Probabilistic model-driven video scheduling for the social media application in the Federated cloud

Federated cloud is a trend in cloud computing which, by interconnecting multiple separate clouds at different geographical locations, can provide a cloud platform with much larger resource capacities. Such a cloud is ideal for supporting the social media applications. The videos in the social media applications are always short clips and stored in different cloud sites. Usually, when finishing a current video, a user quickly tracks and loads second video according to their interest. The behaviors are called as User Interest Tracking behaviors (UIT behaviors). The frequent UIT behaviors pose a great challenge on the UIT waiting delay which is defined as the interval between the request time and the playback time of a video. In this work, we have developed a video scheduling framework to reduce the waiting delay for social media applications in the Federated cloud. We first propose the UIT probabilistic model which can estimate the video access probability based on the UIT statistics. Based on the probability, we further develop a probabilistic model-driven video scheduling scheme to minimize the expected UIT waiting delay. With extensive simulations, we demonstrate that the proposed scheduling scheme works very well.