Application-Level CPU Consumption Estimation: Towards Performance Isolation of Multi-tenancy Web Applications

Abstract

Performance isolation is a key requirement for application-level multi-tenant sharing hosting environments. It requires knowledge of the resource consumption of the various tenants. It is of great importance not only to be aware of the resource consumption of a tenant's given kind of transaction mix, but also to be able to be aware of the resource consumption of a given transaction type. However, direct measurement of CPU resource consumption requires instrumentation and incurs overhead. Recently, regression analysis has been applied to indirectly approximate resource consumption, but challenges still remain for cases with non-determinism and multicollinearity. In this work, we adapts Kalman filter to estimate CPU consumptions from easily observed data. We also propose techniques to deal with the non-determinism and the multicollinearity issues. Experimental results show that estimation results are in agreement with the corresponding measurements with acceptable estimation errors, especially with appropriately tuned filter settings taken into account. Experiments also demonstrate the utility of the approach in avoiding performance interference and CPU overloading.