AggNet: Cost-Aware Aggregation Networks for Geo-distributed Streaming Analytics

Dhruv Kumar, Sohaib Ahmad, A. Chandra
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引用次数: 8

Abstract

Large-scale real-time analytics services continuously collect and analyze data from end-user applications and devices distributed around the globe. Such analytics requires data to be transferred over the wide-area network (WAN) to data centers (DCs) capable of processing the data. Since WAN bandwidth is expensive and scarce, it is beneficial to reduce WAN traffic by partially aggregating the data closer to end-users. We propose aggregation networks for performing aggregation on a geo-distributed edge-cloud infrastructure consisting of edge servers, transit and destination DCs. We identify a rich set of research questions aimed at reducing the traffic costs in an aggregation network. We present an optimization formulation for solving these questions in a principled manner, and use insights from the optimization solutions to propose an efficient, near-optimal practical heuristic. We implement the heuristic in AggNet, built on top of Apache Flink. We evaluate our approach using a geo-distributed deployment on Amazon EC2 as well as a WAN-emulated local testbed. Our evaluation using real-world traces from Twitter and Akamai shows that our approach is able to achieve 47% to 83% reduction in traffic cost over existing baselines without any compromise in timeliness.
AggNet:用于地理分布流分析的成本感知聚合网络
大规模实时分析服务持续收集和分析分布在全球各地的终端用户应用程序和设备的数据。这种分析需要将数据通过广域网(WAN)传输到能够处理数据的数据中心(dc)。由于广域网带宽昂贵且稀缺,因此通过在靠近最终用户的地方部分聚合数据来减少广域网流量是有益的。我们提出聚合网络,用于在由边缘服务器、传输数据中心和目标数据中心组成的地理分布式边缘云基础设施上执行聚合。我们确定了一套丰富的研究问题,旨在降低聚合网络中的流量成本。我们提出了一个优化公式,以原则性的方式解决这些问题,并利用优化解决方案的见解来提出一个有效的、接近最优的实用启发式方法。我们在AggNet中实现了启发式算法,AggNet建立在Apache Flink之上。我们使用Amazon EC2上的地理分布式部署以及wan模拟的本地测试平台来评估我们的方法。我们使用来自Twitter和Akamai的真实世界痕迹进行评估,结果表明,我们的方法能够在不影响及时性的情况下,将现有基线的流量成本降低47%至83%。
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