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学术报告

发布时间: 2017-09-14 09:22 访问人数:


时间:2017 年 9 月 13 日 10:30-11:30
地点:北一楼 121
报告人及报告简介:
李鹏,日本会津大学计算机理工学部副教授。长期从事网络技术, 移动云计算和大数据的研究和相关系统的开发。近年来先后在 IEEE Transactions 等国际权威杂志和会议上发表论文 60 余篇。曾于 2014 年,凭借其突出的研究成果,获得 IEEE Computer Society Japan Chapter颁发的“年轻论文作者奖(YoungAuthorAward)”。担任过多 个国际会议(IEEE ICC, IEEE GLOBECOM)的技术委员。

Title:Networking for Big Data: Traffic-aware Geo-distributed Big Data Analytics
Abstract:Big data generated from everything around us at an unprecedented velocity, volume and variety is changing the way we sense the world. Big data analytics has shown great potential in decision making, optimizing operations, preventing threats and capitalizing on new sources of revenues in various fields such as manufacturing, healthcare, insurance, and retail. To harness the power of big data, many research efforts have been made to develop new data programming models, e.g., MapReduce, and enhance data processing infrastructure from aspects of computation, storage and network. This talk will cover the most recent research results that address the challenges of networking for big data. First, a traffic-aware aggregation architecture will be studied for a single cluster. The all-to-all data forwarding from map tasks to reduce tasks in the traditional MapReduce framework would generate a large amount of network traffic. An aggregation architecture will be designed under the existing MapReduce framework with the objective of minimizing the data traffic during the shuffle phase. Second, for multiple clusters, a novel data-centric architecture with three key techniques, namely, cross-cloud virtual cluster, data-centric job placement, and network coding based traffic routing, will be studied. This design leads to an optimization framework with the objective of minimizing both computation and transmission cost for running a set of MapReduce jobs in geo-distributed clouds.

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