Resource management in cluster computing platforms for large scale data processing
Yi Yao · 2015
In the era of big data, one of the most significant research areas is cluster computing for large-scale data processing. Many cluster computing frameworks and cluster resource management schemes were recently developed to satisfy the increasing demands on large volume data processing. Among them, Apache Hadoop became the de facto platform that has been widely adopted in both industry and academia due to its prominent features such as scalability, simplicity and fault tolerance. The original Hadoop platform was designed to closely resemble the MapReduce framework, which is a programming paradigm for cluster computing proposed by Google. Recently, the Hadoop platform has evolved into its second generation, Hadoop YARN, which serves as a unified cluster resource management layer to support multiplexing of different cluster computing frameworks. A fundamental issue in this field is that given limited computing resources in a cluster, how to efficiently manage and schedule the execution of a large number of data processing jobs. Therefore, in this dissertation, we mainly focus on improving system efficiency and performance for cluster computing platforms, i.e., Hadoop MapReduce and Hadoop YARN, by designing the following new scheduling algorithms and resource management schemes.