报告摘要：
To support multiple on-demand services over fixed communication networks, network operators must allow flexible customization and fast provision of their network resources. One effective approach to this end is network virtualization, whereby each service is mapped to a virtual subnetwork providing dedicated on-demand support to network users. In practice, each service consists of a prespecified sequence of functions, called a service function chain (SFC), while each service function in a SFC can only be provided by some given network nodes. Thus, to support a given service, we must select network function nodes according to the SFC and determine the routing strategy through the function nodes in a specified order. A crucial network slicing problem that needs to be addressed is how to optimally localize the service functions in a physical network as specified by the SFCs, subject to link and node capacity constraints. In this paper, we formulate the network slicing problem as a mixed binary linear program and establish its strong NP-hardness. Furthermore, we propose efficient penalty successive upper bound minimization (PSUM) and PSUM-R(ounding) algorithms, and two heuristic algorithms to solve the problem. Simulation results are shown to demonstrate the effectiveness of the proposed algorithms.
主讲人简介：
罗智泉教授1984年于北京大学获得理学学士学位，1989年于美国麻省理工学院获得理学博士学位。罗智泉教授曾在加拿大麦克马斯特大学任职(1989-2003)，期间曾担任电子与计算机工程系系主任；2003年至2018年他在美国明尼苏达大学任职；从2014年至今，罗智泉教授担任香港中文大学（深圳）副校长。他也是深圳大数据研究院的建院院长。罗智泉教授的研究方向主要包括信号处理领域的优化算法、数据分析及数字通信。
罗智泉教授是加拿大皇家学会会员(FRSC)、IEEE会士、SIAM会士。他曾获得过许多荣誉，包括IEEE信号处理协会最佳论文奖(2004, 2009, 2011)、IEEE信号处理领域最佳杂志论文奖(2015)，运筹与管理研究协会Farkas奖(2010)等。他在许多信号处理和运筹优化领域的优秀期刊担任重要职务，包括IEEE Trans. Signal Processing 期刊主编(2012-2014)，IEEE Signal Processing Magazine(2009-2012) 和 IEEE J. Selected Topics of Signal Processing(2013至今)期刊编委，SIAM J. Optimization(1998-2005)、Mathematics of Operations Research(2007-Present) 和 Management Sciences(2009-Present)编辑等。
此讲座为前沿课题讲座，欢迎全校师生踊跃参加。
教务处
校学术委员会
理学院
2018年6月11日
