Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Kongresse
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Veranstaltungskalender Awards MyNewsletter Firmensuche
Kunden Login
Über Einzelzugang informieren
Über Unternehmenszugang informieren
Referenzkunden
Elektrische Antriebe und Energiesysteme 2025 | 26 + 27 März 2025

19. Internationaler MTZ-Fachkongress Zukunftsantriebe

Seien Sie bei der Konferenz dabei! Dort treffen Experten aus der Automobilindustrie, Energieerzeugung und Stromnetzbetrieb auf Vertreter aus Politik und Wissenschaft. Diskutiert werden wichtige Themen der Branche.
Erweiterte Suche
Anmelden
nach oben
The Journal of Supercomputing
Erschienen in:

02.05.2024

Service placement in fog–cloud computing environments: a comprehensive literature review

verfasst von: Fatemeh Sarkohaki, Mohsen Sharifi

Erschienen in: The Journal of Supercomputing | Ausgabe 12/2024

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

With the rapid expansion of the Internet of Things and the surge in the volume of data exchanged in it, cloud computing became more significant. To face the challenges of the cloud, the idea of fog computing was formed. The heterogeneity of nodes, distribution, and limitation of their resources in fog computing in turn led to the formation of the service placement problem. In service placement, we are looking for the mapping of the requested services to the available nodes so that a set of Quality-of-Service objectives are satisfied. Since the problem is NP-hard, various methods have been proposed to solve it, each of which has its advantages and shortcomings. In this survey paper, while reviewing the most prominent state-of-the-art service placement methods by presenting a taxonomy based on their optimization strategy, the advantages, disadvantages, and applications of each category of methods are discussed. Consequently, recommendations for future works are presented.
Vorheriger Artikel DDSC-SMOTE: an imbalanced data oversampling algorithm based on data distribution and spectral clustering
Nächster Artikel A novel deep learning model-based optimization algorithm for text message spam detection

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Fußnoten
1
Mobile Edge Computing.
 
