Automatic Workload Evaluation (AWE): Predicting Web 2.0 Workload Behavior

  • Kristal Sauer | UC Berkeley

The aim of this project is to use statistical machine learning to predict a system’s performance and resource utilization under changes to the workload or underlying hardware. This could be useful in many scenarios. For instance, in the web service domain, when a company wants to promote a feature of their application and thereby shift the distribution of requests towards a certain type, they would like to have a sense for the resulting performance. Also, when it comes time to upgrade their servers, they would like to predict the system’s behavior on the new hardware. This work is inspired by a study done by Ganapathi et al. (ICDE ’09) in which this approach was utilized for predicting query runtime and resource consumption; the contribution of this work is the application of the predictive framework to the web service domain via analysis of a new Web 2.0 social networking benchmark application called Cloudstone. In previous experiments, we validated that our representation of the input allowed us to recreate the CPU utilization of the system; in this phase of the project, we focus on predicting the performance and resource utilization of a given workload. In this talk, I will describe our framework and present experimental results.

Speaker Details

Kristal Sauer is a second-year graduate student working with David Patterson, Armando Fox, and Michael Jordan in the RAD Lab at UC Berkeley, where she has been awarded the NSF fellowship as well as the Berkeley Chancellor’s Fellowship. She earned BS degrees in Electrical Engineering and Computer Science at the University of Nevada, Las Vegas in 2007. Her research interests include applying statistical machine learning to tackling hard systems problems, especially those related to datacenters. She also enjoys cooking and traveling overseas.

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