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The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 6/2022

05.01.2022

Selecting services in the cloud: a decision support methodology focused on infrastructure-as-a-service context

verfasst von: Cássio L. M. Belusso, Sandro Sawicki, Vitor Basto-Fernandes, Rafael Z. Frantz, Fabricia Roos-Frantz

Erschienen in: The Journal of Supercomputing | Ausgabe 6/2022

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Abstract

Growing demand for reduced local hardware infrastructure is driving the adoption of Cloud Computing. In the Infrastructure-as-a-Service model, service providers offer virtualized computational resources in the form of virtual machine instances. The existence of a large variety of providers and instances makes the decision-making process a difficult task for users, especially as factors such as the datacenter location - where the virtual machine is hosted - have a direct influence on the price of instances. The same instance may present price differences when hosted in different geographically distributed datacenters and, because of that, the datacenter location needs to be taken into account through the decision-making process. Given this problem, we propose the D-AHP, a methodology to aid decision-making based on Pareto Dominance and Analytic Hierarchy Process (AHP). In the D-AHP, the dominance concept is applied to reduce the number of instances to be compared; the instances selection is based on a set of objectives, while AHP ranks the selected ones from a set of criteria and sub-criteria, among them the datacenter location. The results from case studies show that differences may arise in the results, regarding which instance is more suitable for the user, when considering the datacenter location as a criterion to choose an instance. This fact highlights the need to consider this factor during the process of migrating applications to the Cloud. In addition, Pareto Dominance applied early over the set of total instances has proved to be efficient, once it significantly reduces the number of instances to be compared and ordered by the AHP by excluding instances with less computational resources and higher cost in the decision-making process, mainly for larger application workloads.
Vorheriger Artikel Resource pricing and offloading decisions in mobile edge computing based on the Stackelberg game
Nächster Artikel TRAPPY: a truthfulness and reliability aware application placement policy in fog computing

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Literatur
1.
Kumar M, Sharma SC (2018) Deadline constrained based dynamic load balancing algorithm with elasticity in cloud environment. Comput Elect Eng 69:395–411CrossRef
2.
3.
Kumar M, Dubey K, Pandey R (2021) Evolution of emerging computing paradigm cloud to fog: applications, limitations and research challenges. In: 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp 257–261
4.
Mell P, Grance T The NIST Definition of Cloud Computing. Computer Security Division, Information Technology Laboratory, National Institute of Standards and Technology Gaithersburg, Gaithersburg, special publication 800-145 edition, 2011
5.
Hernández I, Sawicki S, Roos-Frantz F, Frantz RZ (2015) Cloud configuration modelling: a literature review from an application integration deployment perspective. Proc Comput Sci 64:977–983CrossRef
6.
Alkhalil A, Sahandi R, John D (2017) A decision process model to support migration to cloud computing. Int J Bus Inf Syst 24:102–106
7.
Ramchand K, Chhetri MB, Kowalczyk R (2021) Enterprise adoption of cloud computing with application portfolio profiling and application portfolio assessment. J Cloud Comput Adv Syst Appl 10:1–18CrossRef
8.
Li A, Yang X, Kandula S, Zhang M (2010) CloudCmp: Comparing public cloud providers. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, pp 1–14
9.
Kihal SE, Schlereth C, Skiera B (2012) Price comparison for Infrastructure-as-a-Service. In: Proceedings of the ECIS Conference, pp 1–12
10.
Menzel M, Ranjan R (2012) CloudGenius: decision support for web server cloud migration. In: Proceedings of the 21st International Conference on World Wide Web, pp 979–988
11.
Mohan Murthy MK, Sanjay HA, Janagal Padmanabha A (2012) Pricing models and pricing schemes of IaaS providers: A comparison study. In: Proceedings of the International Conference on Advances in Computing, Communications and Informatics, pp 143–147
12.
