Stable Matching based Resource Allocation for Large-Scale Fog Computing

Abstract

Fog computing is used to expand Cloud computing services at the network edge. A key step in the process of improving Fog services is the management of Fog resources. Fog resources are usually dynamic, heterogeneous, latencyconstrained, and bandwidth-constrained as compared to Cloud resources. Improving the performance of Fog systems requires addressing Fog resource management. Also, in large-scale Fog networks, resource management still faces more difficulties despite these improvements. Towards this end, this paper proposes a new resource allocation technique in a large-scale Fog network to optimally serve the service requests generated by a set of IoT objects. In this technique, we exploit the proven stable and efficient Gale Shapley matching algorithm in large-scale Fog computing networks. IFogSIM is used to demonstrate the effectiveness of this approach.