A decentralized approach for adaptive workload estimation in virtualized environments

Cloud computing is gaining an important role in providing high quality IT services. However, the heterogeneous and dynamic nature of the activities it hosts makes the related management operations, serving performance or security purposes, complexes. Leveraging the autonomic paradigm, represents a promising solution but it requires efficient grounded monitoring and analysis functions which can in turn implement advanced control algorithms. In this effort, this paper presents a robust and cost effective solution to monitor and estimate the workload in a virtualized environment. It consists in a decentralized algorithm leveraging an incremental Principal Component Analysis (PCA) featuring the system activity of multi-tenants execution environments. To evaluate the relevance of our proposal in terms of both performance and cost, we consider real execution traces of more than one thousand PlanetLab containers hosted on more than forty servers belonging to more than one hundred tenants.