Menzel M, Ranjan R, Wang L, Khan SU, Chen J (2015) CloudGenius: a hybrid decision support method for automating the migration of web application clusters to public clouds. IEEE Trans Comput 64:1336–1348MathSciNetMATHCrossRef
13.
Emeras J, Varrette S, Bouvry P (2016) Amazon elastic compute cloud (EC2) vs. in-house HPC platform: a cost analysis. In: Proceedings of the CLOUD Conference, pp 284–293
14.
López-Pires F, Barán B (2017) Many-objective optimization for virtual machine placement in cloud computing. In: Research Advances in Cloud Computing, Springer, pp 291–326
15.
Mitropoulou P, Filiopoulou E, Michalakelis C, Nikolaidou M (2016) Pricing cloud IaaS services based on a hedonic price index. Computing 98:1075–1089MathSciNetMATHCrossRef
16.
Al-Faifi A, Song B, Hassan MM, Alamri A, Gumaei A (2019) A hybrid multi criteria decision method for cloud service selection from smart data. Fut Gen Comput Syst 93:43–57CrossRef
17.
Nagarajan R, Thirunavukarasu R (2019) A fuzzy-based decision-making broker for effective identification and selection of cloud infrastructure services. Soft Comput 23:9669–9683CrossRef
18.
Chauhan N, Agarwal R, Garg K, Choudhury T (2020) Redundant IaaS cloud selection with consideration of multi criteria decision analysis. Proc Comput Sci 167:1325–1333CrossRef
19.
Yao Y, Cao J, Li M (2013) A network-aware virtual machine allocation in cloud datacenter. In: IFIP International Conference on Network and Parallel Computing, pp 71–82
20.
Malekimajd M, Movaghar A, Hosseinimotlagh S (2015) Minimizing latency in geo-distributed clouds. J Superc 71:4423–4445CrossRef
21.
Souidi M, Souihi S, Hoceini S, Mellouk A (2015) An adaptive real time mechanism for IaaS cloud provider selection based on QoE aspects. In: 2015 IEEE International Conference on Communications, pp 6809–6814
22.
Ziafat H, Babamir SM (2018) Optimal selection of VMs for resource task scheduling in geographically distributed clouds using fuzzy c-mean and MOLP. Soft Pract Exp 48:1820–1846
23.
Jamshidi P, Ahmad A, Pahl C (2013) Cloud migration research: a systematic review. IEEE Trans Cloud Comput 1:142–157CrossRef
24.
Kumar M, Sharma SC (2018) PSO-COGENT: cost and energy efficient scheduling in cloud environment with deadline constraint. Sustain Comput Inf Syst 19:147–164
25.
Kumar M, Sharma SC, Goel A, Singh SP (2019) A comprehensive survey for scheduling techniques in cloud computing. J Net Comput Appl 143:1–33CrossRef
26.
Leite AF, Alves V, Rodrigues GN, Tadonki C, Eisenbeis C, Melo ACMA (2015) Automating resource selection and configuration in inter-clouds through a software product line method. In: 2015 IEEE 8th International Conference on Cloud Computing, pp 726–733
27.
Gómez Sáez S, Andrikopoulos V, Hahn M, Karastoyanova D, Leymann F, Skouradaki M, Vukojevic-Haupt K (2015) Performance and cost evaluation for the migration of a scientific workflow infrastructure to the cloud. In: Proceedings of the CLOSER Conference, pp 352–361
28.
Ur Rehman Z, Khadeer Hussain O, Khadeer Hussain F (2013) Multi-criteria IaaS service selection based on QoS history. In: 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA), pp 1129–1135
29.
Zhang M, Ranjan R, Nepal S, Menzel M, Haller A (2012) A declarative recommender system for cloud infrastructure services selection. In: International Conference on Grid Economics and Business Models, pp 102–113
30.
Nawaz F, Asadabadi MR, Janjua NK, Hussain OK, Chang E, Saberi M (2018) An MCDM method for cloud service selection using a markov chain and the best-worst method. Knowl-Bas Syst 159:120–131CrossRef
31.
