Computing Resource Allocation in Three-Tier IoT Fog Networks: A Joint Optimization Approach Combining Stackelberg Game and Matching
By Huaqing Zhang, Yong Xiao, Shengrong Bu, Dusist Niyato, Richard Yu, Zhu Han. IEEE Internet of Things Journal, 2017
311 citations recorded by the source corpus.
Fog computing is a promising architecture to provide economical and low latency data services for future Internet of Things (IoT)-based network systems. Fog computing relies on a set of low-power fog nodes (FNs) that are located close to the end users to offload the services originally targeting at cloud data centers. In this paper, we consider a specific fog computing network consisting of a set of data service operators (DSOs) each of which controls a set of FNs to provide the required data service to a set of data service subscribers (DSSs). How to allocate the limited computing resources of FNs to all the DSSs to achieve an optimal and stable performance is an important problem. Therefore, we propose a joint optimization framework for all FNs, DSOs, and DSSs to achieve the optimal resource allocation schemes in a distributed fashion. In the framework, we first formulate a Stackelberg game to analyze the pricing problem for the DSOs as well as the resource allocation problem for the DSSs. Under the scenarios that the DSOs can know the expected amount of resource purchased by the DSSs, a many-to-many matching game is applied to investigate the pairing problem between DSOs and FNs. Finally, within the same DSO, we apply another layer of many-to-many matching between each of the paired FNs and serving DSSs to solve the FN-DSS pairing problem. Simulation results show that our proposed framework can significantly improve the performance of the IoT-based network systems.
Subfields: Computer Networks and Communications, Information Systems
View Paper: https://www.semanticscholar.org/paper/714cdb565117df844846fb1b9da0e1fb0b695d23
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In Computer Networks and Communications
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- Ekram Hossain
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In Information Systems
Citations in the corpus
- Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing (2018, methodology): “Hence, the energy-delay tradeoff has received significant attention and has been studied in various MEC systems [7]–[22].”
- Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges (2019, methodology): “[49], [51] proposed an optimized resource allocation framework for fog-based IoT.”
- Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges (2018, background): “A more detail can be found in [72] and [73].”
- Computation Resource Allocation and Task Assignment Optimization in Vehicular Fog Computing: A Contract-Matching Approach (2019, background): “problems with mutual preferences in Internet of things (IoT) fog networks [31], device-to-device networks [12], and vehicular content distribution networks [1], etc.”