Managing Resources across Geo-Distributed Datacenters for Big Data Analytics and Mobile Cloud Applications

大数据分析和移动云应用越来越多地在地理上分布，依赖于全球多个数据中心的服务。然而，广域数据中心间链路的建设和升级成本高昂，数据中心间带宽在不久的将来必然会成为稀缺资源。传统的数据分析需要首先将所有原始数据移动到一个数据中心，然后才能对其进行处理，这会对其性能产生不利影响，并且是带宽密集型的。另一方面，实时流分析和交互式移动云应用程序（如视频会议和消息传递）需要低延迟。因此，迫切需要以精细的粒度管理和分配数据中心间的资源，以满足来自数据分析和移动云应用程序的各种性能目标。

Both big data analytics and mobile cloud applications are increasingly geographically distributed, relying upon services from multiple datacenters around the world. Yet, wide-area inter-datacenter links are costly to build and to upgrade, and inter-datacenter bandwidth is bound to become a scarce resource in the near future. Traditional data analytics need to first move all raw data to one datacenter before they can be processed, which adversely affect their performance and are bandwidth intensive. Real-time streaming analytics and interactive mobile cloud applications such as video conferencing and messaging, on the other hand, require low latencies. As such, there is a pressing need to manage and allocate inter-datacenter resources at a fine granularity to meet the diverse performance targets from both data analytics and mobile cloud applications.