Cloud-based social application deployment using local processing and global distribution

Social applications represent a paradigm shift on how the Internet is to be used, and have already changed the way we work, live, and play. When it comes to deploying social applications, cloud computing platforms are used to meet the Internet-scale, self-propagating, and fast-growing demands from these applications. Yet, to deploy social media applications in the most effective and economic fashion, we need to strategically design and follow a set of theoretical and practical principles. In this paper, we seek to design a set of new principles to guide social application deployment. Learning from large-scale measurement-based observations using a real-world social application, the gist of our principles is to detach the typically integrated "collection → processing → distribution" work ows in social applications into separate local processing and global distribution procedures, which can be effectively deployed using different cloud services. Moreover, based on a predictive model of regional propagation, we formulate the resource allocation problems in the processes of collecting/processing and distributing content as two optimization problems, which can be solved by efficient algorithms. Finally, based on our theoretical design, we have implemented an example social application on Amazon EC2 and Google AppEngine, where IaaS-based computation instances perform content collection and processing, and the PaaS-based platform is employed to distribute the contents that are widely propagating. Our PlanetLab-based trace-driven experiments have further confirmed the superiority of our design.

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