Reducing operational costs in cloud social TV: An opportunity for cloud cloning

The emergence of social TV has transformed TV experiences, providing a unified media experience across different devices. In response to this trend, we have implemented a multi-screen social TV system, offering video teleportation as an attractive feature. The enabling technology is instantiating a cloud clone to support all media outlets of each user. As the user shifts his attention from one device to the other, the cloud clone might migrate to a better location to reduce its operational cost. This paper investigates this cloud clone migration problem, aiming to minimize the monetary cost on operating video teleportation. Specifically, we formulate it into a Markov Decision Problem, to balance the trade-off between the migration cost and the content transmission cost. Under this framework, four algorithms are proposed to solve this optimization problem. We first characterize an upper and a lower bound for the optimal cost, by considering a random fixed placement and an offline algorithm. We then present a semi-online and a more practical Q-learning approach to make online decisions. Their performances are evaluated based on both simulated and real user traces. The results show that the Q-learning method achieves up to 25% cost compared to random fixed placement in typical scenarios. The savings are affected by the delivery path length, the migration size, and the user behavior pattern. Moreover, our investigations reveal the optimal cloud clone location is either at the nearest or the furthest node to the user along the content delivery path for a single user scenario.