LEO Satellite Networks Assisted Geo-distributed Data Processing

TL;DR

This paper introduces DVA, a novel satellite selection algorithm that optimizes data transfer from edge clouds to core clouds via LEO satellites, reducing network congestion and transmission time for big data tasks.

Contribution

The paper proposes DVA, a data volume aware satellite selection algorithm that improves data transfer efficiency in LEO satellite-assisted edge-cloud networks.

Findings

DVA significantly reduces average access network duration.

DVA outperforms existing satellite selection algorithms in simulations.

The approach effectively prevents network congestion during data transfer.

Abstract

Nowadays, the increasing deployment of edge clouds globally provides users with low-latency services. However, connecting an edge cloud to a core cloud via optic cables in terrestrial networks poses significant barriers due to the prohibitively expensive building cost of optic cables. Fortunately, emerging Low Earth Orbit (LEO) satellite networks (e.g., Starlink) offer a more cost-effective solution for increasing edge clouds, and hence large volumes of data in edge clouds can be transferred to a core cloud via those networks for time-sensitive big data tasks processing, such as attack detection. However, the state-of-the-art satellite selection algorithms bring poor performance for those processing via our measurements. Therefore, we propose a novel data volume aware satellite selection algorithm, named DVA, to support such big data processing tasks. DVA first takes into account both the data size in edge clouds and satellite capacity to finalize the selection, thereby preventing congestion in the access network and reducing transmitting duration. Extensive simulations validate that DVA has a significantly lower average access network duration than the state-of-the-art satellite selection algorithms in a LEO satellite emulation platform.