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    Performance modelling, analysis and prediction of Spark jobs in Hadoop cluster : a thesis by publications presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science, School of Mathematical & Computational Sciences, Massey University, Auckland, New Zealand
    (Massey University, 2022) Ahmed, Nasim
    Big Data frameworks have received tremendous attention from the industry and from academic research over the past decade. The advent of distributed computing frameworks such as Hadoop MapReduce and Spark are powerful frameworks that offer an efficient solution for analysing large-scale datasets running under the Hadoop cluster. Spark has been established as one of the most popular large-scale data processing engines because of its speed, low latency in-memory computation, and advanced analytics. Spark computational performance heavily depends on the selection of suitable parameters, and the configuration of these parameters is a challenging task. Although Spark has default parameters and can deploy applications without much effort, a significant drawback of default parameter selection is that it is not always the best for cluster performance. A major limitation for Spark performance prediction using existing models is that it requires either large input data or system configuration that is time-consuming. Therefore, an analytical model could be a better solution for performance prediction and for establishing appropriate job configurations. This thesis proposes two distinct parallelisation models for performance prediction: the 2D-Plate model and the Fully-Connected Node model. Both models were constructed based on serial boundaries for a certain arrangement of executors and size of the data. In order to evaluate the cluster performance, various HiBench workloads were used, and workload’s empirical data were fitted with the models for performance prediction analysis. The developed models were benchmarked with the existing models such as Amdahl’s, Gustafson, ERNEST, and machine learning. Our experimental results show that the two proposed models can quickly and accurately predict performance in terms of runtime, and they can outperform the accuracy of machine learning models when extrapolating predictions.
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    An empirical comparison between MapReduce and Spark : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Information Sciences at Massey University, Auckland, New Zealand
    (Massey University, 2019) Liu, YuJia
    Nowadays, big data has become a hot topic around the world. Thus, how to store, process and analysis this big volume of data has become a challenge to different companies. The advent of distributive computing frameworks provides one efficient solution for the problem. Among the frameworks, Hadoop and Spark are the two that widely used and accepted by the big data community. Based on that, we conduct a research to compare the performance between Hadoop and Spark and how parameters tuning can affect the results. The main objective of our research is to understand the difference between Spark and MapReduce as well as find the ideal parameters that can improve the efficiency. In this paper, we extend a novel package called HiBench suite which provides multiple workloads to test the performance of the clusters from many aspects. Hence, we select three workloads from the package that can represent the most common application in our daily life: Wordcount (aggregation job),TeraSort (shuffle/sort job) and K-means (iterative job). Through a large number of experiments, we find that Spark is superior to Hadoop for aggreation and iterative jobs while Hadoop shows its advantages when processing the shuffle/sort jobs. Besides, we also provide many suggestions for the three workloads to improve the efficiency by parameter tuning. In the future, we are going to further our research to find out whether there are some other factors that may affect the efficiency of the jobs.