A Coordination Framework for Deploying Hadoop MapReduce Jobs on Hadoop Cluster

University essay from KTH/Skolan för informations- och kommunikationsteknik (ICT)

Abstract: Apache Hadoop is an open source framework that delivers reliable, scalable, and distributed computing. Hadoop services are provided for distributed data storage, data processing, data access, and security. MapReduce is the heart of the Hadoop framework and was designed to process vast amounts of data distributed over a large number of nodes. MapReduce has been used extensively to process structured and unstructured data in diverse fields such as e-commerce, web search, social networks, and scientific computation. Understanding the characteristics of Hadoop MapReduce workloads is the key to achieving improved configurations and refining system throughput. Thus far, MapReduce workload characterization in a large-scale production environment has not been well studied. In this thesis project, the focus is mainly on composing a Hadoop cluster (as an execution environment for data processing) to analyze two types of Hadoop MapReduce (MR) jobs via a proposed coordination framework. This coordination framework is referred to as a workload translator. The outcome of this work includes: (1) a parametric workload model for the target MR jobs, (2) a cluster specification to develop an improved cluster deployment strategy using the model and coordination framework, and (3) better scheduling and hence better performance of jobs (i.e. shorter job completion time). We implemented a prototype of our solution using Apache Tomcat on (OpenStack) Ubuntu Trusty Tahr, which uses RESTful APIs to (1) create a Hadoop cluster version 2.7.2 and (2) to scale up and scale down the number of workers in the cluster. The experimental results showed that with well tuned parameters, MR jobs can achieve a reduction in the job completion time and improved utilization of the hardware resources. The target audience for this thesis are developers. As future work, we suggest adding additional parameters to develop a more refined workload model for MR and similar jobs.

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