Virtual machine (VM) deployment has been a hot and difficult research topic in the cloud computing field. We address the problem of VM deployment in cloud computing where the situation of physical machine is considered, Traditional VM deployment algorithm considered the resource usage of task without considering the resource utilization of physical machine. There is a significant gap between the solutions obtained by existing algorithms and the optimal solutions, leading to lower resource utilization and unfair resource allocation. We propose an online task scheduling algorithm to solve VM deployment problem. Our proposed algorithm is based on greedy strategy and uses the jump tables to decide VM scheduling. Furthermore, our proposed algorithm considers the characteristics of the task, which will be divided into four types of tasks. This can math task’s property and physical machine to improve deployment efficiency. Experimental results demonstrate that our proposed algorithm is very suitable for cloud computing, effectively utilize resources, and improve performance of the system.
AmbrustM.FoxA. and GriffithR., Above the Clouds: A Berkeley View of Cloud Computing [EB/OL]. (2011-01-25). http://www.eecs.berkeley.edu/pubs/techrpts/2009/EECS-2009-28.pdf.
2.
YangH.J.WangZ.Y.WangX. and ChangJ., High-efficiency texure coding and synthesis on point-based pear surface, Journal of Computational Methods in Sciences and Engineering17 (2017), 397–409.
3.
BuyyaR. and YeoC.S., Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility, Future Generation Computer System25 (2009), 599–616.
MellJ.T., Cloud Computing, National Institute of Standards and Techology20 (2011), 176–183.
6.
BuyyaR. and YeoS.C., Dynamic task assignment and resource management in cloud services by using bargaining solution, Future Generation Computer System25 (2009), 108–115.
7.
GaoT. and KasabovN., A method used for dotted data matrix image processing, Journal of Computational Methods in Sciences and Engineering15 (2015), 685–693.
8.
ArmbustM.FoxA. and GriffithR., Online optimization of busy time on parallel machines, Commun, ACM53 (2010), 215–224.
9.
IsardM.PrabhakaranV. and CurreyJ., Goldberg, Quincy: Fair Scheduling for Distributed Computing Clusters, In SOSP’099 (2009), 57–65.
10.
GhodsiA.ZahariaM. and ShenkerS., Choosy: Max-Min Fair Sharing for Datacenter Jobs with Constraints, EuroSys 201310 (2013), 76–83.
11.
GhodsiA.ZahariaM. and HindmanB., Dominant resource fairness: fair allocation of multiple resource types, in: Proceedings of the 8th USENIX Conference on Networked Systems Design and Implementation, NSDI’11, vol. 8, 2011, pp. 47–55.
12.
WangH. and VarmanP.J., Balancing Fairness and Efficiency in Tiered Storage System with Bottleneck-Aware Allocation, in: Proceedings of the USENIX Conference on File and Storage Technologies (FAST), vol. 26, 2014, pp. 92–98.
13.
LiuX.LiuJ. and ZhuC., P2P Incentive Mechanism Based on Static Gameof Incomplete Information, Applied Mechanics and Materials138 (2012), 1234–1238.
14.
LiuX. and ZhuC., Incentive Mechanism of Equivalent Transaction Based on Reputation, in: International Conference on Computational Intelligence and Software Engineering (CiSE 2011), 170-174,978-1-4244-6448-7/11, vol. 11, 2011, pp. 170–174.
15.
ErguD.KouG. and PengY., Load balancing mechanisms and techniques in the cloud environments, The Journal of Supercomputing64 (2013), 835–842.
16.
SongJ.Q.LiuH.DengK. and ZhangC.M., Super resolution reconstruction of medical image based on adaptive quad-tree decomposition, Journal of Computational Methods in Sciences and Engineering17 (2017), 411–422.
17.
ShalomM.VoloshinA. and WongW.H., Research on Cloud Computing, Theoretical Computer Science23 (2014), 190–197.
18.
MertziosG.B.ShalomM. and VoloshinA., Analysis and Research on the Mechanism of Cloud Computing, Theoretical Computer Science25 (2015), 562–574.
19.
MilaniA.S. and NavimipourN.J., Research on the Calculation Method of Cloud Computing, Journal of Network and Computer Applications28 (2016), 71–86.
20.
SjinK.S.ParkM.J. and JungJ.Y., Algorithm Analysis and Practice of Cloud Computing, Concurrency and Computation: Practice & Experience26 (2014), 1432–1346.
21.
YuanJ., Multi virtual machine fast deployment mechanism study of Wuhan, Huazhong University of Science and Technology42 (2008), 364–372.
22.
WillianmE. and TesauroW.G., Utility functions in autonomic system, in: Proceedings of 1 st IEEE International Conference On Autonomic Computing, vol. 1, 2004, pp. 38–45.
23.
LiuX.ZhangX.LiW. and ZhangX., Swarm optimization algorithms applied to multi-resource fair allocation in heterogeneous cloud computing systems, Computing99 (2017), 1231–1255.
24.
LiuX.ZhangX.LiW. and ZhangX., Discrete Interior Search Algorithm for Multi-resource Fair Allocation in Heterogeneous Cloud Computing Systems, in: Proceedings of he International Conference on Intelligent Computing, Springer International Publishing, 2016, pp. 615–626.
25.
BobroffN.KochutA. and BeatyK., Dynamic placement of virtual machines for managing SLA violations, in: Proceedings of 10th IFIP/IEEEE International Symposium on Integrated Network Management, vol. 32, 2007, pp. 216–228.
26.
YueD.L. and LiuH.T., Computational engineering related computing problems, Computer Engineering and Design32 (2011), 1889–1898.
27.
ShuoJ., PC cluster load balancing scheduling strategy of research in Yingkou, Journal of China University of Petroleum38 (2010), 112–121.
28.
YangX.L.LiX.Q. and LiG.J., The design and implementation of parallel switch system simulation platform based on Java, Journal of Computational Methods in Sciences and Engineering16 (2016), 451–457.
