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Integrated Resource Allocation Model for Cloud and Fog Computing: Toward Energy-Efficient Infrastructure as a Service (IaaS)

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Book cover Advances on Computational Intelligence in Energy

Part of the book series: Green Energy and Technology ((GREEN))

Abstract

Cloud is transmigrating to network edge where they are seen as virtualized resources called “Fog Computing” that expand the idea of Cloud Computing perspective to the network edge. This chapter proposes an integrated resource allocation model for energy-efficient Infrastructure as a Service (IaaS) that extends from the network edge of the Fog to the Cloud datacenter. We first developed a new architecture and introduced a policy on the Fog end where a decision will be made to either process the user request on the Fog or it will be moved to the Cloud datacenter. We developed a decision model on top of the architecture. The decision model takes into consideration of the resource constraints of CPU, Memory, and Storage. Using this will improve resource utilization as well as the reduction in energy consumption by a datacenter. Finally, we addressed future research direction considering the model components and its performance.

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References

  1. Foster I et al (2008) Cloud computing and grid computing 360-degree compared. In: IEEE 2008 grid computing environments workshop, Austin, TX, pp 1–10

    Google Scholar 

  2. Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst 28(5):755–768

    Article  Google Scholar 

  3. Usman M et al (2016) A conceptual framework for realizing energy efficient resource allocation in cloud data centre. Indian J Sci Technol 9(46):1–8

    Google Scholar 

  4. Madni SHH, Latiff MSA, Coulibaly Y (2016) An appraisal of meta-heuristic resource allocation techniques for IaaS cloud. Indian J Sci Technol 9:1–14

    Google Scholar 

  5. Madni SHH, Latiff MSA, Coulibaly Y (2016) Resource scheduling for infrastructure as a service (IaaS) in cloud computing: challenges and opportunities. J Netw Comp Appl 68:173–200

    Article  Google Scholar 

  6. Yi S, Li C, Li Q (2015) A survey of fog computing: concepts, applications and issues. In: 15th proceedings of the 2015 workshop on mobile big data, Hangzhou, pp 37–42

    Google Scholar 

  7. Yannuzzi M et al (2014) Key ingredients in an IoT recipe: fog computing, cloud computing, and more fog computing. In: 19th IEEE international workshop on computer aided modeling and design of communication links and networks (CAMAD), Athens, pp 325–329

    Google Scholar 

  8. Bose R, Saha MK, Sarddar D (2015) Fog computing made easy with the help of Citrix and Billboard manager. Int J Comput Appl 121:19–23

    Article  Google Scholar 

  9. Masip-Bruin X et al (2016) Fog-to-cloud computing (F2C): the key technology enabler for dependable e-health services deployment. In: 15th Mediterranean ad hoc networking workshop (Med-Hoc-Net), Vilanova i la Geltru, pp 1–5

    Google Scholar 

  10. Deng R et al (2015) Towards power consumption-delay tradeoff by workload allocation in cloud-fog computing. In: 5th IEEE international conference on communications (ICC), London, pp 3909–3914

    Google Scholar 

  11. Gital AY et al (2014) A framework for the design of cloud based collaborative virtual environment architecture. In: Proceedings of the international multiconference of engineers and computer scientists (IMECS), Hong Kong, pp 1–5

    Google Scholar 

  12. Usman MJ et al (2014) Modified low energy adaptive clustering hierarchy protocol for efficient energy consumption in wireless sensor networks. Int Rev Comput Softw (IRECOS) 9:1904–1915

    Article  Google Scholar 

  13. Gital AY et al (2015) A zone-based CVE architectural model for improving scalability and consistency. Electrical Engineering and Information Technology (IEET), Hangzhou, China, pp 63–68

    Google Scholar 

  14. Deng R et al (2016) Optimal workload allocation in fog-cloud computing towards balanced delay and power consumption. IEEE Internet Things J 3:1171–1181

    Google Scholar 

  15. Al Faruque MA, Vatanparvar K (2016) Energy management-as-a-service over fog computing platform. IEEE Internet Things J 3:161–169

    Article  Google Scholar 

  16. Tang B et al (2015) (2015) A hierarchical distributed fog computing architecture for big data analysis in smart cities. In: 2015 proceedings of the ASE big data & social informatics. ACM, Kaohsiung, Taiwan, pp 1–28

    Google Scholar 

  17. Stojmenovic I (2014) Fog computing: a cloud to the ground support for smart things and machine-to-machine networks. In: 26th Australasian telecommunication networks and applications conference (ATNAC), Southbank, VIC, pp 117–122

    Google Scholar 

  18. Costa R et al (2016) Smart cargo for multimodal freight transport: when “Cloud” becomes “Fog”. IFAC-PapersOnLine 49:121–126

    Article  Google Scholar 

  19. Vatanparvar K, Faruque A, Abdullah M (2016) Energy management as a service over fog computing platform. IEEE Internet Things J 3:163–169

    Google Scholar 

  20. Masip-Bruin X et al (2016) Foggy clouds and cloudy fogs: a real need for coordinated management of fog-to-cloud computing systems. IEEE Wirel Commun 23:120–128

