Predicting intermediate storage performance for workflow applications

System configuration decisions for I/O-intensive workflow applications can be complex even for expert users. Users face decisions to configure several parameters optimally (e.g., replication level, chunk size, number of storage node) - each having an impact on overall application performance. This paper presents our progress on addressing the problem of supporting storage system configuration decisions for workflow applications. Our approach accelerates the exploration of the configuration space based on a low-cost performance predictor that estimates turn-around time of a workflow application in a given setup. Our evaluation shows that the predictor is effective in identifying the desired system configuration, and it is lightweight using 2000-5000× less resources (machines × time) than running the actual benchmarks.