The rationale and vision of this project is to leverage from the above two paradigms especially since the Inria team has expertise in P2P computing and the UCSB team has expertise in Cloud computing. At present, in the commercial realm, cloud computing has emerged as a dominant paradigm. However, we contend that cloud computing is amenable for supporting client-server interactions. As we move towards applications that are more collaborative and require continuous interactivity (i.e., latency sensitive applications), the cloud computing paradigm may not be able to sustain such applications. Examples of such applications arise in the area of distributed gaming, group video-chat, online interactive classrooms, and synchronous group interactions in online social networks. The commonality among all these applications is that they require many-to-many communication as well as the need for streaming media flow among all the members. Clearly, the centralized model of cloud computing will result in significant network resources (i.e., bandwidth) since all the input streams need to be delivered to the cloud, where they are processed and then the cloud has to deliver the resulting output streams to all the end-points. A more compelling design would be to use the cloud for managing such application sessions and use the P2P model for media processing and media delivery among the end-users involved in a collaborative application.
The main objective of this research and scientific collaboration is to develop a hybrid architecture of a computational platform that leverages the cloud computing and the P2P computing paradigms. The resulting architecture will enable scalable data management and data analysis infrastructures that can be used to host a variety of next-generation applications that benefit from computing, storage, and networking resources that exist not only at the network core (i.e., data-centers) but also at the network edge (i.e., machines at the user level as well as machines available in CDNs – content distribution networks hosted in ISPs).
In the context of data management and data analysis, we explore new applications that are particularly amenable for being hosted on the proposed hybrid architecture. In particular, we develop prototype applications such as collaborative applications that require the many-to-many delivery of data. We tackle the data management research challenges to support such applications. Similarly, big data analysis has emerged as an important problem due to the vast amount of information and data that is becoming available in diverse domains such as scientific computing and online social networks. During the course of this project, we explore new parallel processing paradigms for big data analytics especially in the context of scientific data and decentralized (P2P) recommendation for big data sharing. Furthermore, we address privacy-preserving data management in this context.
