Dispersing Instant Social Video Service Across Multiple Clouds

TL;DR

This paper explores the feasibility and optimization of dispersing instant social video content across multiple cloud providers to improve service quality and reduce costs, using large-scale measurements and a heuristic algorithm.

Contribution

It introduces a novel multi-cloud social video hosting framework with a heuristic algorithm for optimal content placement considering social propagation and cost trade-offs.

Findings

Measurement studies reveal social propagation patterns and deployment characteristics.

The proposed heuristic algorithm effectively balances user preferences and inter-cloud traffic.

Simulation results demonstrate improved performance over baseline methods.

Abstract

Instant social video sharing which combines the online social network and user-generated short video streaming services, has become popular in today's Internet. Cloud-based hosting of such instant social video contents has become a norm to serve the increasing users with user-generated contents. A fundamental problem of cloud-based social video sharing service is that users are located globally, who cannot be served with good service quality with a single cloud provider. In this paper, we investigate the feasibility of dispersing instant social video contents to multiple cloud providers. The challenge is that inter-cloud social \emph{propagation} is indispensable with such multi-cloud social video hosting, yet such inter-cloud traffic incurs substantial operational cost. We analyze and formulate the multi-cloud hosting of an instant social video system as an optimization problem. We conduct large-scale measurement studies to show the characteristics of instant social video deployment, and demonstrate the trade-off between satisfying users with their ideal cloud providers, and reducing the inter-cloud data propagation. Our measurement insights of the social propagation allow us to propose a heuristic algorithm with acceptable complexity to solve the optimization problem, by partitioning a propagation-weighted social graph in two phases: a preference-aware initial cloud provider selection and a propagation-aware re-hosting. Our simulation experiments driven by real-world social network traces show the superiority of our design.