QoS of applications is essential for content providers, and it is required to improve the end-to-end communication quality from a content provider to users. Generally, a content provider's data center network is connected to multiple ASes and has multiple egress paths to reach the content user's network. However, on the Internet, the communication quality of network paths outside of the provider's administrative domain is a black box, so multiple egress paths cannot be quantitatively compared. In addition, it is impossible to determine a unique egress path within a network domain because the parameters that affect the QoS of the content are different for each network. We propose a “Performance Aware Egress Path Discovery” method to improve QoS for content providers. The proposed method uses two techniques: Egress Peer Engineering with Segment Routing over IPv6 and Passive End-to-End Measurement. The method is superior in that it allows various metrics depending on the type of content and can be used for measurements without affecting existing systems. To evaluate our method, we deployed the Performance Aware Egress Path Discovery System in an existing content provider network and conducted experiments to provide production services. Our findings from the experiment show that, in this network, 15.9% of users can expect a 30Mbps throughput improvement, and 13.7% of users can expect a 10ms RTT improvement.

Traffic matrix (TM) estimation has been extensively studied for decades. Although conventional estimation techniques assume that traffic volumes are unchanged between origins and destinations, packets are often lost on a path due to traffic burstiness, silent failures, etc. Counting every path at every link, we could easily get the traffic volumes with their change, but this approach significantly increases the measurement cost since counters are usually implemented using expensive memory structures like a SRAM. This paper proposes a mathematical model to estimate TMs including volume changes. The method is established on a Boolean fault localization technique; the technique requires fewer counters as it simply determines whether each link is lossy. This paper extends the Boolean technique so as to deal with traffic volumes with error bounds that requires only a few counters. In our method, the estimation errors can be controlled through parameter settings, while the minimum-cost counter placement is determined with submodular optimization. Numerical experiments are conducted with real network datasets to evaluate our method.

Estimating the packet loss ratio of TCP transfers is essential for passively measuring Quality of Service (QoS) on the Internet traffic. However, only a few studies have been conducted on this issue. The Benko-Veres algorithm is one technique for estimating the packet loss ratio of two networks separated by a measurement point. However, this study shows that it leads to an estimation error of a few hundred percent in the particular environment where the packet loss probabilities between the two networks are asymmetrical. We propose a passive method for packet loss estimation that offers improved estimation accuracy by introducing classification conditions for the TCP retransmission timeout. An experiment shows that our proposed algorithm suppressed the maximum estimation error to less than 15%.

Analyzing short-interval-traffic behaviors is important for network performance management to realize high quality multimedia applications. However, it is difficult to measure short-interval-traffic volumes because there are complications in collecting short-interval-traffic data from routers. An example is a heavy load on routers or inaccurate measurement by the short-polling interval; it even demands expensive measurement tools. To resolve these disadvantages, an estimating method of short-interval-traffic distribution (EMSIT) has been proposed. This method estimates short-interval-traffic distributions using MIB (Management Information Base) data, which collects traffic volumes in cycles of several minutes. In this paper, we propose a new estimation method (EMSIT-LD) based on EMSIT, which applies to the case of long-term-traffic dynamics. We evaluate it using computer simulations and actual traffic data.

There is an emerging requirement for real-time flow-based traffic monitoring, which is vital to detecting and/or tracing DoS attacks as well as troubleshooting and traffic engineering in the ISP networks. We propose the architecture for a scalable real-time flow measurement tool in order to allow operators to flexibly define "the targeted flows" on-demand, to obtain various statistics on those flows, and to visualize them in a real-time manner. A traffic distribution device and multiple traffic capture devices processing packets in parallel are included in the architecture, in which the former device copies traffic and distributes it to the latter devices. We evaluate the performance of a proto-type implementation on PC-UNIX in testbed experiments to demonstrate the scalability of our architecture. The evaluation shows that the performance increases in proportion to the number of the capture devices and the maximum performance reaches 80 K pps with six capture devices. Finally we also show applications of our tool, which indicate the advantage of flexible fine-grained flow measurements.

Performance measurements are indispensable for managing the Internet. Among the performance measurement techniques known, passive measurement is attractive because of its accuracy; user traffic is observed without inserting additional test traffic. However, the technique is handicapped by its large storage and bandwidth costs. This paper proposes a passive packet loss measurement technique that effectively avoids the difficulty of the conventional passive measurement approaches. Its key advance is utilizing the packet feature computed by a hash function. Since the feature can identify a packet with a short length of data, it becomes possible to greatly decrease the storage and bandwidth costs of passive measurements. The paper details the measurement procedure and assesses the design parameters used in the method. In addition, the validity of the proposed method is confirmed through experiments. The experiments also show the advantage of the method over the conventional active measurement.