AUTOMATED AND ADAPTIVE DOWNLOAD SERVICE USING P2P APPROACH IN CLOUD

Due to the explosive growth of the Internet and increasing demand for multimedia information on the web, video content distribution over the Internet has received tremendous attention from academic and industry. The most common approach for such is the peer-to-Peer approach. In this approach, peers who create demand for videos also share their content with other peers. The service capacity thus increases automatically with increasing peer population, making scalability an advantage of the peer-to-peer solution but a high speed file downloading is not guaranteed. So, to remedy this, a cloud downloading scheme is deployed having a high service capacity using eye Os. This system used two design philosophies using cloud either as a server or a server to suit itself to any operating system scenarios. The server mode when video population is large compared to cache size, and the helper mode when peer request rate is high compared to server bandwidth. We design an adaptive algorithm (AMS) to select the service mode automatically. The ability of AMS to achieve good performance in different operating regimes is validated by simulation.