The QUASIT Model and Framework for Scalable Data Stream Processing with Quality of Service

Many academic and industrial research activities have recently recognized the relevance of expressive models and effective frameworks for highly scalable data processing, such as MapReduce. This paper presents the novel Quasit programming model and runtime framework for stream processing in datacenters, with its original capabilities of i) allowing developers to choose among a large set of quality policies to associate with their processing tasks in a fine-grained way, and ii) effectively managing processing execution depending on the associated quality indications. The paper describes the Quasit programming model, via the primary design/implementation choices made in the Quasit runtime framework (available for download from the project Web site) to achieve maximum scalability, flexibility, and reusability. The first experiences with our prototype and the reported experimental results show the feasibility of our approach and its good performance in terms of both limited overhead and horizontal scalability.