A Correlation-based Methodology to Infer Communication Patterns between Cloud Virtual Machines

The VMs allocation over the servers of a cloud data center is becoming a critical task to guarantee energy savings and high performance. Only recently network-aware techniques for VMs allocation have been proposed. However, a network-aware placement requires the knowledge of data transfer patterns between VMs, so that VMs exchanging significant amount of information can be placed on low cost communication paths (e.g. on the same server). The knowledge of this information is not easy to obtain unless a specialized monitoring function is deployed over the data center infrastructure. In this paper, we propose a correlation-based methodology that aims to infer communication patterns starting from the network traffic time series of each VM without relaying on a special purpose monitoring. Our study focuses on the case where a data center hosts a multi-tier application deployed using horizontal replication. This typical case of application deployment makes particularly challenging the identification of VMs communications because the traffic patterns are similar in every VM belonging to the same application tier. In the evaluation of the proposed methodology, we compare different correlation indexes and we consider different time granularities for the monitoring of network traffic. Our study demonstrates the feasibility of the proposed approach, that can identify which VMs are interacting among themselves even in the challenging scenario considered in our experiments.