PRO is a preference-aware recurring query processing system that produces a recurring execution configuration that meets the application guidelines expressed via preference models. We propose an approach to tackle this maximal preference execution configuration problem using a PRO execution relation graph (ERG) model that effectively incorporates the dependencies between executions. This enables us to transform this problem into the well-known minimum weight length-k path problem, and to further design a dynamic-programming based pseudo-polynomial solution, called PRO-OPT. We also introduce adaptive re-optimization techniques to tackle the problem of fluctuating stream workloads.
Zhongfang Zhuang, Chuan Lei, Elke A. Rundensteiner, and Mohamed Eltabakh. "PRO: Preference-Aware Recurring Query Optimization."
Helix is the first scalable multi-query sharing engine tailored for recurring workloads in the MapReduce infrastructure. Helix deploys new sliced window-alignment techniques to create sharing opportunities among recurring queries without introducing additional I/O overheads or unnecessary data scans. It introduces a cost/benefit model for creating a sharing plan among the recurring queries, and a scheduling strategy for executing them to maximize the SLA satisfaction.
Chuan Lei, Zhongfang Zhuang, Elke A. Rundensteiner, and Mohamed Eltabakh. "Shared execution of recurring workloads in MapReduce."
Redoop Infrastructure Demonstration
This demonstration presents the Redoop infrastructure, the first full-fledged MapReduce framework with native support for recurring big data queries. We demonstrate Redoop’s capabilities on a compute cluster with real life workloads including click-stream and sensor data analysis.
Chuan Lei, Zhongfang Zhuang, Elke A. Rundensteiner, and Mohamed Eltabakh. "Redoop infrastructure for recurring big data queries."