It is imperative for public cloud providers to guarantee performance targets for tenants' virtual machines (VMs) while respecting strict business confidentiality, e.g., having no information on applications nor their performance. A large body of related work addresses the challenges of detecting performance interferences by leveraging client's quality of service (QoS) metrics, e.g., latency, and additional profiling servers. In this paper, we take the perspective of the cloud provider and propose a general black-box approach that detects different resource contentions by throttling neighboring VMs. Specifically, we design a three-phase detection algorithm that includes: (i) an alarm phase to identify statistical outliers using control charts; (ii) a passive clustering phase to match the current sample to historical behaviors; and (iii) an active throttling phase to discern contentions from application phase changes via throttling. The algorithm is specifically designed for scenarios where multiple co-located VMs request detection analysis simultaneously. We implement and evaluate the proposed three-phase algorithm on four latency sensitive applications, i.e., Wikimedia and three benchmarks from Cloudsuite. Our extensive experimental results show that we can reach an average detection accuracy above 90 percent while limiting the performance degradation experienced by offender workloads to short learning phases.

Visualization technology powers up the cloud computing paradigm and inevitably raises concerns about performance isolation of collocated virtual machines (VM). It is imperative for public cloud providers to guarantee performance targets for tenants' VMs while respecting strict business confidentiality, e.g., having no information on applications nor their performance. A large body of related work addresses the challenges of detecting performance interferences by leveraging client's quality of service (QoS) metrics, such as latency, and additional profiling servers. Whereas to assist cloud providers, we resort to an on-line blackbox approach based on throttling that detects a wide range of resource contentions with no cooperation need from the virtual machines. We focus on different resource metrics and actively monitor them from the hypervisor in fine time granularity at low cost. To detect resource contention, we propose a three-phase algorithm: an alarm phase, to identify statistical outliers in the victim's VM resource metrics; a passive diagnosis phase, to match the current sample to historical behaviors; and, an active learning phase, to discern contentions from application phase changes via throttling. We evaluate our algorithm on a prototype running Wikimedia as victim application across a set of VMs collocated with neighboring VMs running resource hoggers, i.e. PARSEC and Cachebench. Our extensive experimental results show that we can reach an average detection accuracy above 90% while limiting the performance degradation experienced by offender workloads to short learning phases.