A Survey of Semantics-Aware Performance Optimization for Data-Intensive Computing

TL;DR

This survey reviews semantics-aware techniques that enhance performance in data-intensive computing, highlighting recent advances, challenges, and future research directions in optimizing large-scale data processing systems.

Contribution

It provides a comprehensive overview of semantics-aware performance optimization approaches, categorizes existing techniques, and discusses future research challenges in data-intensive computing.

Findings

Identified four types of performance defects in data-intensive systems.

Surveyed state-of-the-art semantics-aware optimization techniques.

Discussed key research challenges and future opportunities.

Abstract

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.