2021/07/24 by Bingbing Rao, Liqiang Wang, Rao, Bingbing +1
Computer Science · #Cloud Computing and Resource Management #Distributed #FOS: Computer and information sciences #Graph Theory and Algorithms #IoT and Edge/Fog Computing #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2107.11540
openalex publication_date 2021/07/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We are living in the era of Big Data and witnessing the explosion of data. Given that the limitation of CPU and I/O in a single computer, the mainstream approach to scalability is to distribute computations among a large number of processing nodes in a cluster or cloud. This paradigm gives rise to the term of data-intensive computing, which denotes a data parallel approach to process massive volume of data. Through the efforts of different disciplines, several promising programming models and a few platforms have been proposed for data-intensive computing, such as MapReduce, Hadoop, Apache Spark and Dyrad. Even though a large body of research work has being proposed to improve overall performance of these platforms, there is still a gap between the actual performance demand and the capability of current commodity systems. This paper is aimed to provide a comprehensive understanding about current semantics-aware approaches to improve the performance of data-intensive computing. We first introduce common characteristics and paradigm shifts in the evolution of data-intensive computing, as well as contemporary programming models and technologies. We then propose four kinds of performance defects and survey the state-of-the-art semantics-aware techniques. Finally, we discuss the research challenges and opportunities in the field of semantics-aware performance optimization for data-intensive computing.