Bubble-Up: Increasing Utilization In Modern Warehouse Scale Computers Via Sensible Co-Locations
Proceedings of the 44th Annual IEEE/ACM International Symposium on Microarchitecture, 2011, IEEE, New York, NY, USA
Jason Mars, Linjia Tang, Robert Hundt, Kevin Skadron, Mary Lou Souffa
In this paper, we present Bubble-Up, a characterization methodology that enables the accurate prediction of the performance degradation that results from contention for shared resources in the memory subsystem. By using a bubble to apply a tunable amount of “pressure” to the memory subsystem on processors in production datacenters, our methodology can predict the performance interference between co-locate applications with an accuracy within 1% to 2% of the actual performance degradation. Using this methodology to arrive at “sensible” co-locations in Google’s production datacenters with real-world large-scale applications, we can improve the utilization of a 500-machine cluster by 50% to 90% while guaranteeing a high quality of service of latency-sensitive applications.