摘要 :
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. In this paper, we propose Resilient ...
展开
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. In this paper, we propose Resilient Routing Reconfiguration (R3), a novel routing protection scheme that is (i) provably congestion-free under a large number of failure scenarios; (ii) efficient by having low router processing overhead and memory requirements; (iii) flexible in accommodating different performance requirements (e.g., handling realistic failure scenarios, prioritized traffic, and the trade-off between performance and resilience); and (iv) robust to both topology failures and traffic variations. We implement R3 on Linux using a simple extension of MPLS, called MPLS-ff. We conduct extensive Emulab experiments and simulations using realistic network topologies and traffic demands. Our results show that R3 achieves near-optimal performance and is at least 50% better than the existing schemes under a wide range of failure scenarios.
收起
摘要 :
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 n...
展开
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. This can be achieved manually for a small number of devices, but does not scale to large networks with hundreds or thousands of routers and extremely large number of different upgrades made on a regular basis.
In this paper, we design and implement a novel infrastructure MERCURY for detecting the impact of network upgrades (or triggers) on performance. MERCURY extracts interesting triggers from a large number of network maintenance activities. It then identifies behavior changes in network performance caused by the triggers. It uses statistical rule mining and network configuration to identify commonality across the behavior changes. We systematically evaluate MERCURY using data collected at a large tier-1 ISP network. By comparing to operational practice, we show that MERCURY is able to capture the interesting triggers and behavior changes induced by the triggers. In some cases, MERCURY also discovers previously unknown network behaviors demonstrating the effectiveness in identifying network conditions flying under the radar.
收起
摘要 :
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-...
展开
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. These unique characteristics impose tremendous challenges in the effective management of IPTV network and service.
In this paper, we focus on characterizing and troubleshooting performance issues in one of the largest IPTV networks in North America. We collect a large amount of measurement data from a wide range of sources, including device usage and error logs, user activity logs, video quality alarms, and customer trouble tickets. We develop a novel diagnosis tool called Giza that is specifically tailored to the enormous scale and hierarchical structure of the IPTV network. Giza applies multi-resolution data analysis to quickly detect and localize regions in the IPTV distribution hierarchy that are experiencing serious performance problems. Giza then uses several statistical data mining techniques to troubleshoot the identified problems and diagnose their root causes. Validation against operational experiences demonstrates the effectiveness of Giza in detecting important performance issues and identifying interesting dependencies. The methodology and algorithms in Giza promise to be of great use in IPTV network operations.
收起
摘要 :
Cellular networks are constantly evolving due to frequent changes in radio access and end user equipment technologies, applications, and traffic. Network upgrades should be performed with extreme caution since millions of users he...
展开
Cellular networks are constantly evolving due to frequent changes in radio access and end user equipment technologies, applications, and traffic. Network upgrades should be performed with extreme caution since millions of users heavily depend on the cellular networks. Before upgrading the entire network, it is important to conduct field evaluation of upgrades.The choice and number of field test locations have significant impact on the time-to-market and confidence in how well various network upgrades will work out in the rest of the network. We propose a novel approach - Reflection to automatically determine where to conduct the upgrade field tests to accurately identify important features that affect the upgrade and predict for the performance of untested locations. We demonstrate its effectiveness using real traces collected from a major US cellular network as well as synthetic traces.
收起
