Adaptive Preference-Aware Co-Location for Improving Resource Utilization of Power Constrained Datacenters

Abstract

Large-scale datacenters often host latency-sensitive services that have stringent Quality-of-Service requirement and experience diurnal load pattern. Co-locating best-effort applications that have no QoS requirement with the latency-sensitive services has been widely used to improve the resource utilization of datacenters with careful shared resource management. However, existing co-location techniques tend to result in the power overload problem on power constrained servers due to the ignorance of the power consumption. To this end, we propose Sturgeon, a runtime system proactively manages resources between co-located applications in a power constrained environment, to ensure the QoS of latency-sensitive services while maximizing the throughput of best-effort applications. Our investigation shows that, at a given load, there are multiple feasible resource configurations to meet both QoS requirement and power budget, while one of them yields the maximum throughput of best-effort applications. To find such a configuration, we establish models to accurately predict the performance and power consumption of the co-located applications. Sturgeon monitors the QoS of the services periodically, in order to eliminate the potential QoS violation caused by the unpredictable interference. Besides, when the datacenter hosts different types of applications to perform co-location, Sturgeon places applications with their preferable candidates to improve the overall throughput. The experimental results show that at server level Sturgeon improves the throughput of the best-effort application by 25.43 percent compared to the state-of-the-art technique, while guaranteeing the 95%-ile latency within the QoS target; at cluster level, Sturgeon improves the overall throughput of best-effort applications by 13.74 percent compared to the baseline.