Optimal Searching of Prefetched DASH Segments in Fog Nodes: A Multi-Armed Bandit Approach

Abstract

Data prefetching at fog nodes that significantly reduces latency is critical for video streaming applications. In the framework of dynamic video streaming over fog, some segments (parts of video) can be prefetched by the network provider. Sharing the dynamic information about prefetched node locations to all of the users is not a scalable approach, due to signalling and performance overheads. However, at the time of downloading, the client devices can search for the availability of DASH video segments in the fog nodes present in their vicinity. There is, however, a dilemma of how many fog nodes can be queried, without affecting performance such as continuous playback of the video. \par In this paper, we propose an efficient mechanism for searching video segments over different fog nodes. This search mechanism is formulated as a sequential stochastic modeling framework known as Multi-Armed Bandit∼(MAB). While the state space of this model is a countably infinite set, we propose an algorithmic approach to transform to a finite state space model without loss of optimality. With extensive simulation results, the analytical results are validated. We study the different parameters influencing the improved optimal DASH performance, in terms of segment prefetch probability, and number of search attempts made by the client.