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ISTC-CC Abstract
An Infrastructure for Automating Large-scale Performance Studies and Data Processing
IEEE Big Data Conference (IEEE BigData’13), October 2013.
Deepal Jayasinghe, Josh Kimball, Tao Zhu, Siddharth Choudhary, Calton Pu
Georgia Institute of Technology
The Cloud has enabled the computing model to shift from traditional data centers to publicly shared computing infrastructure; yet, applications leveraging this new computing model can experience performance and scalability issues, which arise from the hidden complexities of the cloud. The most reliable path for better understanding these complexities is an empirically based approach that relies on collecting data from a large number of performance studies. Armed with this performance data, we can understand what has happened, why it happened, and more importantly, predict what will happen in the future. However, this approach presents challenges itself, namely in the form of data management. We attempt to mitigate these data challenges by fully automating the performance measurement process. Concretely, we have developed an automated infrastructure, which reduces the complexity of the large-scale performance measurement process by generating all the necessary resources to conduct experiments, to collect and process data and to store and analyze data. In this paper, we focus on the performance data management aspect of our infrastructure.
FULL PAPER: pdf
