Over the years, the popularity of iterative data-intensive applications such as machine learning applications has grown immensely. Unlike batch applications, iterative applications such as k-means, regression or classification algorithms require multiple access to the input data to train it sufficiently for convergence. In the context of big data, these applications are executed on distributed computing frameworks such as Apache Spark. These frameworks are simple to deploy and use, however, under the hood they are complex and highly configurable. To perform an exhaustive study of the impact of these ubiquitous parameters on application performance would be cumbersome due to the exponential amount of their combinations.

In recent years, there has been a lot of focus on benchmarking and performance modelling of data-intensive applications to understand and improve the development of big data systems. Several interesting approaches were proposed; however, as of writing this paper and to the best of our knowledge, there are no comprehensive surveys that thoroughly examine the gaps, trends and trajectories of this area. To fill this void, we, therefore, present a review of the state-of-art benchmarking and performance modelling efforts in data-intensive applications. We start by introducing the two most common dataflow patterns used, for each of these patterns, we review their approach to benchmarking, modelling and validation & experimental environments. Furthermore, we construct a taxonomy and classification to provide a deep understanding of the focus areas of this domain and identify the opportunities for further research. We conclude by analysing each research gap and highlighting future trends.

Modern cluster management systems have been evolving to cope with running and managing diverse cloud applications on heterogeneous computing clusters. Consequently, the system behaviours become complex and non-trivial to explain. In this paper we take the recently published Google trace data set version 3 (V3) as a case study to explore various aspects of intercluster differences. We analyse the distribution of underlying physical machines resource, e.g. number and types of machine, and metrics of computational job requests, e.g. job duration, utilisation and Cycles Per Instruction (CPI). We also apply an unsupervised learning algorithm on the metrics to characterise jobs. Our analysis suggests that the composition of the underlying machine resources in different cells can be substantially different, and the cells with similar machine resource structures can utilise resources differently depending on the characteristics of job requests.

The recent boom of big data, coupled with the challenges of its processing and storage gave rise to the development of distributed data processing and storage paradigms like MapReduce, Spark, and NoSQL databases. With the advent of cloud computing, processing and storing such massive datasets on clusters of machines is now feasible with ease. However, there are limited tools and approaches, which users can rely on to gauge and comprehend the performance of their big data applications deployed locally on clusters, or in the cloud. Researchers have started exploring this area by providing benchmarking suites suitable for big data applications. However, many of these tools are fragmented, complex to deploy and manage, and do not provide transparency with respect to the monetary cost of benchmarking an application. In this paper, we present Plug And Play Bench (PAPB1): an infrastructure aware abstraction built to integrate and simplify the deployment of big data benchmarking tools on clusters of machines. PAPB automates the tedious process of installing, configuring and executing common big data benchmark workloads by containerising the tools and settings based on the underlying cluster deployment framework. Our proof of concept implementation utilises HiBench as the benchmark suite, HDP as the cluster deployment framework and Azure as the cloud platform. The paper further illustrates the inclusion of cost metrics based on the underlying Microsoft Azure cloud platform.