The Laboratory of Advanced Research on Cloud Computing had 2 articles accepted at the 23rd IEEE Symposium on Computers and Communications 2018 (ISCC 2018), which will be held on 25-28 June in Natal, Brazil. The results obtained come from original research developed by the research group as well as from cooperation with partner institutions. The works, their authors, as well as their abstract will be described below.

Evaluating and estimating, and Improving Network Performance in Container-based Clouds

The paper “Evaluating and estimating, and Improving Network Performance in Container-based Clouds”, is authored by MSc. Cassiano Rista (PUCRS), Dr. Marcelo Teixera (UTFPR), Dr. Dalvan Griebler (PUCRS/SETREM) and Dr. Luiz Gustavo Fernandes (PUCRS).

Abstract

Cloud computing has recently attracted a great deal of interest from both industry and academia, emerging as an important paradigm to improve resource utilization, efficiency, flexibility, and pay-per-use. However, cloud platforms inherently include a virtualization layer that imposes performance degradation on network-intensive applications. Thus, it is crucial to anticipate possible performance degradation to resolve system bottlenecks. This paper uses the Petri Nets approach to create different models for evaluating, estimating, and improving network performance in container-based cloud environments. Based on model estimations, we assessed the network bandwidth utilization of the system under different setups. Then, by identifying possible bottlenecks, we show how the system could be modified to improve performance. We then tested how the model would behave through real-world experiments. When the model indicates probable bandwidth saturation, we propose a link aggregation approach to increase bandwidth, using lightweight virtualization to reduce virtualization overhead. Results reveal that our model anticipates the structural and behavioral characteristics of the network in the cloud environment. Therefore, it systematically improves network efficiency, which saves effort, time, and money.

Performance of Data Mining, Media, and Financial Applications under Private Cloud Conditions

The paper “Performance of Data Mining, Media, and Financial Applications under Private Cloud Conditions” is authored by Dr. Dalvan Griebler (PUCRS/SETREM), MSc. Adriano Vogel (PUCRS), MSc. Carlos A. F. Maron (PUCRS), Tecn⁰ Anderson M. Maliszewski (SETREM), Dr. Claudio Schepke (UNIPAMPA) and Dr. Luiz Gustavo Fernandes (PUCRS).

Abstract

This paper contributes to a performance analysis of real-world workloads under private cloud conditions. We selected six benchmarks from PARSEC related to three mainstream application domains (financial, data mining, and media processing). Our goal was to evaluate these application domains in different cloud instances and deployment environments, concerning container or kernel-based instances and using dedicated or shared machine resources. Experiments have shown that performance varies according to the application characteristics, virtualization technology, and cloud environment. Results highlighted that financial, data mining, and media processing applications running in the LXC instances tend to outperform KVM when there is a dedicated machine resource environment. However, when two instances are sharing the same machine resources, these applications tend to achieve better performance in the KVM instances. Finally, financial applications achieved better performance in the cloud than media and data mining.