling”,Journal of Computer Science and Technology 18 442 -451

E. U.Munir,J.Li,S.Shi, 2007.QoS Sufferage Heuristic for Independent Task Scheduling in Grid”,Information Technology Journal 6 1166 -1170

F.Dong,J.Luo,L.Gao,L.Ge, 2006.A Grid Task Scheduling Algorithm Based on QoS Priority Grouping”,Proceedings of the Fifth International Conference on Grid and Cooperative Computing (GCC) 58 -61