Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
A Fuzzy-Based System for Actor Node Selection in WSANs for Improving Network Connectivity and Increasing Number of Covered Sensors Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Optimized Resource Allocation in Fog-Cloud Environment Using Insert Select

Authors : Muhammad Usman Sharif, Nadeem Javaid, Muhammad Junaid Ali, Wajahat Ali Gilani, Abdullah Sadam, Muhammad Hassaan Ashraf

Published in: Advances in Network-Based Information Systems

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Energy management in modern way is done using cloud computing services to fulfill the energy demands of the users. These amenities are used in smart buildings to manage the energy demands. Entertaining maximum requests in minimum time is the main goal of our proposed system. To achieve this goal, in this paper, a scheme for resource distribution is proposed for cloud-fog based system. When the request is made by the user, the allocation of Virtual Machines (VMs) to the Data Centers (DCs) is required to be done timely for DSM. This model helps the DCs in managing the VMs in such a way that the request entertainment take minimum Response Time (RT). The proposed Insert Select Technique (IST) tackle this problem very effectively. Simulation results depicts the cost effectiveness and effective response time (RT) achievement.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
previous chapter A Dynamism View Model of Convergence and Divergence of IoT Standardization
next chapter Smart Grid Management Using Cloud and Fog Computing
Literature
1.
Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J. Internet Serv. Appl. 1, 718 (2010)CrossRef
2.
Fatima, I., Javaid, N., Iqbal, M.N., Shafi, I., Anjum, A., Memon, U.: Integration of cloud and fog based environment for effective resource distribution in smart buildings. In: 14th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC-2018), pp. 2-6 (2018)
3.
Zahoor, S., Javaid, N., Khan, A., Muhammad, F.j., Zahid, M., Guizani, M.: A cloud-fog-based smart grid model for efficient resource utilization. In: 14th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC-2018) (2018)
4.
Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25, 599–616 (2009)CrossRef
5.
Subashini, S., Kavitha, V.: A survey on security issues in service delivery models of cloud computing. J. Netw. Comput. Appl. 34, 111 (2011)CrossRef
6.
Mukherjee, M., Matam, R., Shu, L., Maglaras, L., Ferrag, M.A., Choudhury, N., Kumar, V.: Security and privacy in fog computing: challenges. IEEE Access 5, 1929319304 (2017)
7.
Wickremasinghe, B., Buyya, R.: CloudAnalyst: a CloudSim-based tool for modelling and analysis of large scale cloud computing environments. MEDC Proj. Rep. 22(6), 433–659 (2009)
8.
del Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.-C., Harley, R.G.: Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans. Evol. Comput. 12, 171–195 (2008)CrossRef
9.
Wang, X., Ning, Z., Wang, L.: Offloading in internet of vehicles: a fog-enabled real-time traffic management system. In: IEEE Transactions on Industrial Informatics, vol. 11 (2018)
10.
Dehghanpour, E., Kazemi Karegar, H., Kheirollahi, R., Soleymani, T.: Optimal coordination of directional overcurrent relays in microgrids by using cuckoo-linear optimization algorithm and fault current limiter. IEEE Trans. Smart Grid 9, 1365–1375 (2018)CrossRef
11.
Thirumalaisamy, B., Jegannathan, K.: A novel energy management scheme using ANFIS for independent microgrid. Int. J. Renew. Energy Res. (IJRER) 6(3), 735–46 (2016)
12.
Zachar, M., Daoutidis, P.: Microgrid/Macrogrid energy exchange: a novel market structure and stochastic scheduling. IEEE Trans. Smart Grid 8, 178–189 (2017)CrossRef
13.
Huld, T., Moner-Girona, M., Kriston, A.: Geospatial analysis of photovoltaic mini-grid system performance. Energies 10, 218 (2017)CrossRef
14.
Lin, W.-M., Tu, C.-S., Tsai, M.-T.: Energy management strategy for microgrids by using enhanced bee colony optimization. Energies 9, 5 (2015)CrossRef
15.
Tumuluru, V.K., Huang, Z., Tsang, D.H.K.: Integrating price responsive demand into the unit commitment problem. IEEE Trans. Smart Grid 5, 2757–2765 (2014)CrossRef
16.
Tsai, L., Liao, W.: StarCube: an on-demand and cost-effective framework for cloud data center networks with performance guarantee. IEEE Trans. Cloud Comput. 6, 235249 (2018)CrossRef
17.
Nguyen, Thoai, Nam.: A Genetic Algorithm for Power-aware Virtual Machine Allocation in Private Cloud (2013)
18.
Dasgupta, K., Mandal, B., Dutta, P., Mandal, J.K., Dam, S.: A genetic algorithm (GA) based load balancing strategy for cloud computing. Procedia Technol. 10, 340–347 (2013)CrossRef
19.
Desai, T., Prajapati, J.: A Survey of Various Load Balancing Techniques and Challenges in Cloud Computing (2013)
20.
Mirjalili, S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27, 1053–1073 (2015)CrossRef
21.
Cao, Z., Lin, J., Wan, C., Song, Y., Zhang, Y., Wang, X.: Optimal cloud computing resource allocation for demand side management. IEEE Trans. Smart Grid 113 (2016)
22.
Adhianto, L., Banerjee, S., Fagan, M., Krentel, M., Marin, G., Mellor-Crummey, J., Tallent, N.R.: HPCTOOLKIT: tools for performance analysis of optimized parallel programs. Concurrency Comput. Pract. Exp. (2009)
23.
Sahni, J., Vidyarthi, P.: A cost-effective deadline-constrained dynamic scheduling algorithm for scientific workflows in a cloud environment. IEEE Trans. Cloud Comput. 6, 218 (2018)CrossRef
24.
Wang, S., Zhang, J., Huang, T., Pan, T., Liu, J., Liu, Y.: Flow distribution-aware load balancing for the datacenter. Comput. Commun. 106, 136–146 (2017)CrossRef
25.
Wang, L., Gelenbe, E.: Adaptive dispatching of tasks in the cloud. IEEE Trans. Cloud Comput. 6, 33–45 (2018)CrossRef
26.
Islam, M.A., Ren, S., Quan, G., Shakir, M.Z., Vasilakos, A.V.: Water-constrained geographic load balancing in data centers. IEEE Trans. Cloud Comput. 5, 208–220 (2017)CrossRef
27.
Liu, L., Chang, Z., Guo, X., Mao, S., Ristaniemi, T.: Multiobjective optimization for computation offloading in fog computing. IEEE Internet Things J. 5, 283–294 (2018)CrossRef
Metadata
Title
Optimized Resource Allocation in Fog-Cloud Environment Using Insert Select
Authors
Muhammad Usman Sharif
Nadeem Javaid
Muhammad Junaid Ali
Wajahat Ali Gilani
Abdullah Sadam
Muhammad Hassaan Ashraf
Copyright Year
2019
Book
Advances in Network-Based Information Systems

Print ISBN: 978-3-319-98529-9

Electronic ISBN: 978-3-319-98530-5

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-98530-5_53

Premium Partner

    Image Credits