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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=616208
• 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. In this program, we propose to design and implement a new software-defined inter-datacenter infrastructure, in order to optimize the utilization of inter-datacenter bandwidth. Operating in the application layer, our infrastructure provides a substrate and a public application programming interface (API) to support existing data analytic frameworks and interactive mobile cloud applications. It schedules data analytic tasks, routes data streams, and manages bandwidth allocations to application flows at a fine granularity, with the objective of utilizing scarce inter-datacenter bandwidth with optimal efficiency. Following the principles of software-defined networking, our infrastructure fully decouples the control and data planes, and makes task scheduling, flow routing, and traffic engineering decisions based on live analytics of network conditions. We will propose new algorithms to make these decisions in the controller, catering for the specific needs of each category of applications. For batch and streaming analytics applications, our new algorithms are designed to optimize task scheduling and assignment in multiple datacenters, so that both the volume and latency of inter-datacenter traffic can be optimized without prior knowledge of the communication patterns of data analytics jobs. For interactive mobile cloud applications that are bandwidth-intensive and latency-sensitive, our algorithms are designed to optimize resource allocations according to the delay sensitivity of competing application flows. To be more expressive, our infrastructure uses executable scripts to implement control plane decisions in the data plane. We plan to implement our new infrastructure in a real-world inter-datacenter testbed involving multiple datacenters worldwide. This program will lead to fundamentally new ways of using a large number of datacenters around the world to support big data analytics and mobile applications, both of which will soon to be critical and indispensable to businesses and consumers alike in Canada.

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。