Son A-Y, Huh E-N (2017) Study on a migration scheme by fuzzy-logic-based learning and decision approach for QoS in cloud computing. In: 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), pp 507–512
32.
Gabsi H, Drira R, Ghezala HHB (2019) A hybrid approach for personalized and optimized IaaS services selection. Int J Adv Int Syst 12:14–26
33.
Soltani S, Martin P, Elgazzar K (2018) A hybrid approach to automatic IaaS service selection. J Cloud Comput Adv Syst Appl 7:1–18CrossRef
34.
Zhang G, Zhu X, Bao W, Yan H, Tan D (2018) Local storage based consolidation with resource demand prediction and live migration in clouds. IEEE Access 6:26854–26865CrossRef
35.
Tang J-M, Luo L, Wei K-M (2015) A heuristic resource scheduling algorithm of cloud computing based on polygons correlation calculation. In: 2015 IEEE 12th International Conference on e-Business Engineering, pp 365–370
36.
Portella G, Rodrigues GN, Nakano E, Melo ACMA (2018) Statistical analysis of amazon EC2 cloud pricing models. Conc Comp Pract Exp, pp 1–15
37.
Erradi A, Sharma B, Bouguettaya A (2017) Using financial options for pricing of IaaS cloud resources. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp 584–591
38.
Mansouri Y, Nadjaran Toosi A, Buyya R (2013) Brokering algorithms for optimizing the availability and cost of cloud storage services. In: 2013 IEEE 5th International Conference on Cloud Computing Technology and Science, pp 581–589
39.
Rodamilans CB, Baruchi A, Midorikawa ET (2014) Experiences applying performance evaluation to select a cloud provider. In: Proceedings of Recent Advances in Computer Engineering, Communications and Information Technology, pp 289–300
40.
Chun S-H, Choi B-S (2013) Service models and pricing schemes for cloud computing. Clus Comput 17:529–535CrossRef
41.
Ouarnoughi H, Boukhobza J, Singhoff F, Rubini S (2016) A cost model for virtual machine storage in cloud IaaS context. In: 2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), pp 664–671
42.
Gireesha O, Somu N, Krithivasan K, Sriram VSS (2020) IIVIFS-WASPAS: an integrated multi-criteria decision-making perspective for cloud service provider selection. Fut Gen Comput Syst 103:91–110CrossRef
43.
Ziafat H, Babamir SM (2017) A method for the optimum selection of datacenters in geographically distributed clouds. J Superc 73:4042–4081CrossRef
44.
Huang J, Kauffman RJ, Ma D (2015) Pricing strategy for cloud computing: a damaged services perspective. Dec Suppl Syst 78:80–92CrossRef
45.
Singh VK, Dutta K (2015) Dynamic price prediction for amazon spot instances. In: 2015 48th Hawaii International Conference on System Sciences, pp 1513–1520
46.
Al-Roomi M, Al-Ebrahim S, Buqrais S, Ahmad I (2013) Cloud computing pricing models: a survey. Int J Grid Dist Comput 6:93–106CrossRef
47.
Khajeh-Hosseini A, Greenwood D, Smith JW, Sommerville I (2011) The cloud adoption toolkit: supporting cloud adoption decisions in the enterprise. Soft Pract Exp 42:447–465CrossRef
48.
Samimi P, Patel A (2011) Review of pricing models for grid & cloud computing. In: Proc of IEEE Symposium on Computers and Informatics, pp 634–639
49.
Zhao Z, Jiang Y, Zhao X (2015) SLA_oriented service selection in cloud environment: a PROMETHEE_based approach. In: 2015 4th International Conference on Computer Science and Network Technology (ICCSNT), pp 872–875
50.
Chen S, Lee H, Moinzadeh K (2019) Pricing schemes in cloud computing: utilization-based vs. reservation-based. Prod Oper Manag 28:82–102CrossRef
51.
