Analysis of Congestion Control Virtualization on Execution of Hadoop MapReduce Application
ResumoCloud providers host multiple virtual machines (VMs) configured based on specific versions of operating systems and libraries. The diversity of algorithms and parameters related to TCP constitutes a heterogeneous communication scenario. Legacy congestion control algorithms compromise the performance of VM-hosted applications. Due to total control in the data center, providers can apply the virtualization of congestion control (VCC) to generate optimized algorithms. From the tenant’s perspective, virtualization is a transparently performed. Although promising, the application of VCC requires a deep analysis of the impact on the final applications. Thus, the present work dissects the execution of Hadoop MapReduce atop VCC-based scenarios. The experimental analysis discusses the execution time of Hadoop MapReduce and the behavior of intermediate switches queues, highlighting some VCC limitations.
Palavras-chave: Data centers, Monitoring, Temperature sensors, Temperature measurement, Conferences, Temperature distribution, Computational fluid dynamics, Virtual Machine (VM), Cloud Computing, Hadoop MapReduce (HMR)
MORO, Vilson; PILLON, Maurício Aronne; MIERS, Charles Christian; KOSLOVSKI, Guilherme Piêgas. Analysis of Congestion Control Virtualization on Execution of Hadoop MapReduce Application. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (WSCAD), 19. , 2018, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 93-93.