Survey on Traffic of Metro Area Network with Measurement On-Line

International audienceNetwork traffic measurements can provide essential data for network research and operation. While Internet traffic has been heavily studied for several years, there are new characteristics of traffic having not been understood well brought by new applications for example P2P. It is difficult to get these traffic metrics due to the difficulty to measurement traffic on line for high speed link and to identify new applications using dynamic ports. In this paper, we present a broad overview of Internet traffic of an operated OC-48 export link of a metro area network from a carrier with the method of measurement on-line. The traffic behaves a daily characteristic well and the traffic data of whole day from data link layer to application layer is presented. We find the characteristics of traffic have changed greatly from previous measurements. Also, we explain the reasons bringing out these changes. Our goal is to provide the first hand of traffic data that is helpful for people to understand the change of traffic with new applications

Insights into the issue in IPv6 adoption: a view from the Chinese IPv6 Application mix

Published onlineThis is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Although IPv6 has been standardized more than 15 years ago, its deployment is still very limited. China has been strongly pushing IPv6, especially due to its limited IPv4 address space. In this paper, we describe measurements from a large Chinese academic network, serving a significant population of IPv6 hosts. We show that despite its expected strength, China is struggling as much as the western world to increase the share of IPv6 traffic. To understand the reasons behind this, we examine the IPv6 applicative ecosystem. We observe a significant IPv6 traffic growth over the past 3 years, with P2P file transfers responsible for more than 80% of the IPv6 traffic, compared with only 15% for IPv4 traffic. Checking the top websites for IPv6 explains the dominance of P2P, with popular P2P trackers appearing systematically among the top visited sites, followed by Chinese popular services (e.g., Tencent), as well as surprisingly popular third-party analytics including Google. Finally, we compare the throughput of IPv6 and IPv4 flows. We find that a larger share of IPv4 flows get a high-throughput compared with IPv6 flows, despite IPv6 traffic not being rate limited. We explain this through the limited amount of HTTP traffic in IPv6 and the presence of Web caches in IPv4. Our findings highlight the main issue in IPv6 adoption, that is, the lack of commercial content, which biases the geographic pattern and flow throughput of IPv6 traffic. Copyright © 2014 John Wiley & Sons, Ltd

Mnemonic Lossy Counting: An Efficient and Accurate Heavy-hitters Identification Algorithm

International audienceIdentifying heavy-hitter traffic flows efficiently and accurately is essential for Internet security, accounting and traffic engineering. However, finding all heavy-hitters might require large memory for storage of flows information that is incompatible with the usage of fast and small memory. Moreover, upcoming 100Gbps transmission rates make this recognition more challenging. How to improve the accuracy of heavy-hitters identification with limited memory space has become a critical issue. This paper presents a scalable algorithm named Mnemonic Lossy Counting (MLC) that improves the accuracy of heavy-hitters identification while having a reasonable time and space complexity. MLC algorithm holds potential candidate heavy-hitters in a historical information table. This table is used to obtain tighter error bounds on the estimated sizes of candidate heavy-hitters. We validate the MLC algorithm using real network traffic traces, and we compared its performance with two state-of-theart algorithms, namely Lossy Counting (LC) and Probabilistic Lossy Counting (PLC). The results reveal that: 1) with same set of parameters and memory usage, MLC achieves between 31.5% and 6.67% fewer false positives than LC and PLC. 2) MLC and LC have a zero false negative ratio, whereas 38% of the cases PLC has a non-zero false negatives and PLC can miss up to 4.4% of heavy-hitters. 3) MLC has a slightly lower memory cost than LC during the first few windows and its memory usage decreases with time, when PLC memory usage declines sharply. 4) MLC has similar runtime than LC, and smaller time than PLC