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Proactive Load Balancing Mechanism for Fog Computing Supported by Parked Vehicles in IoV-SDN 被引量:1

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摘要 Internet of Vehicles(IoV)is a new style of vehicular ad hoc network that is used to connect the sensors of each vehicle with each other and with other vehicles’sensors through the internet.These sensors generate different tasks that should be analyzed and processed in some given period of time.They send the tasks to the cloud servers but these sending operations increase bandwidth consumption and latency.Fog computing is a simple cloud at the network edge that is used to process the jobs in a short period of time instead of sending them to cloud computing facilities.In some situations,fog computing cannot execute some tasks due to lack of resources.Thus,in these situations it transfers them to cloud computing that leads to an increase in latency and bandwidth occupation again.Moreover,several fog servers may be fuelled while other servers are empty.This implies an unfair distribution of jobs.In this research study,we shall merge the software defined network(SDN)with IoV and fog computing and use the parked vehicle as assistant fog computing node.This can improve the capabilities of the fog computing layer and help in decreasing the number of migrated tasks to the cloud servers.This increases the ratio of time sensitive tasks that meet the deadline.In addition,a new load balancing strategy is proposed.It works proactively to balance the load locally and globally by the local fog managers and SDN controller,respectively.The simulation experiments show that the proposed system is more efficient than VANET-Fog-Cloud and IoV-Fog-Cloud frameworks in terms of average response time and percentage of bandwidth consumption,meeting the deadline,and resource utilization.
机构地区 Ministry of Education
出处 《China Communications》 SCIE CSCD 2021年第2期271-289,共19页 中国通信(英文版)
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  • 1M. Armbrust, et al, "A view of cloud comput- ing", Communications of the ACM, vol.53, no.4, pp 50-58, 2010.
  • 2J. Manyika, et al, "Big data: The next frontier for innovation, competition, and productivity", The McKinsey Global Institute, vol.5, no.33, pp 222, 2011.
  • 3F. Bonomi, et al, "Fog computing and its role in the internet of things", Proceedings of the first edition of the MCC workshop on Mobile cloud computing, ACM, pp 13-16, 2012.
  • 4G, Vilutis, et al, "Model of load balancing and scheduling in Cloud computing", Information Technology Interfaces (ITI), Proceedings of the 11-1 2072 34th International Conference on. IEEE, pp 117-122, 2012.
  • 5B. Mondal, D. Kousik, and D. Paramartha, "Load balancing in cloud computing using stochastic hill climbing-a soft computing approach", Pro- cedia Technology, no.4, pp 783-789, 2012.
  • 6RV. Krishna, "Honey bee behavior inspired load balancing of tasks in cloud computing environ- ments", Applied Soft Computing, vo1.13, no.5, pp 2292-2303, 2013.
  • 7K. Mukherjee and G. Sahoo, "Mathematical model of cloud computing framework using fuzzy bee colony optimization technique", Ad- vances in Computing, Control, & Telecommuni- cation Technologies, ACT'09. International Con- ference on. IEEE, pp 664-668, 2009.
  • 8G.B. Zheng, "Achieving high performance on extremely large parallel machines: Performance prediction and load balancing", Ph.D. Thesis. Urbana, Illinois: University of Illinois at Urba- na-Champaign, 2005.
  • 9T. Agarwal, "Strategies for topology-aware task mapping and for rebalancing with bounded mi- grations Urbana", Illinois: University of Illinois at Urbana-Champaign, 2005.
  • 10O.A. Rahmeh, R Johnson and A. Taleb-Bendiab, "A dynamic biased random sampling scheme for scalable and reliable grid networks", INFO- COMP Journal of Computer Science, vol.7,no.4, pp 1-10, 2008.

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