Literatur
1.
Qays MO et al (2023) Key communication technologies, applications, protocols and future guides for IoT-assisted smart grid systems: a review. Energy Rep 9:2440–2452
2.
Moudgil V et al (2023) Integration of IoT in building energy infrastructure: a critical review on challenges and solutions. Renew Sustain Energy Rev 174:113121
3.
Sharma VK et al (2022) An optimization-based machine learning technique for smart home security using 5G. Comput Electr Eng 104:108434
4.
Philip SJ, Luu TJ, Carte T (2023) There’s No place like home: Understanding users’ intentions toward securing internet-of-things (IoT) smart home networks. Comput Hum Behav 139:107551
5.
Khanpara P et al (2023) A context-aware internet of things-driven security scheme for smart homes. Secur Priv 6(1):e269
6.
Zaminkar M, Sarkohaki F, Fotohi R (2021) A method based on encryption and node rating for securing the RPL protocol communications in the IoT ecosystem. Int J Commun Syst 34(3):e4693
7.
Salehi-Amiri A et al (2022) Designing an effective two-stage, sustainable, and IoT based waste management system. Renew Sustain Energy Rev 157:112031
8.
Salman MY, Hasar H (2023) Review on environmental aspects in smart city concept: water, waste, air pollution and transportation smart applications using IoT techniques. Sustain Cities Soc 94:104567
9.
Hashemi-Amiri O et al (2023) An allocation-routing optimization model for integrated solid waste management. Exp Syst Appl 227:120364
10.
Sridhar K et al (2023) A modular IOT sensing platform using hybrid learning ability for air quality prediction. Meas Sens 25:100609
11.
Barthwal A (2023) A Markov chain–based IoT system for monitoring and analysis of urban air quality. Environ Monit Assess 195(1):235MathSciNet
12.
13.
Kumar P et al (2023) A blockchain-orchestrated deep learning approach for secure data transmission in IoT-enabled healthcare system. J Parallel Distrib Comput 172:69–83
14.
Krishnamoorthy S, Dua A, Gupta S (2023) Role of emerging technologies in future IoT-driven healthcare 4.0 technologies: a survey, current challenges and future directions. J Ambient Intell Humaniz Comput 14(1):361–407
15.
Rejeb A et al (2023) The Internet of Things (IoT) in healthcare: Taking stock and moving forward. Internet of Things 22:100721
16.
17.
Cheikhrouhou O et al (2023) A lightweight blockchain and fog-enabled secure remote patient monitoring system. Internet of Things 22:100691
18.
Khan AA et al (2023) The collaborative role of blockchain, artificial intelligence, and industrial internet of things in digitalization of small and medium-size enterprises. Sci Rep 13(1):1656MathSciNet
19.
Rahman A et al (2023) Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT. Digit Commun Netw 9(2):411–421
20.
Huang J et al (2023) AoI-aware energy control and computation offloading for industrial IoT. Futur Gener Comput Syst 139:29–37
21.
Karakaya A, Ulu A, Akleylek S (2022) GOALALERT: a novel real-time technical team alert approach using machine learning on an IoT-based system in sports. Microprocess Microsyst 93:104606
22.
Liu L (2021) Construction of youth public sports service system based on embedded system and wireless IoT. Microprocess Microsyst 83:103984
23.
Prajapati D et al (2022) Blockchain and IoT embedded sustainable virtual closed-loop supply chain in E-commerce towards the circular economy. Comput Ind Eng 172:108530
24.
Kulkarni PM et al (2022) IOT data fusion framework for e-commerce. Meas Sens 24:100507
25.
Boursianis AD et al (2022) Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: a comprehensive review. Internet of Things 18:100187
26.
Zeng H et al (2023) An IoT and Blockchain-based approach for the smart water management system in agriculture. Expert Syst 40(4):e12892
27.
McCaig M, Rezania D, Dara R (2023) Framing the response to IoT in agriculture: a discourse analysis. Agric Syst 204:103557
28.
Krishankumar R, Ecer F (2023) Selection of IoT service provider for sustainable transport using q-rung orthopair fuzzy CRADIS and unknown weights. Appl Soft Comput 132:109870
29.
Jiang H et al (2023) RETRACTED ARTICLE: creating a ubiquitous learning environment using IoT in transportation. Soft Comput 27(2):1213–1213
30.
Wu X et al (2023) A digital decision approach for scheduling process planning of shared bikes under internet of things environment. Appl Soft Comput 133:109934
31.
Kuo Y-H, Leung JM, Yan Y (2023) Public transport for smart cities: recent innovations and future challenges. Eur J Oper Res 306(3):1001–1026MathSciNet
32.
Quy VK et al (2022) Smart healthcare IoT applications based on fog computing: architecture, applications and challenges. Complex Intell Syst 8(5):3805–3815MathSciNet
33.
