A hybrid cloud-P2P architecture for multimedia information retrieval on VoD services

Recent research in Cloud Computing and Peer-to-Peer systems for Video-on-Demand (VoD) has focused on multimedia information retrieval, using cloud nodes as video streaming servers and peers as a way to distribute and share the video segments. A key challenge faced by these systems is providing an efficient way to retrieve the information segments descriptor, composed of its metadata and video segments, distributed among the cloud nodes and the Peer-to-Peer (P2P) network. In this paper, we propose a novel Cloud Computing and P2P hybrid architecture for multimedia information retrieval on VoD services that supports random seeking while providing scalability and efficiency. The architecture comprises Cloud and P2P layers. The Cloud layer is responsible for video segment metadata retrieval, using ontologies to improve the relevance of the retrieved information, and for distributing the metadata structures among cloud nodes. The P2P layer is responsible for finding peers that have the physical location of a segment. In this layer, we use trackers, which manage and collect the segments shared among other peers. We also use two Distributed Hash Tables, one to find these trackers and the other to store the information collected in case the tracker leaves the network and another peer needs to replace it. Unlike previous work, our architecture separates cloud nodes and peers responsibilities to manage the video metadata and its segments, respectively. Also, we show via simulations, the possibility of converting any peer to act as a tracker, while maintaining system scalability and performance, avoiding using centralized and powerful servers.