Dimitri N (2020) Pricing cloud IaaS computing services. J Cloud Comput Adv Syst Appl 9:1–11CrossRef
52.
Jatoth C, Gangadharan GR, Fiore U, Buyya R (2018) SELCLOUD: a hybrid multi-criteria decision-making model for selection of cloud services. Soft Comput 22:1–15
53.
Wu C, Buyya R, Ramamohanarao K (2019) Cloud pricing models: taxonomy, survey, and interdisciplinary challenges. ACM Comput Serv 52(108):1–108
54.
Baranwal G, Kumar D, Raza Z, Vidyarthi DP (2018) A negotiation based dynamic pricing heuristic in cloud computing. Int J Grid Ut Comput 9:83–96CrossRef
55.
Kansal S, Kumar H, Kaushal S, Sangaiah AK (2018) Genetic algorithm-based cost minimization pricing model for on-demand IaaS cloud service. J Superc 74:1–26
56.
Li Y, Meng X, Dong H (2016) A simulated annealing combined genetic algorithm for virtual machine migration in cloud datacenters. In: 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp 572–577
57.
Mazrekaj A, Shabani I, Sejdiu B (2016) Pricing schemes in cloud computing an overview. Int J Adv Comput Sci Appl 7:80–86
58.
Ran Y, Yang J, Zhang S, Xi H (2017) Dynamic iaas computing resource provisioning strategy with QoS constraint. IEEE Trans Serv Comput 10:190–202CrossRef
59.
Wang Q, Ming Tan M, Tang X, Cai W (2017) Minimizing cost in IaaS clouds via scheduled instance reservation. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp 1565–1574
60.
Robert Wilson BM, Khazaei B, Hirsch L (2016) Towards a cloud migration decision support system for small and medium enterprises in Tamil Nadu. In: 2016 IEEE 17th International Symposium on Computational Intelligence and Informatics (CINTI), pp 341–346
61.
Zheng W, Xia Y, Zhou M, Wu L, Luo X, Pang S, Zhu Q (2017) Percentile performance estimation of unreliable iaas clouds and their cost-optimal capacity decision. IEEE Access 5:2808–2818CrossRef
62.
63.
Ur Rehman Z, Khadeer Hussain O, Khadeer Hussain F (2012) IaaS cloud selection using MCDM methods. In: 2012 IEEE Ninth International Conference on e-Business Engineering, pp 246–251
64.
Soni A, Hasan M (2017) Pricing schemes in cloud computing: a review. Int J Adv Comput Res 7:60–70CrossRef
65.
Alabool H, Kamil A, Arshad N, Alarabiat D (2018) Cloud service evaluation method-based multi-criteria decision-making: a systematic literature review. J Syst Soft 139:161–188CrossRef
66.
Hosseinzadeh M, Hama HK, Ghafour MY, Masdari M, Ahmed OH, Khezri H (2020) Service selection using multi-criteria decision making: A comprehensive overview. J Net Syst Manag 28:1639–1693CrossRef
67.
Lee S, Seo KK (2013) A multi-criteria decision-making model for an IaaS provider selection problem. Int J Adv Comput Tech 5:363–367
68.
Lee S, Seo K-K (2015) A hybrid multi-criteria decision-making model for a cloud service selection problem using BSC, fuzzy delphi method and fuzzy AHP. Wir Per Commun 86:57–75CrossRef
69.
Lee Y-H (2014) A decision framework for cloud service selection for SMEs: AHP analysis. SOP Trans Mark Res 1:51–61CrossRef
70.
Sun M, Zang T, Xu X, Wang R (2013) Consumer-centered cloud services selection using AHP. In: 2013 International Conference on Service Sciences (ICSS), pp 1–6
71.
Zhang M, Ranjan R, Menzel M, Nepal S, Strazdins P, Wang L (2015) A cloud infrastructure service recommendation system for optimizing real-time QoS provisioning constraints. IEEE Sys. J., pages 1–12. arXiv preprint arXiv:​1504.​01828
72.
