MeFoRE: QoE based resource estimation at Fog to enhance QoS in IoT

Internet of Things (IoT) is now transitioning from theory to practice. This means that a lot of data will be generated and the management of this data is going to be a big challenge. To transform IoT into reality and build upon realistic and more useful services, better resource management is required at the perception layer. In this regard, Fog computing plays a very vital role. With the advent of Vehicular Ad hoc Networks (VANET) and remote healthcare and monitoring, quick response time and latency minimization are required. However, the receiving nodes have a very fluctuating behavior in resource consumption especially if they are mobile. Fog, a localized cloud placed close to the underlying IoTs, provides the means to cater such issues by analyzing the behavior of the nodes and estimating resources accordingly. Similarly, Service Level Agreement (SLA) management and meeting the Quality of Service (QoS) requirements also become issues. In this paper, we devise a methodology, referred to as MEdia FOg Resource Estimation (MeFoRE), to provide resource estimation on the basis of service give-up ratio, also called Relinquish Rate (RR), and enhance QoS on the basis of previous Quality of Experience (QoE) and Net Promoter Score (NPS) records. The algorithms are implemented using CloudSim and applied on real IoT traces on the basis of Amazon EC2 resource pricing.

[1]  Eui-nam Huh,et al.  Fog Computing and Smart Gateway Based Communication for Cloud of Things , 2014, 2014 International Conference on Future Internet of Things and Cloud.

[2]  Mohammad Hayajneh,et al.  Data Management for the Internet of Things: Design Primitives and Solution , 2013, Sensors.

[3]  Eui-nam Huh,et al.  Dynamic resource provisioning through Fog micro datacenter , 2015, 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[4]  Songwei Fu,et al.  CRAWDAD dataset due/packet-delivery (v.2015-03-30) , 2015 .

[5]  Huansheng Ning,et al.  Future Internet of Things Architecture: Like Mankind Neural System or Social Organization Framework? , 2011, IEEE Communications Letters.

[6]  Young-Koo Lee,et al.  Intra graph clustering using collaborative similarity measure , 2015, Distributed and Parallel Databases.

[7]  Javier Cubo,et al.  A Cloud-Based Internet of Things Platform for Ambient Assisted Living , 2014, Sensors.

[8]  Malek Ben Salem,et al.  Fog Computing: Mitigating Insider Data Theft Attacks in the Cloud , 2012, 2012 IEEE Symposium on Security and Privacy Workshops.

[9]  Antonio Puliafito,et al.  Enabling the Cloud of Things , 2012, 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[10]  Kenji Tei,et al.  ClouT : Cloud of things for empowering the citizen clout in smart cities , 2014, 2014 IEEE World Forum on Internet of Things (WF-IoT).

[11]  Yonezawa Takuro.  Cloud of Things for empowering the citizen clout in smart cities , 2014 .

[12]  Dusit Niyato,et al.  A Framework for Cooperative Resource Management in Mobile Cloud Computing , 2013, IEEE Journal on Selected Areas in Communications.

[13]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[14]  Feng Xia,et al.  Cloudlet deployment in local wireless networks: Motivation, architectures, applications, and open challenges , 2016, J. Netw. Comput. Appl..

[15]  Sudip Misra,et al.  Target Tracking Using Sensor-Cloud: Sensor-Target Mapping in Presence of Overlapping Coverage , 2014, IEEE Communications Letters.

[16]  Antonio Iera,et al.  Improving Service Management in the Internet of Things , 2012, Sensors.