In the era of global-scale services, organisations produce huge volumes of data, often distributed across multiple data centres, separated by vast geographical distances. While cluster computing applications, such as MapReduce and Spark, have been widely deployed in data centres to support commercial applications and scientific research, they are not designed for running jobs across geo-distributed data centres. The necessity to utilise such infrastructure introduces new challenges in the data analytics process due to bandwidth limitations of the inter-data-centre communication. In this article, we discuss challenges and survey the latest geo-distributed big-data analytics frameworks and schedulers (based on MapReduce and Spark) with WAN-bandwidth awareness.

在全球服务时代，组织会产生大量数据，这些数据通常分布在多个数据中心中，并且地理距离遥远。虽然集群计算应用程序（例如MapReduce和Spark）已广泛部署在数据中心中以支持商业应用程序和科学研究，但它们并不是为在地理分布的数据中心中运行作业而设计的。由于数据中心间通信的带宽限制，利用这种基础设施的必要性在数据分析过程中带来了新的挑战。在本文中，我们讨论了挑战并调查了具有WAN带宽意识的最新地理分布的大数据分析框架和调度程序（基于MapReduce和Spark）。