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

跨地理分布式数据中心管理资源以进行大数据分析和移动云应用程序

• 批准号: RGPIN-2016-06281
• 负责人: Li, Baochun
• 金额: $ 4.74万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637341
• 关键词: Managing; Resources; across; Geo; Distributed

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.

大数据分析和移动云应用程序都越来越多地分布在地理上，这取决于世界各地的多个数据中心的服务。然而，广阔的地区间链接的构建和升级成本高昂，而跨境的带宽必定会在不久的将来成为稀缺的资源。传统的数据分析需要首先将所有原始数据移至一个数据中心，然后才能处理它们，从而对其性能产生不利影响，并且是带宽密集的。另一方面，实时流媒体分析和交互式移动云应用程序（例如视频会议和消息传递）需要低潜伏期。因此，有迫切需要以精细的粒度管理和分配数据间资源，以满足来自数据分析和移动云应用程序的各种性能目标。