A model predictive controller for contention-aware resource allocation in virtualized data centers

Data center efficiency is primarily sought by sharing physical resources, such as processors, memory, and disks in the form of virtual machines or containers among multiple users, i.e., workload consolidation. However, the reality is co-located applications in these virtual platforms compete for resources and interfere with each others' performance, resulting in performance variability/degradation. In this paper, we present the contentionaware resource allocation (CARA) solution, which optimizes data center efficiency. It is essentially devised based on a model predictive control that enables to make judicious consolidation decisions with future system states. CARA consolidates workloads explicitly taking into account the correlation between shared and isolated resource usage patterns. Based on our experimental results, CARA improves the overall resource utilization by 32%, without a significant impact on the quality-of-service (QoS) enforcement level. Such improvement results in a fewer number of active servers and in turn contributes to an overall energy saving by 33%.