Matching IoT Devices to the Fog Service Providers: A Mechanism Design Perspective†

In the Internet of Things (IoT) + Fog + Cloud architecture, with the unprecedented growth of IoT devices, one of the challenging issues that needs to be tackled is to allocate Fog service providers (FSPs) to IoT devices, especially in a game-theoretic environment. Here, the issue of allocation of FSPs to the IoT devices is sifted with game-theoretic idea so that utility maximizing agents may be benign. In this scenario, we have multiple IoT devices and multiple FSPs, and the IoT devices give preference ordering over the subset of FSPs. Given such a scenario, the goal is to allocate at most one FSP to each of the IoT devices. We propose mechanisms based on the theory of mechanism design without money to allocate FSPs to the IoT devices. The proposed mechanisms have been designed in a flexible manner to address the long and short duration access of the FSPs to the IoT devices. For analytical results, we have proved the economic robustness, and probabilistic analyses have been carried out for allocation of IoT devices to the FSPs. In simulation, mechanism efficiency is laid out under different scenarios with an implementation in Python.

[1]  Antonio Brogi,et al.  QoS-Aware Deployment of IoT Applications Through the Fog , 2017, IEEE Internet of Things Journal.

[2]  Nikhil R. Devanur,et al.  Stability of service under time-of-use pricing , 2017, STOC.

[3]  Ioannis Stavrakakis,et al.  QoE-Aware Rendering Service Allocation in Fog-Assisted Cloud Gaming Environments , 2020, 2020 5th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM).

[4]  Antonio Puliafito,et al.  Fog Computing for the Internet of Things , 2019, ACM Trans. Internet Techn..

[5]  Tao Qin,et al.  Selling Reserved Instances in Cloud Computing , 2015, IJCAI.

[6]  Poulami Dalapati A Survey on Cloud Computing , 2013 .

[7]  Jiangchuan Liu,et al.  Truthful Online Auction Toward Maximized Instance Utilization in the Cloud , 2018, IEEE/ACM Transactions on Networking.

[8]  Rajkumar Buyya,et al.  Fog Computing: A Taxonomy, Survey and Future Directions , 2016, Internet of Everything.

[9]  Zongpeng Li,et al.  A truthful (1-ε)-optimal mechanism for on-demand cloud resource provisioning , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[10]  Toni Janevski,et al.  Advanced QoS Provisioning and Mobile Fog Computing for 5G , 2018, Wirel. Commun. Mob. Comput..

[11]  Eui-nam Huh,et al.  Fog Computing Micro Datacenter Based Dynamic Resource Estimation and Pricing Model for IoT , 2015, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications.

[12]  Zhenni Li,et al.  Cost-Aware Streaming Workflow Allocation on Geo-Distributed Data Centers , 2017, IEEE Transactions on Computers.

[13]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[14]  Zongpeng Li,et al.  A Truthful $(1-\epsilon)$-Optimal Mechanism for On-demand Cloud Resource Provisioning , 2016, 1611.07619.

[15]  Sajal Mukhopadhyay,et al.  On free of cost service distribution in cloud computing , 2017, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[16]  HuPengfei,et al.  Survey on fog computing , 2017 .

[17]  Roch H. Glitho,et al.  A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges , 2017, IEEE Communications Surveys & Tutorials.

[18]  Luca Sanguinetti,et al.  Energy-Aware Competitive Power Allocation for Heterogeneous Networks Under QoS Constraints , 2014, IEEE Transactions on Wireless Communications.

[19]  Joseph Naor,et al.  Efficient online scheduling for deadline-sensitive jobs: extended abstract , 2013, SPAA.

[20]  Ujjwal Maulik,et al.  A Framework for Allocation of IoT Devices to the Fog Service Providers in Strategic Setting , 2019, 3PGCIC.

[21]  Deyu Qi,et al.  A Task Scheduling Algorithm Based on Classification Mining in Fog Computing Environment , 2018, Wirel. Commun. Mob. Comput..

[22]  Miao Pan,et al.  Joint Radio and Computational Resource Allocation in IoT Fog Computing , 2018, IEEE Transactions on Vehicular Technology.

[23]  Hsiao-Hwa Chen,et al.  Joint mode selection and resource allocation for downlink fog radio access networks supported D2D , 2015, 2015 11th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness (QSHINE).

[24]  Mugen Peng,et al.  Edge computing technologies for Internet of Things: a primer , 2017, Digit. Commun. Networks.

[25]  Ivan E. Sutherland,et al.  A futures market in computer time , 1968, Commun. ACM.

[26]  Kui Ren,et al.  When cloud meets eBay: Towards effective pricing for cloud computing , 2012, 2012 Proceedings IEEE INFOCOM.

[27]  Azzam Mourad,et al.  FoGMatch: An Intelligent Multi-Criteria IoT-Fog Scheduling Approach Using Game Theory , 2020, IEEE/ACM Transactions on Networking.

[28]  Supreet Kaur,et al.  A Survey in fog Computing , 2018 .

[29]  L. S. Shapley,et al.  College Admissions and the Stability of Marriage , 2013, Am. Math. Mon..

[30]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[31]  Zhu Han,et al.  Computing Resource Allocation in Three-Tier IoT Fog Networks: A Joint Optimization Approach Combining Stackelberg Game and Matching , 2017, IEEE Internet of Things Journal.

[32]  Zhu Han,et al.  A Machine-Learning-Based Auction for Resource Trading in Fog Computing , 2020, IEEE Communications Magazine.

[33]  Zongpeng Li,et al.  An Online Auction Mechanism for Dynamic Virtual Cluster Provisioning in Geo-Distributed Clouds , 2017, IEEE Transactions on Parallel and Distributed Systems.

[34]  Tim Roughgarden,et al.  CS 269 I : Incentives in Computer Science , 2016 .

[35]  David Hutchison,et al.  Game Theory for Multi-Access Edge Computing: Survey, Use Cases, and Future Trends , 2017, IEEE Communications Surveys & Tutorials.

[36]  Asif Ali Laghari,et al.  Quality of Experience and Quality of Service of Gaming Services in Fog Computing , 2020, ICMSS.

[37]  Heiko Ludwig,et al.  Zenith: Utility-Aware Resource Allocation for Edge Computing , 2017, 2017 IEEE International Conference on Edge Computing (EDGE).

[38]  Nicholas J. P. Race,et al.  Combinatorial Auction-Based Resource Allocation in the Fog , 2016, 2016 Fifth European Workshop on Software-Defined Networks (EWSDN).

[39]  Sajal Mukhopadhyay,et al.  Allocating resources in cloud computing when users have strict preferences , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[40]  Tim Roughgarden,et al.  Algorithmic Game Theory , 2007 .