云数据中心采用负载整合的方式，即将多个应用负载放置在一个物理计算单元中运行，通过为应用负载分配逻辑计算资源的方式共享物理计算资源，以期达到提高数据中心的资源利用率以及降低投资与运行成本的目的。当计算任务增多时，多应用负载对共享计算资源的竞争更加激烈，所产生的性能干扰会导致各应用的性能下降以及负载执行时间的不确定性。本项目针对数据中心多应用负载共同运行时因性能干扰而导致资源利用率低下与应用负载服务质量下降等问题，研究多应用负载间性能干扰的量化与预测方法，以及降低性能干扰的措施与方法。内容包括：多应用性能干扰模型建立以及应用间性能干扰的前摄性检测、性能干扰感知的参数配置调优、系统级性能干扰隔离机制等，为提高数据中心资源利用率，降低成本与能耗提供理论与技术支撑。

Cloud datacenters usually adopt workload co-location strategy, which means place and execute multiple workloads in one single physical computing unit. Thus the physical computing resources can be shared by allocating logical computing resources to the workloads, which achieves the goals of increasing the datacenter resource utilization and reducing the infrastructural and operational cost. However, when the computing tasks grow, the competition among workloads for the shared computing resources will become intense, and the interference among workloads will lead to the low system performance and the uncertainty of execution time. This proposal aims at addressing the low resource utilization problems and the QoS degradation related issues caused by cross-workload interference while multiple workloads running together in datacenter, and will research the cross workload interference quantization and prediction methods, as well as the corresponding interference reduction countermeasures. The detailed research approaches include: the modeling of multi-applications interference and the proactive detection on cross workload interference, the interference-aware system parameter tuning algorithm, and the operating system level performance interference isolation mechanism. The approaches together will provide a theoretical and technical basis to increase the datacenter resource utilization and reduce the infrastructure cost and energy consumption.