Analysis of Virtualized Congestion Control in Applications Based on Hadoop MapReduce

Among the existing applications for processing massive volumes of data, the Hadoop MapReduce (HMR) is widely used in clouds, having above all internal network flows of different volume and periodicity. In this regard, providers have the challenge of managing data centers with a wide range of operating systems and features. The diversity of algorithms and parameters related to TCP constitutes a heterogeneous communication scenario prone to degradation of communication-intensive applications. Due to total control in the data center, providers can apply the Virtualized Congestion Control (VCC) to generate optimized algorithms. From the tenant’s perspective, virtualization is a transparently performed. Some technologies have made possible to develop such virtualization. Explicit Congestion Notification (ECN) is a technique for congestion identification which acts by monitoring the queues occupancy. Although promising, the specialized literature lacks on a deep analysis of the VCC impact on the applications. Our work characterizes the VCC impact on HMR on scenarios in which there are present applications competing for network resources using optimized and non-optimized TCP stacks. We identified the HMR has its performance substantially influenced by the data volume according to the employed TCP stack. Moreover, we highlight some VCC limitations.