Intelligent Colocation of Workloads for Enhanced Server Efficiency

Many server applications achieve only a fraction of their theoretical peak performance due to bottlenecks in the shared caches, instruction execution units, I/O or memory bandwidth, even though the remaining resources may be underutilized. It is very hard for developers and runtime systems to ensure that all these critical resources are fully exploited by a single application. An attractive technique for increasing server system utilization is to colocate multiple applications on the same server. When applications share critical resources, however, these applications may adversely affect each other, due to contention on the shared resources. In this paper, we show that server efficiency can be improved by modeling the expected performance degradation of colocated applications from measured hardware performance counters, and exploiting such a model to determine an optimized mix of colocated applications. This paper presents a novel resource management approach and makes the following contributions: (1) a new machine learning model to predict the performance degradation of colocated applications from hardware counters and (2) an intelligent scheduling scheme deployed on an existing resource manager to enable application co-scheduling with minimum performance degradation. Our results show that our approach achieves performance improvements of 15 % (avg) and 26 % (max) compared to the standard policy commonly used by existing job managers.