Analysis of a Network IO Bottleneck in Big Data Environments Based on Docker Containers

• Published in: Big data research Vol. 3; pp. 24 - 28
• Main Authors: China Venkanna Varma, P., K., Venkata Kalyan Chakravarthy, Valli Kumari, V., Viswanadha Raju, S.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.04.2016
• Subjects: Containers; Context switching; Docker; Hadoop; Map reduce; Containers; Context switching; Docker; Map reduce; Hadoop
• ISSN: 2214-5796; 2214-580X
• DOI: 10.1016/j.bdr.2015.12.002

We live in a world increasingly driven by data with more information about individuals, companies and governments available than ever before. Now, every business is powered by Information Technology and generating Big data. Future Business Intelligence can be extracted from the big data. NoSQL [1] and Map-Reduce [2] technologies find an efficient way to store, organize and process the big data using Virtualization and Linux Container (a.k.a. Container) [3] technologies. Provisioning containers on top of virtual machines is a better model for high resource utilization. As the more containers share the same CPU, the context switch latency for each container increases significantly. Such increase leads to a negative impact on the network IO throughput and creates a bottleneck in the big data environments. As part of this paper, we studied container networking and various factors of context switch latency. We evaluate Hadoop benchmarks [5] against the number of containers and virtual machines. We observed a bottleneck where Hadoop [4] cluster throughput is not linear with the number of nodes sharing the same CPU. This bottleneck is due to virtual network layers which adds a significant delay to Round Trip Time (RTT) of data packets. Future work of this paper can be extended to analyze the practical implications of virtual network layers and a solution to improve the performance of big data environments based on containers.