In IaaS platforms, hardware infrastructures are sliced into multiple virtual machines (VMs) to provide computing capabilities for users. Virtualization greatly improves the resource utilization, however it introduces potential risk of variation in VM performance. VMs co-located together have a high probability of performance degradation when one of the VMs behaves as a noisy neighbor competing hardware resource with other victims. How to efficiently monitor and quantify this type of performance interference thus becomes a key challenge for IaaS providers. To address these challenges, this study presents an approach which transparently monitors and quantifies VMs interferences through low-level metrics with hardware performance counters (HPCs). The approach explores the information within HPC and LLC miss rates, builds performance prediction model and quantifies performance interference of different (CPU-bound and net-bound) VMs. Experimental results show that the proposed approach can predict the performance degradation effectively with an acceptable overhead that is lower than 10%.