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Ernest is a performance prediction framework for analytics jobs developed using frameworks like Apache Spark and run on cloud computing infrastructure.

One of the main challenges in deploying large scale analytics applications in the cloud is choosing the right hardware configuration. Specifically in Amazon EC2 or Google Compute Engine clusters, choosing the right instance type and the right number of instances can significantly improve performance or lower cost.

Ernest is a performance prediction framework that helps address this problem. Ernest builds performance models based on the behavior of the job on small samples of data and then predicts its performance on larger datasets and cluster sizes. To minimize the time and resources spent in building a model, Ernest uses optimal experiment design, a statistical technique that allows us to collect as few training points as required. For more details please see our [paper] (http://shivaram.org/publications/ernest-nsdi.pdf) and talk slides from NSDI 2016.

The easiest way to install Ernest is by cloning this repository.

Running Ernest requires installing SciPy, NumPy and CVXPY. An easy way to do this is using the requirements.txt file.

pip install -r requirements.txt

At a high level there are three main steps to use Ernest as summarized in the following figure.

These include:

1. Determining what sample data points to collect. To do this we will be using experiment design implemented in expt_design.py. This will return the set of training data points required to build a performance model.
2. Collect running time for the set of training data points. These can be executed using Spark EC2 scripts or Amazon EMR etc.
3. Building a performance model and using it for prediction. To do this we create a CSV file with measurements from previous step and use predictor.py.

For a more detailed example you can see our example on building a performance model for Spark MLlib algorithms.

One of the key insights that is used by Ernest is that a number of machine learning workloads are iterative in nature and have predictable structure in terms of computation and communication. Thus we are able to run a few iterations of the job on small samples of data to build a performance model. However this assumption may not be valid for all workloads.

Further, to compare across instance types, we currently need to build a separate model for each instance type. We are working on developing new techniques to share performance models across instance types.