Analyzing Big Data Workloads Using Discrete Simulation

Virtualized IT platforms abstracting from physical infrastructure offer easy scaling of storage and compute resources. This is a great progress for handling dynamic big data workloads but holds the danger of solving performance issues just with upscaling, without looking at causes. We propose to analyze the behavior of complex systems handling big data workloads using system modelling and discrete event simulation. It can be very revealing to see a simulated system in action by running simulations in different configurations, showing the impact of a modified system structure on its runtime behavior and resource consumption. With a toolbased system workload simulation example in the Earth Observation data domain we show how this approach can lead to significant resource and cost savings

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

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

A gray-box modeling methodology for runtime prediction of Apache Spark jobs

Apache Spark jobs are often characterized by processing huge data sets and, therefore, require runtimes in the range of minutes to hours. Thus, being able to predict the runtime of such jobs would be useful not only to know when the job will finish, but also for scheduling purposes, to estimate monetary costs for cloud deployment, or to determine an appropriate cluster configuration, such as the number of nodes. However, predicting Spark job runtimes is much more challenging than for standard database queries: cluster configuration and parameters have a significant performance impact and jobs usually contain a lot of user-defined code making it difficult to estimate cardinalities and execution costs. In this paper, we present a gray-box modeling methodology for runtime prediction of Apache Spark jobs. Our approach comprises two steps: first, a white-box model for predicting the cardinalities of the input RDDs of each operator is built based on prior knowledge about the behavior and application parameters such as applied filters data, number of iterations, etc. In the second step, a black-box model for each task constructed by monitoring runtime metrics while varying allocated resources and input RDD cardinalities is used. We further show how to use this gray-box approach not only for predicting the runtime of a given job, but also as part of a decision model for reusing intermediate cached results of Spark jobs. Our methodology is validated with experimental evaluation showing a highly accurate prediction of the actual job runtime and a performance improvement if intermediate results can be reused