RanKloud: Scalable Multimedia Data Processing in Server Clusters

Multimedia data is produced in massive quantities. The applications that drive this massive data influx today span a large spectrum, from entertainment, surveillance, and e-commerce to Web content and social media. This data flood brings forth a need for highly parallelizable frameworks for scalable processing and efficient analysis of large media collections.In this article, we have observed that a major obstacle in executing large media-analysis operations in a scalable manner is that, when implemented naively, a significant portion of the work might be wasted. RanKloud is a system we are developing for share-nothing, batched parallelization of large media-analysis operations in a waste-avoiding manner. RanKloud achieves this by treating the utility of the data and features to the given analysis as an integral part of the process and by developing data partitioning and resource strategies that consider the ranked semantics of the analysis operations as well as data and utility characteristics discovered at runtime. Future work will include extension of the RanKloud framework with new primitives and investigation of the application of RanKloud to various large-scale multimedia data analysis applications.