Abstract
In cloud computing infrastructure-based services, resource scheduling is still an open issue. Normally resource scheduling involves multi-objective fulfillment but often developed as single-objective problems and solutions are proposed. For dealing with multi-objective problems, optimization techniques come in-aid to develop various techniques as cloud resource scheduling is a soft computing problem. The ultimate aim of cloud resource scheduling is to reduce the billing cost of users and to increase the revenue of cloud service providers. In this paper, the MCAMO technique is proposed for cloud resource scheduling especially dealing with infrastructure-based cloud services. This method deals with multi-objective by applying multi constraints while resource scheduling in infrastructure cloud services. The proposed method is novel as it deals with the constraints of the submitted jobs along with fulfilling the objectives of the cloud service client. For a powerful arrangement, the fitness value worth takes a base worth value and the improved determination of the asset resources relies upon the MCAMO calculation. The performance of the MCAMO technique is assessed by comparing through few existing multi-objective constraints applied VM machines scheduling techniques using the cloudsim simulator. The comparison proves that the proposed MCAMO technique provides optimized resource scheduling than other methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Change history
29 June 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04227-8
References
Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic L (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25(6):599–616
Chen S, Wu J, Lu Z (2012) A cloud computing resource scheduling policy based on genetic algorithm with multiple fitness. In: 2012 IEEE 12th International Conference on Computer and Information Technology (CIT), pp. 177–184
Foster I, Zhao Y, Raicu I, Lu S (2008) Cloud computing and grid computing 360-degree compared. In: Grid Computing Environments Workshop. GCE’08 pp. 1–10
Fu Z-J, Sun X-M, Liu Q, Zhou L, Shu J-G (2015) Achieving efficient cloud search services: multi-keyword ranked search over encrypted cloud data supporting parallel computing. IEICE Trans Commun E98-B(1):190–200
Fu Z-J, Ren K, Shu J-G, Sun X-M, Huang F-X (2016) Enabling personalized search over encrypted outsourced data with efficiency improvement. IEEE Trans Parallel Distrib Syst 27(9):2546–2559
Han S, Min S, Lee H (2019) Energy efficient VM scheduling for big data processing in cloud computing environments. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-019-01361-8
Jennings B, Stadler R (2015) Resource management in clouds: survey and research challenges. J Netw Syst Manag 23(3):567–619
Liu J, Luo XG, Zhang XM, Zhang F (2013) Job scheduling algorithm for cloud computing based on particle swarm optimization. Adv Mater Res 662:957–960
Lu G, Zeng WH (2014) Cloud computing survey. Appl Mech Mater 530:650–661
Madni SHH, Latiff MSA, Coulibaly Y (2016) Resource scheduling for infrastructure as a service (IaaS) in cloud computing: challenges and opportunities. J Netw Comput Appl 68:173–200
Malar ACJ, Kowsigan M, Krishnamoorthy N, Karthick S, Prabhu S, Venkatachalam K (2020) Multi constraints applied energy efficient routing technique based on ant colony optimization used for disaster resilient location detection in mobile ad-hoc network. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-020-01767-9
Manvi SS, Krishna Shyam G (2014) Resource management for Infrastructure as a Service (IaaS) in cloud computing: a survey. J Netw Comput Appl 41:424–440
Mohammed AS, Balaji BS, Saleem Basha MSS, Asha PN, Venkatachalam K (2020) FCO—fuzzy constraints applied cluster optimization technique for wireless AdHoc networks. Comput Commun 154:501–508
Naseri A, Navimipour NJ (2018) A new agent-based method for QoS-aware cloud service composition using particle swarm optimization algorithm. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-018-0773-8
Netjinda N, Sirinaovakul B, Achalakul T (2014) Cost optimal scheduling in IaaS for dependent workload with particle swarm optimization. J. Supercomput 68(3):1579–1603
Ren Y-J, Shen J, Wang J, Han J, Lee SY (2015) Mutual verifiable provable data auditing in public cloud storage. J Internet Technol 16(2):317–323
Sindhu S, Mukherjee S (2013) A genetic algorithm based scheduler for cloud environment. In: 2013 4th International Conference on Computer and Communication Technology (ICCCT), pp 23–27
Sreenu K, Malempati S (2017) MFGMTS: epsilon constraint-based modified fractional grey wolf optimizer for multi-objective task scheduling in cloud computing. IETE J Res 65:201–215
Tsai J-T, Fang J-C, Chou J-H (2013) Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Comput Oper Res 40(12):3045–3055
Xia Z-H, Wang X-H, Sun X-M, Wang Q (2015) A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. IEEE Trans Parallel Distrib Syst 27(3):340–352
Zhang H, Li P, Zhou Z, Yu X (2013) A PSO-based hierarchical resource scheduling strategy on cloud computing. In: Trustworthy computing and services, pp. 325–332
Zhang F, Cao J, Li K, Khan SU, Hwang K (2014) Multi-objective scheduling of many tasks in cloud platforms. Future Gener Comput Syst 37:309–320
Zhong Z, Chen K, Zhai X, Zhou S (2016) Virtual machine-based task scheduling algorithm in a cloud computing environment. Tsinghua Sci Technol 21(6):660–667
Zuo L, Shu L, Dong S, Chen Y, Yan L (2017) A multi-objective hybrid cloud resource scheduling method based on deadline and cost constraints. IEEE Access 5:22067–22080
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04227-8
About this article
Cite this article
Ramamoorthy, S., Ravikumar, G., Saravana Balaji, B. et al. RETRACTED ARTICLE: MCAMO: multi constraint aware multi-objective resource scheduling optimization technique for cloud infrastructure services. J Ambient Intell Human Comput 12, 5909–5916 (2021). https://doi.org/10.1007/s12652-020-02138-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-02138-0