Peixoto M et al (2023) FogJam: a fog service for detecting traffic congestion in a continuous data stream VANET. Ad Hoc Netw 140:103046
34.
Tavousi F, Azizi S, Ghaderzadeh A (2022) A fuzzy approach for optimal placement of IoT applications in fog–cloud computing. Clust Comput 25:1–18
35.
Sabuj SR et al (2022) Delay optimization in mobile edge computing: cognitive UAV-assisted eMBB and mMTC services. IEEE Trans Cognit Commun Netw 8(2):1019–1033
36.
37.
He Y et al (2022) Trajectory optimization and channel allocation for delay sensitive secure transmission in UAV-relayed VANETs. IEEE Trans Veh Technol 71(4):4512–4517
38.
39.
40.
Bonomi, F., et al. Fog computing and its role in the internet of things. in Proceedings of the first edition of the MCC workshop on Mobile cloud computing. 2012.
41.
Kumar D, Annam S (2022) Fog Computing Applications with Decentralized Computing Infrastructure—Systematic Review. in PROCEEDINGS OF THE SEVENTH INTERNATIONAL CONFERENCE ON MATHEMATICS AND COMPUTING: ICMC 2021. 2022. Springer.
42.
Songhorabadi M et al (2023) Fog computing approaches in IoT-enabled smart cities. J Netw Comput Appl 211:103557
43.
Sethi V, Pal S (2023) FedDOVe: a federated deep Q-learning-based offloading for vehicular fog computing. Futur Gener Comput Syst 141:96–105
44.
Hazra A et al (2023) Fog computing for next-generation internet of things: fundamental, state-of-the-art and research challenges. Comput Sci Rev 48:100549
45.
Singh S, Vidyarthi D (2023) An integrated approach of ml-metaheuristics for secure service placement in fog–cloud ecosystem. Internet of Things 22:100817
46.
Singh S, Vidyarthi D (2022) QoS-Aware Service Placement for Fog Integrated Cloud Using Modified Neuro-Fuzzy Approach. in Soft Computing and Its Engineering Applications: 4th INTERNATIONAL CONFERENCE, icSoftComp 2022, Changa, Anand, India, December 9–10, 2022, Proceedings. 2023. Springer.
47.
Teng M et al. (2020) Priority based service placement strategy in heterogeneous mobile edge computing. in Algorithms and Architectures for Parallel Processing: 20th INTERNATIONAL CONFERENCE, ICA3PP 2020, New York City, NY, USA, October 2–4, 2020, Proceedings, Part I 20. 2020. Springer.
48.
Zare M, Sola YE, Hasanpour H (2023) Towards distributed and autonomous IoT service placement in fog computing using asynchronous advantage actor-critic algorithm. J King Saud Univ Comput Inf Sci 35(1):368–381
49.
Das R, Inuwa MM (2023) A review on fog computing: issues, characteristics, challenges, and potential applications. Telematics and Informatics Reports, p. 100049.
50.
Salaht FA, Desprez F, Lebre A (2020) An overview of service placement problem in fog and edge computing. ACM Comput Surv (CSUR) 53(3):1–35
51.
Matoušek J, Gärtner B (2007) Understanding and using linear programming. Springer, Berlin
52.
Kuhn HW, Tucker AW (2013) Nonlinear programming. Traces and emergence of nonlinear programming. Springer, Berlin, pp 247–258
53.
Vielma JP (2015) Mixed integer linear programming formulation techniques. SIAM Rev 57(1):3–57MathSciNet
54.
Velasquez K et al (2017) Service placement for latency reduction in the internet of things. Ann Telecommun 72:105–115
55.
Tinini RI et al. (2017) Optimal placement of virtualized BBU processing in hybrid cloud-fog RAN over TWDM-PON. in GLOBECOM 2017–2017 IEEE GLOBAL COMMUNICATIONS CONFERENCE. IEEE.
56.
Gong Y (2020) Optimal edge server and service placement in mobile edge computing. in 2020 IEEE 9th JOINT INTERNATIONAL INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE CONFERENCE (ITAIC). IEEE.
57.
Kim W-S, Chung S-H (2018) User-participatory fog computing architecture and its management schemes for improving feasibility. IEEE Access 6:20262–20278
58.
Yala L, Frangoudis PA, Ksentini A (2018) Latency and availability driven VNF placement in a MEC-NFV environment. in 2018 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM). IEEE.
59.
Daneshfar N et al. (2018) Service allocation in a mobile fog infrastructure under availability and qos constraints. in 2018 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM). IEEE.
60.
Donassolo B, et al. (2019) Fog based framework for IoT service provisioning. in 2019 16th IEEE ANNUAL CONSUMER COMMUNICATIONS & NETWORKING CONFERENCE (CCNC). IEEE.
61.
Chen M et al (2013) Markov approximation for combinatorial network optimization. IEEE Trans Inf Theory 59(10):6301–6327MathSciNet
62.
Yu R, Xue G, Zhang X (2018) Application provisioning in fog computing-enabled internet-of-things: A network perspective. in IEEE INFOCOM 2018-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS. IEEE.
63.
Ouyang T, Zhou Z, Chen X (2018) Follow me at the edge: mobility-aware dynamic service placement for mobile edge computing. IEEE J Sel Areas Commun 36(10):2333–2345
64.
