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

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 dataintensive 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.

A novel data size‐aware offloading technique for resource provisioning in mobile cloud computing

“…A survey about data‐intensive scientific workflow managements (SWfMSs) 7 is presented for parallel execution across distributed resource providers, such as cloud infrastructure, and to improve the execution in a multisite cloud. According to Rao and Wang, 8 the emergence of mobile devices is the primary source of using the internet anywhere as needed. With this mobile device emergence in a very high manner, the big data‐intensive applications have also emerged in a vast manner and play a vital role in many people's life.…”

SODA: A Semantics-Aware Optimization Framework for Data-Intensive Applications Using Hybrid Program Analysis

“…To break through these dilemmas, a growing number of data-intensive computing frameworks have been proposed, such as MapReduce [13], Apache Hadoop [3], and Spark [45]. Generally, a mainstream approach to gain computing capability and scalability behind these platforms is to distribute data and computations across a cluster of nodes so that a large volume of data can be processed in a parallel and robust manner within a reasonable time [32], [50]. The successes of these frameworks owe to their MapReduce-like programming models, which are further based on data distribution techniques (e.g., Resilient Distributed Dataset (RDD) in Apache Spark [44]), and highorder functions (e.g., map, reduce, f ilter) that can take userdefined functions (UDFs) as arguments.…”