Probabilistic Hesitant Fuzzy Methods for Prioritizing Distributed Stream Processing Frameworks for IoT Applications

Abstract

Distributed stream processing frameworks (DSPFs) are the vital engine, which can handle real-time data processing and analytics for IoT applications. How to prioritize DSPFs and select the most suitable one for special IoT applications is an open issue. To help developers of IoT applications to solve this complex issue, a novel probabilistic hesitant fuzzy multicriteria decision making (MCDM) model is put forward in this paper. To characterize the requirements for large-scale IoT data stream processing, a novel evaluation criteria system including qualitative and quantitative criteria is established. To accurately model the collective opinions from skilled developers and consider their psychological distance, the definition of probabilistic hesitant fuzzy sets (PHFSs) is used. To derive the importance degrees of criteria, a novel probabilistic hesitant fuzzy best-worst (PHFBW) method is proposed based on the score value. To prioritize the DSPFs and choose the most suitable one, a novel probabilistic hesitant fuzzy MULTIMOORA method is put forward. Finally, a practical case composed of four Apache stream processing frameworks, namely, Storm, Flink, Spark, and Samza, is studied. The obtained results indicate that throughput, latency, and reliability are considered to be the three most important criteria, and Flink is the most suitable stream framework.