listed in Table \[table:runningex\]. To facilitate the generation of the values from YARN logs we provided Java and R scripts available on our Github page.

Experiment and Evaluation {#experimentandevaluation}
=========================

**Setup**
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In order to gauge the applicability of our approach on an arbitrary setup, we conducted two sets of experiments on two different hardware setups. On the first setup, we used a single node YARN cluster with 32GB memory and 8 CPUs. YARN was allocated 24GB and a minimum of 3GB per container In the second setup, we used an 8-node in-house YARN cluster to mimic a real-world deployment scenario. Each of these nodes has 8GB of RAM, 8 vCPUs and a 500GB of storage space. YARN is allocated 48GB of memory, and a maximum of 8 containers can run at a time. From the results of the two setups for the experiments, we have a strong case to conclude that, the approach can be used to profile an arbitrary cluster size. In all the experiments, we have adapted the algorithms discussed in the design patterns book [@Miner:2012:MDP:2632825] and used the same $StackOverflow$ data source but a more updated version. Table \[table:algorithms\] summarises all the workloads and their data-sizes 