Ning Z et al (2020) Distributed and dynamic service placement in pervasive edge computing networks. IEEE Trans Parallel Distrib Syst 32(6):1277–1292
65.
Jokar E, Mosleh M, Kheyrandish M (2022) Discovering community structure in social networks based on the synergy of label propagation and simulated annealing. Multimed Tools Appl 81(15):21449–21470
66.
Mirjalili S, Mirjalili S (2019) Genetic algorithm. Evolutionary Algorithms and Neural Networks: Theory and Applications, p. 43–55.
67.
Wang D, Tan D, Liu L (2018) Particle swarm optimization algorithm: an overview. Soft Comput 22:387–408
68.
Blum C (2005) Ant colony optimization: Introduction and recent trends. Phys Life Rev 2(4):353–373
69.
Jokar E, Mosleh M, Kheyrandish M (2022) GWBM: an algorithm based on grey wolf optimization and balanced modularity for community discovery in social networks. J Supercomput 78(5):7354–7377
70.
Hoseiny F et al. (2021) PGA: a priority-aware genetic algorithm for task scheduling in heterogeneous fog–cloud computing. in IEEE INFOCOM 2021-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS). IEEE.
71.
Sarrafzade N, Entezari-Maleki R, Sousa L (2022) A genetic-based approach for service placement in fog computing. J Supercomput 78(8):10854–10875
72.
Maia AM et al. (2020) Dynamic service placement and load distribution in edge computing. in 2020 16TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM). IEEE.
73.
Khosroabadi F, Fotouhi-Ghazvini F, Fotouhi H (2021) Scatter: service placement in real-time fog-assisted iot networks. J Sens Actuator Netw 10(2):26
74.
Eyckerman R et al (2020) Requirements for distributed task placement in the fog. Internet of Things 12:100237
75.
Souza VB et al (2018) Towards a proper service placement in combined Fog-to-Cloud (F2C) architectures. Futur Gener Comput Syst 87:1–15
76.
Apat HK et al. (2021) A Nature-Inspired-Based Multi-objective Service Placement in Fog Computing Environment, in Intelligent Systems: Proceedings of ICMIB 2020. Springer. p. 293–304.
77.
Ma R (2021) Edge server placement for service offloading in internet of things. Secur Commun Netw 2021:1–16
78.
Hu Y et al (2022) An energy-aware service placement strategy using hybrid meta-heuristic algorithm in iot environments. Clust Comput 26:1–7
79.
Natesha B, Guddeti RMR (2021) Adopting elitism-based Genetic Algorithm for minimizing multi-objective problems of IoT service placement in fog computing environment. J Netw Comput Appl 178:102972
80.
Natesha B, Guddeti RMR (2022) Meta-heuristic based hybrid service placement strategies for two-level fog computing architecture. J Netw Syst Manage 30(3):47
81.
Guerrero C, Lera I, Juiz C (2019) Evaluation and efficiency comparison of evolutionary algorithms for service placement optimization in fog architectures. Futur Gener Comput Syst 97:131–144
82.
Shahryari O-K et al (2021) Energy and task completion time trade-off for task offloading in fog-enabled IoT networks. Pervasive Mob Comput 74:101395
83.
Apat HK et al (2024) A hybrid meta-heuristic algorithm for multi-objective IoT service placement in fog computing environments. Decis Anal J 10:100379
84.
Azizi S et al (2024) DCSP: a delay and cost-aware service placement and load distribution algorithm for IoT-based fog networks. Comput Commun 215:9–20
85.
Jordan MI, Mitchell TM (2015) Machine learning: Trends, perspectives, and prospects. Science 349(6245):255–260MathSciNet
86.
Jokar E, Mosleh M, Kheyrandish M (2022) Overlapping community detection in complex networks using fuzzy theory, balanced link density, and label propagation. Expert Syst 39(5):e12921
87.
Quadri C, Ceselli A, Rossi GP (2023) Multi-user edge service orchestration based on deep reinforcement learning. Comput Commun 203:30–47
88.
89.
90.
Zhan W et al (2020) Deep-reinforcement-learning-based offloading scheduling for vehicular edge computing. IEEE Internet Things J 7(6):5449–5465
91.
Talpur A, Gurusamy M (2021) DRLD-SP: a deep-reinforcement-learning-based dynamic service placement in edge-enabled internet of vehicles. IEEE Internet Things J 9(8):6239–6251
92.
93.
Ibn-Khedher H et al (2022) Next-generation edge computing assisted autonomous driving based artificial intelligence algorithms. IEEE Access 10:53987–54001
94.
Zhou Z et al (2019) Reliable task offloading for vehicular fog computing under information asymmetry and information uncertainty. IEEE Trans Veh Technol 68(9):8322–8335
95.
Nsouli A, El-Hajj W, Mourad A (2023) Reinforcement learning based scheme for on-demand vehicular fog formation. Veh Commun 40:100571
96.
97.
Sharma A, Thangaraj V (2024) Intelligent service placement algorithm based on DDQN and prioritized experience replay in IoT-Fog computing environment. Internet of Things 25:101112
98.