Boutkhoum O, Hanine M, Agouti T, Tikniouine A (2016) Selection problem of cloud solution for big data accessing: Fuzzy AHP-PROMETHEE as a proposed methodology. J Dig Inf Manag 14:368–382
73.
Meesariganda BR, Ishizaka A (2017) Mapping verbal AHP scale to numerical scale for cloud computing strategy selection. Appl Soft Comput 53:111–118CrossRef
74.
Supriya M, Sangeeta K, Patra GK (2016) Trustworthy cloud service provider selection using multi criteria decision making methods. Eng Lett 24:1–10
75.
Sharma M, Sehrawat R (2020) A hybrid multi-criteria decision-making method for cloud adoption: evidence from the healthcare sector. Tech Soc 61:1–12CrossRef
76.
López C, Ishizaka A (2017) GAHPsort: a new group multi-criteria decision method for sorting a large number of the cloud-based ERP solutions. Comput Ind 92:12–24CrossRef
77.
Saripalli P, Pingali G (2011) MADMAC: Multiple attribute decision methodology for adoption of clouds. In: 2011 IEEE 4th International Conference on Cloud Computing, pp 316–323
78.
Sohaib O, Naderpour M (2017) Decision making on adoption of cloud computing in e-commerce using fuzzy TOPSIS. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp 1–6
79.
Silas S, Rajsingh EB, Ezra K (2012) Efficient service selection middleware using ELECTRE methodology for cloud environments. Inf Tech J 11:868–875CrossRef
80.
Adamuthe AC, Pandharpatte RM, Thampi GT (2013) Multiobjective virtual machine placement in cloud environment. In: 2013 International Conference on Cloud & Ubiquitous Computing & Emerging Technologies, pp 8–13
81.
Malekloo M, Kara N, (2014) Multi-objective ACO virtual machine placement in cloud computing environments. In: 2014 IEEE Globecom Workshops (GC Wkshps), pp 112–116
82.
Xu B, Peng Z, Xiao F, Gates AM, Yu J-P (2015) Dynamic deployment of virtual machines in cloud computing using multi-objective optimization. Soft Comput 19:2265–2273CrossRef
83.
Ibrahim E, El-Bahnasawy NA, Omara FA (2016) Task scheduling algorithm in cloud computing environment based on cloud pricing models. In: 2016 World Symposium on Computer Applications & Research (WSCAR), pp 65–71
84.
Kumar Sharma N, Reddy Guddeti RM (2016) On demand virtual machine allocation and migration at cloud data center using hybrid of cat swarm optimization and genetic algorithm. In: 2016 Fifth International Conference on Eco-friendly Computing and Communication Systems (ICECCS), pp 27–32
85.
Sheikholeslami F, Navimipour NJ (2017) Service allocation in the cloud environments using multi-objective particle swarm optimization algorithm based on crowding distance. Sw Evol Comput 35:53–64CrossRef
86.
Dörterler S, Dörterler M, Ozdemir S (2017) Multi-objective virtual machine placement optimization for cloud computing. In: 2017 International Symposium on Networks, Computers and Communications (ISNCC), pp 1–6
87.
Ebadifard F, Morteza Babamir S (2017) Optimizing multi objective based workflow scheduling in cloud computing using black hole algorithm. In: 2017 3th International Conference on Web Research (ICWR), pp 102–108
88.
Jahani A, Khanli LM (2016) Cloud service ranking as a multi objective optimization problem. J Superc 72:1897–1926CrossRef
89.
Sofia AS, GaneshKumar P (2018) Multi-objective task scheduling to minimize energy consumption and makespan of cloud computing using NSGA-II. J Net Syst Man 26:463–485CrossRef
90.
Tao F, Li C, Liao TW, Laili Y (2016) BGM-BLA: a new algorithm for dynamic migration of virtual machines in cloud computing. IEEE Trans Serv Comput 9:910–925CrossRef
91.
