论文标题
距离对混合云中分布式数据库系统的性能和可扩展性的影响
The Impact of Distance on Performance and Scalability of Distributed Database Systems in Hybrid Clouds
论文作者
论文摘要
管理大数据的越来越多的需求导致了高级数据库管理系统的出现。旨在评估私人或公共云数据中心托管的NOSQL和关系数据库的性能和可扩展性的努力。但是，在评估混合云中这些数据库的性能和可伸缩性方面几乎没有工作，在混合云中，私人和公共云数据中心之间的距离可能是影响其性能的关键因素之一。因此，在本文中，我们对混合云中六个现代数据库的吞吐量，可伸缩性和VMS大小与VMS编号进行了详细评估，其中包括阿德莱德和基于Azure的数据中心的私人云，位于悉尼，孟买和弗吉尼亚州。根据结果，随着私有云和公共云之间的距离增加，大多数数据库的吞吐量性能会降低。其次，MongoDB获得了最佳的吞吐性能，其次是MySQL C光泽，而Cassandra则通过性能暴露了最波动的。第三，垂直可伸缩性比水平可扩展性更高的数据库吞吐量。第四，利用更大的VM而不是具有更少核心的VM可以提高Cassandra，Riak和Redis的吞吐量。
The increasing need for managing big data has led the emergence of advanced database management systems. There has been increased efforts aimed at evaluating the performance and scalability of NoSQL and Relational databases hosted by either private or public cloud datacenters. However, there has been little work on evaluating the performance and scalability of these databases in hybrid clouds, where the distance between private and public cloud datacenters can be one of the key factors that can affect their performance. Hence, in this paper, we present a detailed evaluation of throughput, scalability, and VMs size vs. VMs number for six modern databases in a hybrid cloud, consisting of a private cloud in Adelaide and Azure based datacenter in Sydney, Mumbai, and Virginia regions. Based on results, as the distance between private and public clouds increases, the throughput performance of most databases reduces. Second, MongoDB obtains the best throughput performance, followed by MySQL C luster, whilst Cassandra exposes the most fluctuation in through performance. Third, vertical scalability improves the throughput of databases more than the horizontal scalability. Forth, exploiting bigger VMs rather than more VMs with less cores can increase throughput performance for Cassandra, Riak, and Redis.
