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都越来越依赖于来自世界各地多个数据中心的服务，在地理上越来越分散。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，并且占用大量带宽。另一方面，实时流分析和交互式移动云应用(如视频会议和消息传递)需要较低的延迟。因此，迫切需要细粒度地管理和分配数据中心间的资源，以满足数据分析和移动云应用程序的不同性能目标。*在本计划中，我们建议设计和实施一个新的软件定义的数据中心间基础设施，以优化数据中心间带宽的利用。我们的基础设施在应用层运行，提供底层和公共应用编程接口(API)，以支持现有数据分析框架和交互式移动云应用。它以精细的粒度调度数据分析任务、路由数据流并管理应用程序流的带宽分配，目的是以最佳效率利用稀缺的数据中心间带宽。遵循软件定义的网络原则，我们的基础设施完全解耦控制平面和数据平面，并基于对网络状况的实时分析来做出任务调度、流量路由和流量工程决策。*我们将在控制器中提出新的算法来做出这些决策，以满足每一类应用的特定需求。对于批处理和流分析应用，我们的新算法旨在优化多个数据中心的任务调度和分配，以便在不预先了解数据分析作业的通信模式的情况下，优化数据中心间流量的数量和延迟。对于带宽密集型和延迟敏感的交互式移动云应用，我们的算法旨在根据竞争应用流的延迟敏感度来优化资源分配。为了更具表现力，我们的基础设施使用可执行脚本在数据平面中实施控制平面决策。我们计划在涉及全球多个数据中心的真实世界数据中心间试验台中实施我们的新基础设施。该计划将带来使用世界各地的大量数据中心来支持大数据分析和移动应用程序的全新方式，这两者将很快成为加拿大企业和消费者不可或缺的关键要素。**

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.**