ESP: A Statistical Approach to Predicting Application Interference

Nikita Mishra; John D Lafferty; Henry Hoffmann. 20 September, 2016.
Communicated by Henry Hoffmann.


Independent applications co-scheduled on the same hardware will interfere with one another. Predicting this interference is a key step in scheduling independent applications in large distributed systems. In this paper we study several state-of-the-art regression models for estimating application interference. We find that linear models produce low-accuracy prediction, while interaction (i.e., non-linear) models require so many input features that their usage becomes impractical. We therefore propose ESP, a hybrid regression model that uses linear techniques to select a smaller set of input features, and then uses non-linear techniques to estimate application interference from these features. The result is a highly accurate and practical regression model for application interference estimation. To demonstrate this practicality, we implement ESP and integrate it into a scheduler for both single and multi-node Linux/x86 systems and compare the scheduling performance to state-of-the-art heuristics for scheduling to avoid interference. We find that ESP-based schedulers are 1.25-1.8◊ faster than these heuristics depending on the scheduling scenario. Additionally, we find that ESPís accurate interference predictions allow schedulers to avoid catastrophic decisions which heuristic approaches fundamentally cannot detect.

Original Document

The original document is available in PDF (uploaded 20 September, 2016 by Henry Hoffmann).