Fog Computing Realization for Big Data Analytics
This chapter provides background on big data analytics and describes how fog‐engine (FE) can be deployed in the traditional centralized data analytics platform and how it enhances existing system capabilities. The FE provides on‐premise data analytics as well as the capabilitie...
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| Veröffentlicht in: | Fog and Edge Computing S. 259 - 290 |
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| Hauptverfasser: | , , , |
| Format: | Buchkapitel |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
Wiley
2019
John Wiley & Sons, Incorporated John Wiley & Sons, Inc |
| Ausgabe: | 1 |
| Schriftenreihe: | Wiley Series on Parallel and Distributed Computing |
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| ISBN: | 9781119524984, 1119524989 |
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| Abstract | This chapter provides background on big data analytics and describes how fog‐engine (FE) can be deployed in the traditional centralized data analytics platform and how it enhances existing system capabilities. The FE provides on‐premise data analytics as well as the capabilities for Internet‐of‐Things (IoT) devices to communicate with each other and with the cloud. The chapter provides an overview of a typical FE deployment. It explains the system prototype and the results of the evaluation of the proposed solution. Two case studies describing how the proposed idea works for different applications are described. A table shows the list of IoT solutions from five well‐known cloud providers. Data collection is one of the basic aspects of these solutions, which specify the communication protocols between the components of an IoT software platform. Researchers have proposed various applications of fog computing in diverse scenarios such as health monitoring, smart cities, and vehicular networks. |
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| AbstractList | This chapter provides background on big data analytics and describes how fog‐engine (FE) can be deployed in the traditional centralized data analytics platform and how it enhances existing system capabilities. The FE provides on‐premise data analytics as well as the capabilities for Internet‐of‐Things (IoT) devices to communicate with each other and with the cloud. The chapter provides an overview of a typical FE deployment. It explains the system prototype and the results of the evaluation of the proposed solution. Two case studies describing how the proposed idea works for different applications are described. A table shows the list of IoT solutions from five well‐known cloud providers. Data collection is one of the basic aspects of these solutions, which specify the communication protocols between the components of an IoT software platform. Researchers have proposed various applications of fog computing in diverse scenarios such as health monitoring, smart cities, and vehicular networks. This chapter provides background on big data analytics and describes how fog‐engine (FE) can be deployed in the traditional centralized data analytics platform and how it enhances existing system capabilities. The FE provides on‐premise data analytics as well as the capabilities for Internet‐of‐Things (IoT) devices to communicate with each other and with the cloud. The chapter provides an overview of a typical FE deployment. It explains the system prototype and the results of the evaluation of the proposed solution. Two case studies describing how the proposed idea works for different applications are described. A table shows the list of IoT solutions from five well‐known cloud providers. Data collection is one of the basic aspects of these solutions, which specify the communication protocols between the components of an IoT software platform. Researchers have proposed various applications of fog computing in diverse scenarios such as health monitoring, smart cities, and vehicular networks. |
| Author | Ramirez‐Prado, Guillermo Javadi, Bahman Mahanti, Aniket Mehdipour, Farhad |
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| Editor | Buyya, Rajkumar Srirama, Satish Narayana |
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| Keywords | Big Data Cloud computing Real-time systems Engines Organizations Edge computing |
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| References | Mukherjee, Matam, Shu, Maglaras, Ferrag, Choudhury, Kumar (c11-cit-0020) 2017; 5 Yousefpour, Ishigaki, Jue (c11-cit-0029) Yi, Li, Li (c11-cit-0006) 2015 Dubey, Yang, Constant, Amiri, Yang, Makodiya (c11-cit-0021) 2015 (c11-cit-0013) 2013 Guibert, Wu, He, Wang, Li (c11-cit-0037) 2017 Tang, Chen, Hafferman, Wei, He, Yang (c11-cit-0009) 2015 Manzalini (c11-cit-0019) 2013 Shi, Cao, Zhang, Li, Xu (c11-cit-0003) 2016; 3 Bonomi, Milito, Zhu, Addepalli (c11-cit-0018) 2012 Bonomi, Milito, Zhu, Addepalli (c11-cit-0011) 2012 Sood, Mahajan (c11-cit-0035) 2017 Alturki, Reiff‐Marganiec, Perera (c11-cit-0030) 2017 Chang, Srirama, Buyya (c11-cit-0033) 2017; 50 Varghese, Wang, Nikolopoulos, Buyya (c11-cit-0039) 2017 Yi, Li, Li (c11-cit-0005) 2015 Saecker, Markl (c11-cit-0015) 2013; 138 Vaquero, Rodero‐Merino (c11-cit-0004) 2014; 44 Cao, Hou, Brown, Wang, Chen (c11-cit-0034) 2015 Vora, Tanwar, Tyagi, Kumar, Rodrigues (c11-cit-0036) 2017; 6 Satyanarayanan, Simoens, Xiao, Pillai, Chen, Ha, Hu, Amos (c11-cit-0002) 2015; 14 Di‐Martino, Aversa, Cretella, Esposito (c11-cit-0014) 2014; 1 Woolsey (c11-cit-0025) Desirena Lopez, Siller, Huerta (c11-cit-0023) 2017 Liu, Fieldsend, Min (c11-cit-0032) 2017; 5 Coyne, Hajas, Hallback, Lindström, Vollmar (c11-cit-0027) 2016 Dastjerdi, Buyya (c11-cit-0016) 2016; 49 Syed, Fernandez, Ilyas (c11-cit-0028) 2016 Jiang, Huang, Tsang (c11-cit-0031) 2017 Mehdipour, Noori, Javadi (c11-cit-0007) 2016; 100 Craciunescu, Mihovska, Mihaylov, Kyriazakos, Prasad, Halunga (c11-cit-0038) 2015 Barik, Dubey, Samaddar, Gupta, Ray (c11-cit-0010) 2016 Javadi, Zhang, Taufer (c11-cit-0008) 2014 Mehdipour, Javadi, Mahanti (c11-cit-0022) 2016 Javadi, Calheiros, Matawie, Ginige, Cook (c11-cit-0024) (c11-cit-0026) 2015 Rahm, Do (c11-cit-0012) 2000; 23 Dastjerdi, Gupta, Calheiros, Ghosh, Buyya (c11-cit-0001) 2016 Schatsky (c11-cit-0017) 2016 |
