Advertisement

TSP-HVC: a novel task scheduling policy for heterogeneous vehicular cloud environment

  • S. K. Bhoi
  • S. K. Panda
  • S. R. Ray
  • R. K. Sethy
  • V. K. Sahoo
  • B. P. Sahu
  • S. K. Nayak
  • S. Panigrahi
  • R. K. Moharana
  • P. M. Khilar
Original Research

Abstract

Vehicular communication becomes an advanced area of research to deliver services to the users like standardization, traffic management and road safety, infotainment and entertainment. Vehicles carry communication modules, computing facility, storage, Internet accessing capability and equipped with application specific sensors in the on-board unit. Recently, vehicular cloud computing is a technology that is embedded with vehicular networks to solve many vehicular networking issues and challenges like storage, computing, information gain, internet access, network, security, etc. In this paper, we consider the storage as a service (STaaS) issue in vehicular networks. The vehicles in the parking lots are used as a data center to store the information of the user. However, providing this service to the users requires a scheduling policy to give the storage service in a minimum time. Therefore, we develop a new scheduling policy, called TSP-HVC for STaaS in the vehicular cloud environment. The simulation results show that the proposed scheduling policy produces less makespan and high average resource utilization than the well-known min–min and max–min cloud scheduling policies.

Keywords

Vehicular cloud computing Task scheduling Storage as a service Vehicular ad hoc networks Max–min Min–min Makespan Resource utilization 

References

  1. 1.
    Zheng K, Meng H, Chatzimisios P, Lei L, Shen X (2015) An SMDP-based resource allocation in vehicular cloud computing systems. IEEE Trans Industr Electron 62(12):7920–7928CrossRefGoogle Scholar
  2. 2.
    Bitam S, Mellouk A, Zeadally S (2015) VANET-cloud: a generic cloud computing model for vehicular ad hoc networks. IEEE Wirel Commun 22(1):96–102CrossRefGoogle Scholar
  3. 3.
    Lee E, Lee E, Gerla M, Oh S (2014) Vehicular cloud networking: architecture and design principles. IEEE Commun Mag 52(2):148–155CrossRefGoogle Scholar
  4. 4.
    Zeadally S, Hunt R, Chen Y, Irwin A, Hassan A (2012) Vehicular ad hoc networks (VANETS): status, results and challenges. Telecommun Syst 50(4):217–241 (Springer) CrossRefGoogle Scholar
  5. 5.
    Bhoi S, Khilar P (2016) RVCloud: a routing protocol for vehicular ad hoc network in city environment using cloud computing. Wirel Netw 22(4):1329–1341 (Springer) CrossRefGoogle Scholar
  6. 6.
    Gerla M (2012) Vehicular cloud computing. In: the 11th annual Mediterranean ad hoc networking workshop, IEEE, pp 152–155Google Scholar
  7. 7.
    Whaiduzzaman M, Sookhak M, Gani A, Buyya R (2014) A survey on vehicular cloud computing. J Netw Comput Appl 40:325–344 (Elsevier) CrossRefGoogle Scholar
  8. 8.
    Son J, Eun H, Oh H, Kim S, Hussain R (2012) Rethinking vehicular communications: merging VANET with cloud computing. In: IEEE 4th international conference on cloud computing technology and Science, pp 606–609Google Scholar
  9. 9.
    Panda S, Jana P (2015) Efficient task scheduling algorithms for heterogeneous multi-cloud environment. J Supercomput 71(4):1505–1533 (Springer) CrossRefGoogle Scholar
  10. 10.
    Fang Y, Wang F, Ge J (2010) A task scheduling algorithm based on load balancing in cloud computing. In: web information systems and mining, Springer, pp 271–277Google Scholar
  11. 11.
    Panda S, Gupta I, Jana P (2017) Task scheduling algorithms for multi-cloud systems: allocation-aware approach. Information systems frontiers. Springer, New York, pp 1–19Google Scholar
  12. 12.
    Panda S, Jana P (2017) SLA-based task scheduling algorithms for heterogeneous multi-cloud environment. J Supercomput 73(6):2730–2762 (Springer) CrossRefGoogle Scholar
  13. 13.
    Panda S, Jana P (2016) Uncertainty-based QoS min–min algorithm for heterogeneous multi-cloud environment. Arab J Sci Eng 41(8):3003–3025 (Springer) CrossRefGoogle Scholar
  14. 14.
    Panda S, Pande S, Das S (2018) Task partitioning scheduling algorithms for heterogeneous multi-cloud environment. Arab J Sci Eng 43(2):913–933 (Springer) CrossRefGoogle Scholar
  15. 15.
    Nuaimi K, Mohamed N, Nuaimi M, Al-Jaroodi J (2012) A survey of load balancing in cloud computing: challenges and algorithms. In: second symposium on network cloud computing and applications, IEEE, pp 137–142Google Scholar
  16. 16.
    Panda S, Bhoi S, Khilar P (2012) A semi-interquartile min-min max-min (sim2) approach for grid task scheduling. In: international conference on advances in computing, Springer, pp 415–421Google Scholar
  17. 17.
    Kumar M, Yadav A, Khatri P, Raw R (2018) Global host allocation policy for virtual machine in cloud computing. Int J Inform Technol 1–9 (Springer)Google Scholar
  18. 18.
    Sharma S, Rath A (2017) Multi-rumen anti-grazing approach of load balancing in cloud network. Int J Inform Technol 9(2):129–138 (Springer) CrossRefGoogle Scholar
  19. 19.
    Rath M (2017) Resource provision and QoS support with added security for client side applications in cloud computing. Int J Inform Technol 1–8 (Springer)Google Scholar
  20. 20.
    Kumar S, Raza Z (2017) Using clustering approaches for response time aware job scheduling model for internet of things. Int J Inform Technol 9(2):177–195 (Springer) CrossRefGoogle Scholar
  21. 21.
    Karamitsos I, Apostolopoulos C (2018) Optical trends in data centers architectures for smart cities. Int J Inform Technol 10(1):3–9 (Springer) CrossRefGoogle Scholar

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2018

Authors and Affiliations

  • S. K. Bhoi
    • 1
  • S. K. Panda
    • 2
  • S. R. Ray
    • 1
  • R. K. Sethy
    • 1
  • V. K. Sahoo
    • 1
  • B. P. Sahu
    • 1
  • S. K. Nayak
    • 1
  • S. Panigrahi
    • 1
  • R. K. Moharana
    • 1
  • P. M. Khilar
    • 3
  1. 1.Department of Computer Science and EngineeringParala Maharaja Engineering CollegeBerhampurIndia
  2. 2.Department of Information TechnologyVeer Surendra Sai University of TechnologyBurlaIndia
  3. 3.Department of Computer Science and EngineeringNational Institute of TechnologyRourkelaIndia

Personalised recommendations