Authors (if Research)
Pradeep Padala, Xiaoyun Zhu, et al.
Proceedings of the EuroSys 2009
Virtualized data centers enable sharing of resources among
hosted applications. However, it is difficult to satisfy servicelevel
objectives (SLOs) of applications on shared infrastructure,
as application workloads and resource consumption
patterns change over time. In this paper, we present
AutoControl, a resource control system that automatically
adapts to dynamic workload changes to achieve application
SLOs. AutoControl is a combination of an online model estimator
and a novel multi-input, multi-output (MIMO) resource
controller. The model estimator captures the complex
relationship between application performance and resource
allocations, while the MIMO controller allocates the
right amount of multiple virtualized resources to achieve application
SLOs. Our experimental evaluation with RUBiS
and TPC-W benchmarks along with production-trace-driven
workloads indicates that AutoControl can detect and mitigate
CPU and disk I/O bottlenecks that occur over time and
across multiple nodes by allocating each resource accordingly.
We also show that AutoControl can be used to provide
service differentiation according to the application priorities
during resource contention.