Tian Z et al (2019) Evaluating reputation management schemes of internet of vehicles based on evolutionary game theory. IEEE Trans Veh Technol 68(6):5971–5980
99.
100.
Chen Y et al (2022) Qoe-aware decentralized task offloading and resource allocation for end-edge-cloud systems: a game-theoretical approach. IEEE Trans Mob Comput 23(1):769–784
101.
Kayal P, Liebeherr J (2019) Distributed service placement in fog computing: An iterative combinatorial auction approach. in 2019 IEEE 39th INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS). IEEE.
102.
Sharma A, Thangaraj V (2022) DMAP: a decentralized matching game theory based optimized internet of things application placement in fog computing environment. Concurr Comput Pract Exp 34(23):e7189
103.
Shi D et al (2020) Mean field game guided deep reinforcement learning for task placement in cooperative multiaccess edge computing. IEEE Internet Things J 7(10):9330–9340
104.
Aloqaily MB, Kantarci, Mouftah HT (2017) Fairness-aware game theoretic approach for service management in vehicular clouds. in 2017 IEEE 86th VEHICULAR TECHNOLOGY CONFERENCE (VTC-Fall). IEEE.
105.
Zafari F et al (2020) Let’s share: a game-theoretic framework for resource sharing in mobile edge clouds. IEEE Trans Netw Serv Manage 18(2):2107–2122
106.
Xiao Z et al (2019) Vehicular task offloading via heat-aware MEC cooperation using game-theoretic method. IEEE Internet Things J 7(3):2038–2052
107.
Shabir B et al (2022) On collective intellect for task offloading in vehicular fog paradigm. IEEE Access 10:101445–101457
108.
Krogh A (2008) What are artificial neural networks? Nat Biotechnol 26(2):195–197
109.
Wu Z et al (2020) A comprehensive survey on graph neural networks. IEEE trans Neural Netw Learn Syst 32(1):4–24MathSciNet
110.
Li Y, Liang S, Jiang Y (2023) Path reliability-based graph attention networks. Neural Netw 159:153–160
111.
Veličković P (2023) Everything is connected: Graph neural networks. Curr Opin Struct Biol 79:102538
112.
Zhong X and He Y (2021) A Cybertwin-Driven Task Offloading Scheme Based on Deep Reinforcement Learning and Graph Attention Networks. in 2021 13th INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS AND SIGNAL PROCESSING (WCSP). IEEE.
113.
Wu T et al. (2021) A Scalable Computation Offloading Scheme for MEC Based on Graph Neural Networks. in 2021 IEEE Globecom Workshops (GC Wkshps). IEEE.
114.
Eyckerman R et al. (2022) Application placement in fog environments using multi-objective reinforcement learning with maximum reward formulation. in NOMS 2022–2022 IEEE/IFIP network operations and management symposium. IEEE.
115.
Zhang J et al. (2022) Fine-grained service offloading in B5G/6G collaborative edge computing based on graph neural networks. in ICC 2022-IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS. IEEE.
116.
117.
Tang Z et al. (2020) Dependent task offloading for multiple jobs in edge computing. in 2020 29th INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATIONS AND NETWORKS (ICCCN). IEEE.
118.
Sun Z, Mo Y, Yu C (2021) Graph reinforcement learning based task offloading for multi-access edge computing. IEEE Internet Things J 10(4):3138–3150
119.
Liu B (2024) Hybrid fuzzy neural network for joint task offloading in the internet of vehicles. J Grid Comput 22(1):10
120.
121.
Sarkar I et al (2021) Dynamic task placement for deadline-aware IoT applications in federated fog networks. IEEE Internet Things J 9(2):1469–1478
122.
Ayoubi M, Ramezanpour M, Khorsand R (2021) An autonomous IoT service placement methodology in fog computing. Softw Pract Exp 51(5):1097–1120
123.
Cao T et al (2024) Walking on two legs: joint service placement and computation configuration for provisioning containerized services at edges. Comput Netw 239:110144
Metadaten
Titel
Service placement in fog–cloud computing environments: a comprehensive literature review
verfasst von
Fatemeh Sarkohaki
Mohsen Sharifi
Publikationsdatum
02.05.2024
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 12/2024
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-024-06151-4

Weitere Artikel der Ausgabe 12/2024

HyperTuner: a cross-layer multi-objective hyperparameter auto-tuning framework for data analytic services

On the computation of the gradient in implicit neural networks

  • Open Access

The Egyptian national HPC grid (EN-HPCG): open-source Slurm implementation from cluster to grid approach

  • Open Access

Correction to: A heuristic hybrid instance reduction approach based on adaptive relative distance and k-means clustering

  • Correction

Enhancing Grover’s search algorithm: a modified approach to increase the probability of good states

A computation offloading strategy for multi-access edge computing based on DQUIC protocol

Premium Partner

    Bildnachweise