| References_xml | – volume: 5 start-page: 19293 year: 2017 end-page: 19304 ident: c11-cit-0020 article-title: Security and privacy in fog computing: Challenges publication-title: IEEE Access – start-page: 640 year: 2016 end-page: 646 ident: c11-cit-0022 article-title: FOG‐engine: Towards big data analytics in the fog publication-title: Dependable, Autonomic and Secure Computing, 14th International Conference on Pervasive Intelligence and Computing – year: 2017 ident: c11-cit-0023 article-title: IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI) – year: 2015 ident: c11-cit-0026 – year: 2015 ident: c11-cit-0009 article-title: A hierarchical distributed fog computing architecture for big data analysis in smart cities publication-title: ASE BD&SI '15 Proceedings of the ASE BigData and Social Informatics – ident: c11-cit-0024 article-title: Smart nutrition monitoring system using heterogeneous Internet of Things platform – start-page: 21 year: 2014 end-page: 27 ident: c11-cit-0008 article-title: Bandwidth modeling in large distributed systems for big data applications publication-title: 15th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) – year: 2012 ident: c11-cit-0018 article-title: Fog computing and its role in the Internet of Things publication-title: MCC, Finland – year: 2016 ident: c11-cit-0017 article-title: Machine learning is going mobile – volume: 6 year: 2017 ident: c11-cit-0036 article-title: FAAL: Fog computing‐based patient monitoring system for ambient assisted living publication-title: IEEE 19th International Conference on e‐Health Networking, Applications and Services (Healthcom) – start-page: 1 year: 2017 end-page: 5 ident: c11-cit-0037 article-title: CC‐fog: Toward content‐centric fog networks for E‐health publication-title: IEEE 19th International Conference on e‐Health Networking, Applications and Services (Healthcom) – start-page: 1 issue: 99 year: 2017 end-page: 8 ident: c11-cit-0031 article-title: Challenges and Solutions in Fog Computing Orchestration publication-title: IEEE Network – start-page: 613 year: 2016 end-page: 618 ident: c11-cit-0010 article-title: FogGIS: Fog Computing for geospatial big data analytics publication-title: IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON) – ident: c11-cit-0025 article-title: Powering the Next Generation Cloud with Azure Stack – year: 2013 ident: c11-cit-0019 article-title: A foggy edge, beyond the clouds publication-title: Business Ecosystems – volume: 49 start-page: 112 issue: 8 year: 2016 end-page: 116 ident: c11-cit-0016 article-title: Fog computing: Helping the Internet of Things realize its potential publication-title: Computer – year: 2016 ident: c11-cit-0028 article-title: A Pattern for Fog Computing publication-title: Proceedings of the 10th Travelling Conference on Pattern Languages of Programs – start-page: 459 year: 2015 end-page: 463 ident: c11-cit-0038 article-title: Implementation of fog computing for reliable E‐health applications publication-title: 49th Asilomar Conference on Signals, Systems and Computers – volume: 1 start-page: 3 issue: 1/2 year: 2014 end-page: 17 ident: c11-cit-0014 article-title: Big data (lost) in the cloud publication-title: Int. J. Big Data Intelligence – volume: 100 start-page: 59 year: 2016 end-page: 101 ident: c11-cit-0007 article-title: Energy‐efficient big data analytics in datacenters publication-title: Advances in Computers – start-page: 13 year: 2012 end-page: 16 ident: c11-cit-0011 article-title: Fog computing and its role in the Internet of Things publication-title: Proceedings of the first edition of the MCC workshop on mobile cloud computing – year: 2017 ident: c11-cit-0030 article-title: A hybrid approach for data analytics for internet of things publication-title: Proceedings of the Seventh International Conference on the Internet of Things – volume: 44 start-page: 27 issue: 5 year: 2014 end-page: 32 ident: c11-cit-0004 article-title: Finding your way in the fog: Towards a comprehensive definition of fog computing publication-title: SIGCOMM Comput. Commun. 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| Snippet | This chapter provides background on big data analytics and describes how fog‐engine (FE) can be deployed in the traditional centralized data analytics... This chapter provides background on big data analytics and describes how fog‐engine (FE) can be deployed in the traditional centralized data analytics platform... |
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| SubjectTerms | big data analytics centralized data analytics platform fog computing fog‐engine Internet‐of‐Things devices on‐premise data analytics |
| Title | Fog Computing Realization for Big Data Analytics |
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