    Article  Google Scholar 

  21. Madsen H et al (2013) Reliability in the utility computing era: towards reliable fog computing. In: 20th international conference on systems, signals and image processing (IWSSIP), Bucharest, pp 43–46

    Google Scholar 

  22. Bonomi F et al (2014) Fog computing: a platform for internet of things and analytics. In: Big data and Internet of Things: a roadmap for smart environments. Springer International Publishing, pp 169–186

    Google Scholar 

  23. Ottenwälder B et al (2013) MigCEP: operator migration for mobility driven distributed complex event processing. In: 7th ACM international conference on distributed event-based systems, Arlington, Texas USA, pp 183–194

    Google Scholar 

  24. Lewis G et al (2014) Tactical cloudlets: moving cloud computing to the edge. In: IEEE military communications conference (MILCOM), Baltimore, MD, pp 1440–1446

    Google Scholar 

  25. Aazam M, Huh E-N (2014) Fog computing and smart gateway based communication for cloud of things. In: 2014 2nd IEEE international conference on future Internet of Things and cloud (FiCloud), Bercelona, pp 464–470

    Google Scholar 

  26. Aazam M, Huh E-N (2015) Dynamic resource provisioning through fog micro datacenter. In: IEEE international conference on pervasive computing and communication workshops (PerCom Workshops), St. Louis, MO, pp 105–110

    Google Scholar 

  27. Nishio T et al (2013) Service-oriented heterogeneous resource sharing for optimizing service latency in mobile cloud. In: ACM proceedings of the first international workshop on mobile cloud computing & networking, Bangalore, India, pp 19–26

    Google Scholar 

  28. Dsouza C, Ahn G-J, Taguinod M (2014) Policy-driven security management for fog computing: preliminary framework and a case study. In: IEEE 15th international conference on information reuse and integration (IRI), Redwood City, CA, pp 16–23

    Google Scholar 

  29. Dastjerdi AV et al (2016) Fog computing: principals, architectures, and applications. arXiv preprint arXiv:1601.02752

  30. Liu F et al (2012) NIST cloud computing reference architecture: recommendations of the National Institute of Standards and Technology (special publication report 500–292). CreateSpace Independent Publishing Platform

    Google Scholar 

  31. Byers CC, Wetterwald P (2015) Fog computing distributing data and intelligence for resiliency and scale necessary for IoT: the Internet of Things (ubiquity symposium). Ubiquity, pp 1–12

    Google Scholar 

  32. Gupta H et al (2016) iFogSim: a toolkit for modeling and simulation of resource management techniques in Internet of Things, edge and fog computing environments. arXiv preprint arXiv:1606.02007

  33. Bonomi F et al (2012) Fog computing and its role in the internet of things. In: ACM proceedings of the first edition of the MCC workshop on mobile cloud computing, Helsinki, Finland, pp 13–16

    Google Scholar 

  34. Perera C, Qin Y, Estrella JC, Reiff-Marganiec S, Vasilakos AV (2017) Fog computing for sustainable smart cities: a survey. ACM Comput Surv 50:32–43

    Article  Google Scholar 

  35. Moreno-Vozmediano R, Montero RS, Huedo E, Llorente IM (2017) Cross-site virtual network in cloud and fog computing. IEEE Cloud Comput 4:46–53

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mathematical Sciences, Bauchi State University Gadau, 75110, Itas-Gadau, Nigeria

    Mohammed Joda Usman & Ahmed Aliyu

  2. School of Computing, Universiti Teknologi Malaysia, Skudai Johor, 81310, Malaysia

    Abdul Samad Ismail

  3. School of Computing and Technology, University of Gloucestershire, Cheltenham Park Campus, GL50 4SH, UK

    Hassan Chizari

  4. Department of Maths and Computer, Abubakar Tafawa Balewa University Bauchi, Bauchi, 740272, Nigeria

    Abdulsalam Ya’u Gital

  5. Department of Maths and Computer Science, Federal College of Education Technical Gombe, Gombe, 072158, Gombe, Nigeria

    Haruna Chiroma

  6. Department of Computer Science, Ahmadu Bello University Zaria, Zaria, 810211, Kaduna, Nigeria

    Mohammed Abdullahi

Authors
  1. Mohammed Joda Usman
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  2. Abdul Samad Ismail
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  3. Hassan Chizari
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  4. Abdulsalam Ya’u Gital
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  5. Haruna Chiroma
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  6. Mohammed Abdullahi
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  7. Ahmed Aliyu
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Corresponding author

Correspondence to Mohammed Joda Usman .

Editor information

Editors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  2. Federal College of Education (Technical), Gombe, Nigeria

    Haruna Chiroma

  3. Deakin University, Geelong, VIC, Australia

    Jemal H. Abawajy

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Usman, M.J. et al. (2019). Integrated Resource Allocation Model for Cloud and Fog Computing: Toward Energy-Efficient Infrastructure as a Service (IaaS). In: Herawan, T., Chiroma, H., Abawajy, J. (eds) Advances on Computational Intelligence in Energy. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-69889-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-69889-2_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69888-5

  • Online ISBN: 978-3-319-69889-2

  • eBook Packages: EnergyEnergy (R0)

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