Adaptive Nature-Inspired Fog Architecture

2018 IEEE 2nd International Conference on Fog and Edge Computing, ICFEC 2018 - In conjunction with 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, IEEE/ACM CCGrid 2018

Volumn , Issue , 2018, Pages 1-8

  Kimovski, Dragi ^a   Ijaz, Humaira ^b   Saurabh, Nishant ^b   Prodan, Radu ^a  

^a UNIVERSITY OF KLAGENFURT   (Austria)

^b UNIVERSITY OF INNSBRUCK   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER COMPUTING; DECISION MAKING; DECISION THEORY; DIGITAL STORAGE; DISPERSIONS; EDGE COMPUTING; FOG; GRAPH THEORY; GRID COMPUTING; INTERNET OF THINGS; LEARNING SYSTEMS; NETWORK ARCHITECTURE;

ADAPTIVE RESOURCE MANAGEMENT; ARCHITECTURAL APPROACH; CLOUD INFRASTRUCTURES; COMPUTING RESOURCE; INTELLIGENT PROCESSING; INTERNET OF THINGS (IOT); MULTI CRITERIA DECISION MAKING; STORAGE RESOURCES;

FOG COMPUTING;

EID: 85048066317     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CFEC.2018.8358723     Document Type: Conference Paper

References (25)

1. Mahadev Satyanarayanan. The emergence of edge computing. Computer, 50(1):30-39, 2017.
2. Amir Vahid Dastjerdi, Harshit Gupta, Rodrigo N Calheiros, Soumya K Ghosh, and Rajkumar Buyya. Fog computing: Principles, architectures, and applications. arXiv preprint arXiv:1601. 02752, 2016.
3. Prateeksha Varshney and Yogesh Simmhan. Demystifying fog computing: Characterizing architectures, applications and abstractions. arXiv preprint arXiv:1702. 06331, 2017.
4. Zhenyu Wen, Renyu Yang, Peter Garraghan, Tao Lin, Jie Xu, and Michael Rovatsos. Fog orchestration for internet of things services. IEEE Internet Computing, 21(2):16-24, 2017.
5. Luis M Vaquero and Luis Rodero-Merino. Finding your way in the fog: Towards a comprehensive definition of fog computing. ACM SIGCOMM Computer Communication Review, 44(5):27-32, 2014.
6. Flavio Bonomi, Rodolfo Milito, Preethi Natarajan, and Jiang Zhu. Fog computing: A platform for internet of things and analytics. In Big Data and Internet of Things: A Roadmap for Smart Environments, pages 169-186. Springer, 2014.
7. Dragi Kimovski, Julio Ortega, Andrés Ortiz, and Raúl Baños. Parallel alternatives for evolutionary multi-objective optimization in unsupervised feature selection. Expert Systems with Applications, 42(9):4239-4252, 2015.
8. Ulrik Brandes, Stephen P Borgatti, and Linton C Freeman. Maintaining the duality of closeness and betweenness centrality. Social Networks, 44:153-159, 2016.
9. Ulrike Von Luxburg. A tutorial on spectral clustering. Statistics and computing, 17(4):395-416, 2007.
10. Massimo Villari, Maria Fazio, Schahram Dustdar, Omer Rana, and Rajiv Ranjan. Osmotic computing: A new paradigm for edge/cloud integration. IEEE Cloud Computing, 3(6):76-83, 2016.
11. Mathias Santos de Brito, Saiful Hoque, Thomas Magedanz, Ronald Steinke, Alexander Willner, Daniel Nehls, Oliver Keils, and Florian Schreiner. A service orchestration architecture for fog-enabled infrastructures. In Fog and Mobile Edge Computing (FMEC), 2017 Second International Conference on, pages 127-132. IEEE, 2017.
12. Mohammad Aazam and Eui-Nam Huh. Dynamic resource provisioning through fog micro datacenter. In Pervasive Computing and Communication Workshops (PerCom Workshops), 2015 IEEE International Conference on, pages 105-110. IEEE, 2015.
13. Olena Skarlat, Stefan Schulte, Michael Borkowski, and Philipp Leitner. Resource provisioning for iot services in the fog. In Service-Oriented Computing and Applications (SOCA), 2016 IEEE 9th International Conference on, pages 32-39. IEEE, 2016.
14. Jürgen Branke, Kalyanmoy Deb, and Kaisa Miettinen. Multiobjective optimization: Interactive and evolutionary approaches, volume 5252. Springer Science & Business Media, 2008.
15. Dragi Kimovski, Nishant Saurabh, Sandi Gec, Vlado Stankovski, and Radu Prodan. Multi-objective optimization framework for vmi distribution in federated cloud repositories. In European Conference on Parallel Processing, pages 236-247. Springer, 2016.
16. Marc Barthelemy. Betweenness centrality in large complex networks. The European Physical Journal B-Condensed Matter and Complex Systems, 38(2):163-168, 2004.
17. Rui Xu and Donald Wunsch. Survey of clustering algorithms. IEEE Transactions on neural networks, 16(3):645-678, 2005.
18. Alan Edelman. Eigenvalues and condition numbers of random matrices. SIAM Journal on Matrix Analysis and Applications, 9(4):543-560, 1988.
19. Tapas Kanungo, David M Mount, Nathan S Netanyahu, Christine D Piatko, Ruth Silverman, and Angela Y Wu. An efficient k-means clustering algorithm: Analysis and implementation. IEEE transactions on pattern analysis and machine intelligence, 24(7):881-892, 2002.
20. Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, and Tanaka Meyarivan. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: Nsga-ii. In International Conference on Parallel Problem Solving From Nature, pages 849-858. Springer, 2000.
21. Harshit Gupta, Amir Vahid Dastjerdi, Soumya K Ghosh, and Rajkumar Buyya. ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience, 47(9):1275-1296, 2017.
22. Juan J Durillo and Antonio J Nebro. jmetal: A java framework for multiobjective optimization. Advances in Engineering Software, 42(10):760-771, 2011.
23. Roland Matha Dragi Kimovski, Sasko Ristov and Radu Prodan. Multiobjective service oriented network provisioning in ultra-scale systems. In EUROPAR Workshops 2017. Springer, 2017.
24. Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H Witten. The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10-18, 2009.
25. Andrew Y Ng, Michael I Jordan, and Yair Weiss. On spectral clustering: Analysis and an algorithm. In Advances in neural information processing systems, pages 849-856, 2002.