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【数学之美-杰出学者讲坛】2024年第3期 ||Optimal transport and Monge-Ampere equ...
好莱坞数据专家和制作逻辑的数据重塑
长视频平台趋势与洞察
清华大学材料科学与工程研究院《材料科学论坛》:机器学习辅助合金理性设计
报告题目:
Leveraging “Big” Data Analytics for Network Performance Monitoring & Trouble-Shooting
 报告人:
Zhi-Li Zhang
the Qwest Chair Professor in Telecommunications  and 
Distinguished McKnight University Professor, Dept. of 
Computer Science & Engineering, University of Minnesota
报告时间:
2016-10-27 09:30
报告地点:
电子工程馆(罗姆楼)8-206会议室
主办单位:
电子工程系
  简介:
ABSTRACT: As we become increasingly reliant on a variety of large-scale Internet services for our daily activities, providing as good a quality-of-experience (QoE) as possible to users become imperative. In my first case study, using large amount of datasets collected from various sources over time across a large US 3G UMTS  network carrier,  we briefly discuss how to identify the key factors that influence the network performance in terms of the round-trip times and loss rates (averaged over an hourly time scale). We apply RuleFit – a powerful supervised machine learning tool that combines linear regression and decision trees – to develop models and analyze the relative importance of various factors in estimating and predicting the network performance. Our analysis culminates with the detection and diagnosis of both “transient” and “persistent” performance anomalies, with discussion on the complex interactions and differing effects of the various factors that may influence the 3G UMTS network performance.
In the second case study, we provide analysis of web search response times from a service provider's perspective. Using measurement and instrumentation data from Microsoft Bing, we show that search response time (SRT) varies widely over time and also exhibits counter-intuitive behavior. To resolve this paradox and explain SRT variations in general, we develop an analytics framework that separates systemic variations due to periodic changes in service usage and anomalous variations due to unanticipated events such as failures and denial-of-service attacks. Deployment experience shows that our technique detects three times more true (operator-verified) anomalies than existing techniques.
 
BIOGRAPHY: Zhi-Li Zhang received the B.S. degree in computer science from Nanjing University, China and his M.S. and Ph.D. degrees in computer science from the University of Massachusetts.  He joined the faculty of the Department of Computer Science and Engineering at the University of Minnesota in 1997, where he is currently the Qwest Chair Professor in Telecommunications and Distinguished McKnight University Professor. He currently also serves as the Associate Director for Research at the Digital Technology Center, University of Minnesota.
 Prof. Zhang has served on the Editorial Boards of IEEE/ACM Transactions on Networking, Computer Network, etc. He was Technical Program Co-chair of IEEE INFOCOM 2006, ACM/USENIX Internet Measurement Conference (ACM/USENIX IMC'08), IEEE ICNP'13, and has servedon the Technical Program Committees of various conferences and workshops including ACM SIGCOMM, ACM SIGMETRICS, ACM/USENIX IMC, IEEE INFOCOM, IEEE ICNP and CoNext. He received the National Science Foundation CAREER Award in 1997. Prof. Zhang is co-recipient of an ACMSIGMETRICS best paper award, an IEEE International Conference on Network Protocols (ICNP) best paper award, an IEEE INFOCOM best paper award, a RAID best paper award and a SIMPLEX best paper award. He is a member of IEEE and ACM, and a Fellow of IEEE.
 
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