Cloud computing, with the features of flexible resource assignment, timely on-demand service and transparent by-quantity pricing, has been widely applied recently. As a new business service model, cloud platform must be capable of satisfying user demand and enhancing quality of service. Therefore, an excellent resource scheduling scheme is requisite to improve the working efficiency of cloud platform and ensure its stability. To achieve the goal of meeting user demand and maximizing the benefit of cloud platform, a dynamic allocation model for cloud resource, which takes into account requirement of users and benefit of cloud platform, is proposed. On the one hand, the concept of user satisfaction is presented to meet the different requirements of different users on time and cost. And a dynamic pricing model is designed to realize the flexible conversion between time and cost, which can instead serve to ensure quality of service and win customer loyalty. On the other hand, genetic algorithm is employed to schedule cloud resources, which can reduce operating cost, shorten makespan, lessen energy consumption, and ensure load balancing, stability and fluency of cloud platform, in order to maximize the benefit of cloud platform as possible. Finally, the results of 5 comparative experiments show that the dynamic pricing model presented is reasonable and the dynamic resource assignment scheme proposed is feasible and efficient.
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Khalil I, Khreishah A, Azeem M (2014) Cloud computing security: a survey. Computers 3(3):1–35
Yu-Qing Z, Xiao-Fei W, Xue-Feng L, Ling L (2016) Survey on cloud computing security. J Softw 27(6):1328–1348 (in Chinese with English abstract)
Tao C, Gao J (2016) Cloud-based mobile testing as a service. Int J Softw Eng Knowl Eng 26(1):147–152
Tsai CW, Huang WC, Chiang MH, Chiang MC (2014) A hyper-heuristic scheduling algorithm for cloud. IEEE Trans Cloud Comput 2(2):236–250
Gaurav KN, Bhardwaj J (2014) A computation offloading framework to optimize makespan in mobile cloud computing environment. Int J Adv Comput Res 4(15):442–449
Wang W, Luo J, Song A (2013) Dynamic pricing based energy cost optimization in data center environments. Chin J Comput 36(3):599–612 (in Chinese with English abstract)
Zhu X, Yang LT, Chen H, Wang J, Yin S, Liu X (2014) Real-time tasks oriented energyaware scheduling in virtualized clouds. IEEE Trans Cloud Comput 2(2):168–180
Tang F, Yang LT, Tang C, Li J, Guo M (2016) A dynamical and load-balanced flow scheduling approach for big data centers in clouds. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2016.2543722
Gutierrez-Garcia JO, Ramirez-Nafarrate A (2015) Agent-based load balancing in cloud data centers. Clust Comput 18(3):1041–1062
Zhou J, Zhang H, Zha W, Chen Y (2014) User-aware resource provision policy for cloud computing. J Comput Res Dev 51(5):1108–1119 (in Chinese with English abstract)
Halboob W, Abbas H, Khan MK, Khan FA, Pasha M (2015) A framework to address inconstant user requirements in cloud SLAs management. Clust Comput 18(1):123–133
Xiong W, Li B, Chen J, Zhou H (2016) A self-adaptation approach based on predictive control for SaaS. Chin J Comput 39(2):364–376 (in Chinese with English abstract)
Park J, An Y, Yeom K (2015) Architecture of virtual cloud bank for mediating cloud services based on cloud user requirements. J KIISE 42(9):1090–1099
Wang Y, Shi W (2014) Budget-driven scheduling algorithms for batches of mapreduce jobs in heterogeneous clouds. IEEE Trans Cloud Comput 2(3):306–319
Jin H, Wang X, Wu S, Di S, Shi X (2015) Towards optimized fine-grained pricing of IaaS cloud platform. IEEE Trans Cloud Comput 3(4):436–448
Xu H, Li B (2013) Dynamic cloud pricing for revenue maximization. IEEE Trans Cloud Comput 1(2):158–171
Yuan Y, Wang C, Wang C, Ren T, Liu B (2016) An uncompleted information game based resources allocation model for cloud computing. J Comput Res Dev 53(6):1342–1351 (in Chinese with English abstract)
Tang L, Chen H (2017) Joint pricing and capacity planning in the IaaS cloud market. IEEE Trans Cloud Comput 5(1):57–70
Basu S, Chakraborty S, Sharma M (2015) Pricing cloud services—the impact of broadband quality. Omega 50:96–114
Yong PC, Nordholm S, Dam HH (2013) Optimization and evaluation of sigmoid function with a priori SNR estimate for real-time speech enhancement. Speech Commun 55(2):358–376
Kumbhare A, Simmhan Y, Frincu M, Prasanna VK (2015) Reactive resource provisioning heuristics for dynamic dataflows on cloud infrastructure. IEEE Trans Cloud Comput 3(2):105–118
Khatua S, Sur PK, Das RK, Mukherjee N (2016) Heuristic-based resource reservation strategies for public cloud. IEEE Trans Cloud Comput 4(4):392401
Moreno IS, Garraghan P, Townend P, Xu J (2014) Analysis, modeling and simulation of workload patterns in a large-scale utility cloud. IEEE Trans Cloud Comput 2(2):208–221
Xu Z, Liang W, Xia Q (2016) Efficient embedding of virtual networks to distributed clouds via exploring periodic resource demands. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2016.2535215
Xu J, Zhang W, Wang T, Huang T (2016) A genetic algorithm based adaptive strategy for image backup of virtual machines. Chin J Comput 39(2):351–363 (in Chinese with English abstract)
Jung D, Suh T, Yu H, Gil JM (2014) A workflow scheduling technique using genetic algorithm in spot instance-based cloud. KSII Trans Internet Inf Syst 8(9):3126–3145
Thammano A, Teekeng W (2014) A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems. Int J Gen Syst 44(4):499–518
Liu X, Li J, Yang Z, Li Z (2017) A task collaborative execution policy in mobile cloud computing. Chin J Comput 40(2):364–377 (in Chinese with English abstract)
Wang Z, Fan X, Zou Y, Chen X (2016) Genetic algorithm based multiple faults localization technique. J Softw 27(4):879–900 (in Chinese with English abstract)
Srinivas M, Patnaik LM (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern 24(4):656–667
Calheiros RN, Ranjan R, De Rose CAF, Buyya R (2009) CloudSim: a novel framework for modeling and simulation of cloud computing infrastructures and services. In: CoRR. arXiv:0903.2525
Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50
Amazon Elastic Compute Cloud. https://amazonaws-china.com/cn/ec2/pricing/ondemand/. Accessed on 13 May 2018.
Liu P (2015) Cloud computing, 3rd edn. Publishing House of Electronics Industry, Beijing (in Chinese with English abstract)
Wang T, Liu Z, Chen Y, Xu Y, Dai X (2014) Load balancing task scheduling based on genetic algorithm in cloud computing. In: International conference on dependable, autonomic and secure computing. IEEE Computer Society, pp 146–152
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This work is partly supported by the National Natural Science Foundation of China under Grant No. 61762041, the Jiangxi Provincial Natural Science Foundation of China under Grant No. 20181BAB202009, and the Key Project of Science and Technology of Jiangxi Provincial Department of Education of China under Grant No. GJJ180250.
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Qian, Z., Wang, X., Liu, X. et al. An approach to dynamically assigning cloud resource considering user demand and benefit of cloud platform. Computing (2020). https://doi.org/10.1007/s00607-020-00821-w
- Resource scheduling
- User satisfaction
- Benefit model of cloud platform
- Genetic algorithm
- Pricing model
Mathematics Subject Classification