Yin Zhang

R3: Resilient Routing Reconfiguration

By: 
Ye Wang, Hao Wang, Ajay Mahimkar, Richard Alimi, Yin Zhang, Lili Qiu, and Yang Richard Yang
Appears in: 
CCR October 2010

Network resiliency is crucial to IP network operations. Existing techniques to recover from one or a series of failures do not offer performance predictability and may cause serious congestion.

Detecting the Performance Impact of Upgrades in Large Operational Networks

By: 
Ajay Anil Mahimkar, Han Hee Song, Zihui Ge, Aman Shaikh, Jia Wang, Jennifer Yates, Yin Zhang, and Joanne Emmons
Appears in: 
CCR October 2010

Networks continue to change to support new applications, improve reliability and performance and reduce the operational cost. The changes are made to the network in the form of upgrades such as software or hardware upgrades, new network or service features and network configuration changes. It is crucial to monitor the network when upgrades are made because they can have a significant impact on network performance and if not monitored may lead to unexpected consequences in operational networks.

Towards Automated Performance Diagnosis in a Large IPTV Network

By: 
Ajay Anil Mahimkar, Zihui Ge, Aman Shaikh, Jia Wang, Jennifer Yates, Yin Zhang, and Qi Zhao
Appears in: 
CCR October 2009

IPTV is increasingly being deployed and offered as a commercial service to residential broadband customers. Compared with traditional ISP networks, an IPTV distribution network (i) typically adopts a hierarchical instead of mesh-like structure, (ii) imposes more stringent requirements on both reliability and performance, (iii) has different distribution protocols (which make heavy use of IP multicast) and traffic patterns, and (iv) faces more serious scalability challenges in managing millions of network elements.

Spatio-Temporal Compressive Sensing and Internet Traffic Matrices

By: 
Yin Zhang, Matthew Roughan, Walter Willinger, and Lili Qiu
Appears in: 
CCR October 2009

Many basic network engineering tasks (e.g., traffic engineering, capacity planning, anomaly detection) rely heavily on the availability and accuracy of traffic matrices. However, in practice it is challenging to reliably measure traffic matrices. Missing values are common. This observation brings us into the realm of compressive sensing, a generic technique for dealing with missing values that exploits the presence of structure and redundancy in many realworld systems.

Predictable Performance Optimization for Wireless Networks

By: 
Yi Li, Lili Qiu, Yin Zhang, Ratul Mahajan, and Eric Rozner
Appears in: 
CCR October 2008

We present a novel approach to optimize the performance of IEEE 802.11-based multi-hop wireless networks. A unique feature of our approach is that it enables an accurate prediction of the resulting throughput of individual flows. At its heart lies a simple yet realistic model of the network that captures interference, traffic, and MAC-induced dependencies. Unless properly accounted for, these dependencies lead to unpredictable behaviors. For instance, we show that even a simple network of two links with one flow is vulnerable to severe performance degradation.

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