报 告 人：李 鹏 副教授 (日本会津大学)

报告时间：11月4日（周六）9:30～10:30

报告地点：校本部东区计算机大楼402室

邀 请 人：童维勤 教授

报告摘要：

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.

报告人简介:

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。近年来在国际权威杂志（TPDS, JSAC, TC）和会议（INFOCOM, ICDCS, MM）上发表论文70余篇。其研究成果被多位IEEE Fellow引用。 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(Young Author Award)”。2016年，指导学生获得IEEE学生竞赛的一等奖（First Prize of IEEE Communication Society Student Competition）。担任过多个国际会议的技术委员。