IoT 18(13): e1

Research Article

IoT-F2CDM-LB: IoT Based Fog-to-Cloud and Data-in-Motion Architectures with Load Balancing

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  • @ARTICLE{10.4108/eai.6-4-2018.155332,
        author={Istabraq M. Al-Joboury and Emad H. Al-Hemiary},
        title={IoT-F2CDM-LB: IoT Based Fog-to-Cloud and Data-in-Motion Architectures with Load Balancing},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={4},
        number={13},
        publisher={EAI},
        journal_a={IOT},
        year={2018},
        month={1},
        keywords={Internet of Things, Fog Computing, Cloud Computing, Load Balancing, Virtualization Technology},
        doi={10.4108/eai.6-4-2018.155332}
    }
    
  • Istabraq M. Al-Joboury
    Emad H. Al-Hemiary
    Year: 2018
    IoT-F2CDM-LB: IoT Based Fog-to-Cloud and Data-in-Motion Architectures with Load Balancing
    IOT
    EAI
    DOI: 10.4108/eai.6-4-2018.155332
Istabraq M. Al-Joboury1,*, Emad H. Al-Hemiary1
  • 1: Al-Nahrain University, College of Information Engineering, Department of Networks Engineering, Baghdad, Iraq
*Contact email: estabriq_94@coie-nahrain.edu.iq

Abstract

The work in this paper tries to enhance the performance of IoT by modifying the Cloud based architecture in terms of storage, processing, and Load Balancing (LB). The assumption is as follows: In a single Fog server, high traffic coming from Things may cause packet loss which in turn affect the overall IoT performance. To overcome such a situation, LB on Fog layer is proposed and implemented practically using virtualization technology. The proposed IoT based Fog-To-Cloud and Data-inMotion with LB (IoT-F2CDM-LB) architecture consists of two load balancers; HAProxy and Server Load Balancing (SLB), are used to distribute messages from Things to four virtualized Fog servers according to Least Connection technique. The implementation is carried out using VirtualBox and GNS3. Message Queue Telemetry Transport (MQTT) protocol is used to transfer messages between layers. Both load balancers result in packet loss reduction by 50%, lower response time and higher throughput.