Distributed resource sharing in fog-assisted big data streaming

Abstract

Fog computing is a promising architectural pattern to reduce the amount of data that is transferred to the cloud for processing and analysis. In this paper, we study fog-assisted data streaming scenario in which fog nodes at the network edge share their spare resources to help pre-process raw data of applications hosted in the cloud. A distributed resource sharing scheme is presented where the software defined network (SDN) controller dynamically adjusts the volume of application data that will be directed to fog nodes for pre-processing. The SDN controller makes decisions by coordinating fog nodes and cloud platform to collaboratively solve a social welfare maximization problem. Based on a hybrid alternating direction method of multipliers (H-ADMM) algorithm, computation burden for solving the optimization problem is fully distributed to fog nodes, cloud platform and SDN controller, where local variables of fog nodes are updated in parallel. With proper design of message exchange pattern, the communication overhead of the coordination to SDN controller grows smoothly with increasing number of participating fog nodes.