A Machine Learning Auditing Model for Detection of Multi-Tenancy Issues Within Tenant Domain

Cloud computing is intrinsically based on multi-tenancy, which enables a physical host to be shared amongst several tenants (customers). In this context, for several reasons, a cloud provider may overload the physical machine by hosting more tenants that it can adequately handle. In such a case, a tenant may experience application performance issues. However, the tenant is not able to identify the causes, since most cloud providers do not provide performance metrics for customer monitoring, or when they do, the metrics can be biased. This study proposes a two-tier auditing model for the identification of multi-tenancy issues within the tenant domain. Our proposal relies on machine learning techniques fed with application and virtual resource metrics, gathered within the tenant domain, for identifying overloading resources in a distributed application context. The evaluation using Apache Storm as a case study, has shown that our proposal is able to identify a node experiencing multi-tenancy interference of at least 6%, with less than 1% false-positive or false-negative rates, regardless of the affected resource. Nonetheless, our model was able to generalize the multi-tenancy interference behavior based on private cloud testbed monitoring, for different hardware configurations. Thus, a system administrator can monitor an application in a public cloud provider, without possessing any hardware-level performance metrics.