LAUSR

Fuzzy complex event processing-based decision-making systems have received considerable research interests recently. In particular, a well-managed operator distribution is required for improving the performance of the fuzzy complex event processing-based decision-making systems. However, the intrinsic uncertainty in dynamic input events increases the difficulties of operator distribution problem. To address these issues, a cost-aware, fault-tolerant and reliable strategy, called CaFtR is proposed for operator scheduling on fuzzy complex event processing systems based on the Technique for Order Preferences by Similarity to an Ideal Solution. The proposed CaFtR method adequately makes use of network resources to achieve continuous and highly available complex event processing regardless of dynamic operator migrations under fuzzy environment. Finally, a case study is provided to illustrate the efficiency of the proposed method, and the utility of our work is demonstrated through an application on the StreamBase system.