Resource Management in SDN-VANETs: Coordination of Cloud-Fog-Edge Resources Using Fuzzy Logic

In this work, we propose an intelligent system for coordination and management of the cloud-fog-edge resources in Vehicular Ad hoc Networks (VANETs) using Software Defined Networking (SDN) and Fuzzy Logic (FL) approaches. The proposed system called Fuzzy-based System for Resource Management (FSRM) determines the appropriate resources to be used by a vehicle to process different VANETs applications. The decision is made by prioritizing the application requirements: Time Sensitivity (TS) and Data Size (DS), and by considering the available connections of the vehicle i.e., Number of Neighboring Vehicles (NNV) and Vehicle Relative Speed with Neighboring Vehicles (VRSNV). We demonstrate in simulation the feasibility of FSRM to improve the management of the network resources.

[1]  Leonard Barolli,et al.  A fuzzy-based approach for cluster management in VANETs: Performance evaluation for two fuzzy-based systems , 2018, Internet Things.

[2]  Leonard Barolli,et al.  A New Fuzzy-Based Resource Management System for SDN-VANETs , 2019, Int. J. Mob. Comput. Multim. Commun..

[3]  Azzedine Boukerche,et al.  Vehicular cloud computing: Architectures, applications, and mobility , 2018, Comput. Networks.

[4]  Xuemin Shen,et al.  Toward Efficient Content Delivery for Automated Driving Services: An Edge Computing Solution , 2018, IEEE Network.

[5]  Wenchao Xu,et al.  Internet of vehicles in big data era , 2018, IEEE/CAA Journal of Automatica Sinica.

[6]  Leonard Barolli,et al.  Performance analysis of two WMN architectures by WMN-GA simulation system considering different distributions and transmission rates , 2018, Int. J. Grid Util. Comput..

[7]  Abraham Kandel,et al.  Fuzzy Expert Systems , 1991 .

[8]  Sangjin Kim,et al.  Rethinking Vehicular Communications: Merging VANET with cloud computing , 2012, 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings.

[9]  HuangXinyi,et al.  An overview of Fog computing and its security issues , 2016 .

[10]  Mario Gerla,et al.  Towards software-defined VANET: Architecture and services , 2014, 2014 13th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET).

[11]  Leonard Barolli,et al.  A WLAN triage testbed based on fuzzy logic and its performance evaluation for different number of clients and throughput parameter , 2019 .

[12]  Silvia Giordano,et al.  The Next Paradigm Shift: From Vehicular Networks to Vehicular Clouds , 2013 .

[13]  Leonard Barolli,et al.  An Intelligent Approach for Resource Management in SDN-VANETs Using Fuzzy Logic , 2019, BWCCA.

[14]  Yacine Ghamri-Doudane,et al.  Software defined networking-based vehicular Adhoc Network with Fog Computing , 2015, 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM).

[15]  Leonard Barolli,et al.  A Fuzzy-Based System for Cloud-Fog-Edge Selection in VANETs , 2019, EIDWT.

[16]  L. Zadeh,et al.  Fuzzy Logic for the Management of Uncertainty , 1992 .

[17]  Stephan Olariu,et al.  Taking VANET to the clouds , 2011, Int. J. Pervasive Comput. Commun..

[18]  Ivan Stojmenovic,et al.  An overview of Fog computing and its security issues , 2016, Concurr. Comput. Pract. Exp..

[19]  Toshinori Munakata,et al.  Fuzzy systems: an overview , 1994, CACM.

[20]  F. Martin McNeill,et al.  Fuzzy Logic: A Practical Approach , 1994 .

[21]  Leonard Barolli,et al.  IoT node selection in Opportunistic Networks: Implementation of fuzzy-based simulation systems and testbed , 2019, Internet Things.