Guo L, He Z, Zhao S, Zhang N, Wang J, Jiang C (2012) Multi-objective optimization for data placement strategy in cloud computing. In: International Conference on Information Computing and Applications, pp 119–126
92.
Ramezani F, Lu J, Taheri J, Zomaya AY (2017) A multi-objective load balancing system for cloud environments. Comput J 60:1316–1337
93.
Zuo L, Shu L, Dong S, Zhu C, Hara T (2015) A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. IEEE Access 3:2687–2699CrossRef
94.
Fang F, Qu B-B (2017) Multi-objective virtual machine placement for load balancing. In: Proceedings of the IST Conference, pp 1–9
95.
Chen J, Qin Y, Ye Y, Tang Z (2015) A live migration algorithm for virtual machine in a cloud computing environment. In: 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), pp 1319–1326
96.
Baranwal G, Prakash Vidyarthi D (2014) A framework for selection of best cloud service provider using ranked voting method. In: 2014 IEEE International Advance Computing Conference (IACC), pp 831–837
97.
Chung BD, Seo K-K (2015) A cloud service selection model based on analytic network process. Ind J Sci Technol 8:1–5
98.
Chang X, Wang B, Muppala JK, Liu J (2016) Modeling active virtual machines on IaaS clouds using an M/G/m/m+K queue. IEEE Trans Serv Comput 9:408–420CrossRef
99.
Kumari A, Jain S (2016) Auction based resource allocation strategy for infrastructure as a service. In: 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), pp 381–386
100.
de Moraes LB, Fiorese A, Matos F (2017) A multi-criteria scoring method based on performance indicators for cloud computing provider selection. In: Proceedings of the ICEIS Conference, pp 588–599
101.
Rui Z, Bingyong T (2016) The pricing of cloud computing with preferential policies. In: 2016 IEEE 13th International Conference on e-Business Engineering (ICEBE), pp 232–237
102.
Vukovic M, Hwang J (2016) Cloud migration using automated planning. In: NOMS 2016-2016 IEEE/IFIP Network Operations and Management Symposium, pp 96–103
103.
Marks EA, Lozano B (2010) Executive’s guide to cloud computing. John Wiley & Sons Inc, Hoboken
104.
105.
Pareto V (1896) Cours D’Économie Politique. F. Rouge
106.
Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New YorkMATH
107.
Kumar M, Kishor A, Abawajy J, Agarwal P, Singh A, Zomaya A (2021) ARPS: An autonomic resource provisioning and scheduling framework for cloud platforms. IEEE Transactions on Sustainable Computing
108.
Saha M, Panda SK, Panigrahi S (2021) A hybrid multi-criteria decision making algorithm for cloud service selection. Int J Inf Tech 13:1417–1422
109.
110.
111.
112.
Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48:9–26MATHCrossRef
113.
Reiss C, Wilkes J, Hellerstein JL (2011) Google cluster-usage traces: format + schema. Google Inc., Technical report
114.
Reiss C, Wilkes J, Hellerstein JL (2012) Obfuscatory obscanturism: Making workload traces of commercially-sensitive systems safe to release. In: 2012 IEEE Network Operations and Management Symposium, pp 1279–1286
115.
Reiss C, Tumanov A, Ganger GR, Katz RH, Kozuch MA (2012) Towards understanding heterogeneous clouds at scale: google trace analysis. Intel Science & Technology Center for Cloud Computing, Technical report
116.
Zhang Q, Hellerstein JL, Boutaba R (2011) Characterizing task usage shapes in google’s compute clusters. In: Proceedings of the LSDSM Workshop, pp 1–6
117.
Metadaten
Titel
Selecting services in the cloud: a decision support methodology focused on infrastructure-as-a-service context
verfasst von
Cássio L. M. Belusso
Sandro Sawicki
Vitor Basto-Fernandes
Rafael Z. Frantz
Fabricia Roos-Frantz
Publikationsdatum
05.01.2022
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 6/2022
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-021-04248-8

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