Masato Uchida

Identifying Heavy-Hitter Flows from Sampled Flow Statistics ∗

SUMMARY With the rapid increase of link speed in recent years, packet sampling has become a very attractive and scalable means in collecting flow statistics; however, it also makes inferring original flow characteristics much more difficult. In this paper, we develop techniques and schemes to identify flows with a very large number of packets (also known as heavy-hitter flows) from sampled flow statistics. Our approach follows a two-stage strategy: We first parametrically estimate the original flow length distribution from sampled flows. We then identify heavy-hitter flows with Bayes’ theorem, where the flow length distribution estimated at the first stage is used as an a priori distribution. Our approach is validated and evaluated with publicly available packet traces. We show that our approach provides a very flexible framework in striking an appropriate balance between false positives and false negatives when sampling frequency is given.

An Information-Theoretic Characterization of Weighted alpha-Proportional Fairness

This paper provides a novel characterization of fairness concepts in network resource allocation problems from the viewpoint of information theory. The fundamental idea adopted in this paper is to characterize the utility functions used in optimization problems, which motivate fairness concepts, based on a trade-off between user and system satisfaction. Here, user satisfaction is evaluated using information divergence measures that were originally used in information theory to evaluate the difference between two probability distributions. In this paper, information divergence measures are applied to evaluate the difference between the implemented resource allocation and a requested resource allocation. The requested resource allocation is assumed to be ideal in some sense from the users point of view. Also, system satisfaction is evaluated based on the efficiency of the implemented resource utilization, which is defined as the total amount of resources allocated to each user. The results discussed in this paper indicate that the well-known fairness concept called weighted alpha-proportional fairness can be characterized using the alpha-divergence measure, which is a general class of information divergence measures, as an equilibrium of the trade-off described above. In the process of obtaining these results, we also obtained a new utility function that has a parameter to control the trade-off. This new function is then applied to typical examples to solve resource allocation problems in simple network models such as those for two-link networks and wireless LANs.

Performance Evaluation of Power Saving Scheme with Dynamic Transmission Capacity Control

Our major purpose in this study is to develop a method for achieving power conservation with a network itself. The proposed power saving approach takes traffic characteristics of network into consideration. If network switch can adapt its processing speed to arrival traffic rate, power saving can be expected depending on the network utilization. The same idea is already materialized just as Intel’s Speedstep technology [5]. We first confirm this consideration by measuring the power consumption of some switches and show that a Giga bit Ethernet port consumes 2 or 3 W with an addition of a link. Based on this experiment, we propose a scheme to achieve the dynamic control of the transmission capacity between switches in order to reduce the power consumption based upon the change in traffic volume. To control the transmission speed dynamically and gradually, Link Aggregation is used in our scheme. From simulation studies, we show that the proposed architecture can achieve both high power saving capability and sufficient feasibility.

On the Quality of Triangle Inequality Violation Aware Routing Overlay Architecture

It is known that Internet routing policies for both intra- and inter-domain routing can naturally give rise to triangle inequality violations (TIVs) with respect to quality of service (QoS) network metrics such as latencies between nodes. This motivates the exploitation of such TIVs phenomenon in network metrics to design TlV-aware routing overlay architecture which is capable of choosing quality overlay routing paths to improve end-to-end QoS without changing the underlying network architecture. Our idea is to find quality overlay routes between node pairs based on TIV optimization in terms of the latency and packet loss ratio, and that can offer near optimal routing quality in cost-effective and scalable manner. Our intuition to do this is to choose these overlay routes from a small set of transit nodes. We propose to assign nodes with transit selection frequency scores that are computed based on previous node usage for transit, and consolidate a small set of highly ranked transit nodes. For every node pair, we choose the best transit node in this small set for overlay routing, based on TIV optimization in latency and packet loss ratio. We analyze the quality of our TlV-aware routing overlay algorithm analytically and using real Internet measurements on latency and packet loss ratio. Our results show good quality performance in improving end-to-end QoS routing.

Dynamic storage load balancing with analogy to thermal diffusion for P2P file sharing

This paper presents a file replication scheme with analogy to thermal diffusion for storage load balancing in unstructured peer-to-peer (P2P) file sharing networks. The proposed scheme is intended to balance storage load among peers in a dynamic, distributed, and autonomous manner, as in thermal diffusion. Theoretical analysis results show that the presented scheme actually has a statistical analogy with a thermal diffusion equation. In addition, the proposed scheme includes parameters to widely explore the trade-off between storage load balancing and search performance existing in unstructured P2P file sharing networks. Simulation results show that compared to other replication schemes, the proposed scheme has better ability not only in balancing storage load among peers, which is the primary objective of the proposal, but also in widely exploring the performance trade-off.

Fixed Point Optimization Algorithms for Network Bandwidth Allocation Problems with Compoundable Constraints

In this letter, we develop a novel computational approach for network bandwidth allocation problems in which the compoundable constraints about the preferable transmission rate fall in the infeasible region. The approach is based on an iterative technique for non-convex optimization over the fixed point set of a certain nonexpansive mapping. The realized allocation maximizes a predefined utility and is closest to the preferable transmission rate with respect to the norm. Numerical examples show that our approach is an efficient way to solve the non-concave utility maximization problem.

Performance evaluation of qos-aware routing in overlay network

A recent trend in routing research is the use of overlay routing to improve end-to-end QoS without changing the network level architecture The key of this technology is to find alternative routes, which can avoid congested routes, using an overlay network Evaluating the calculation cost to find such an alternative route to develop the technology is important Therefore, this paper evaluates how effective the technology can be when the number of alternative route candidates is limited The evaluation results indicate that the technology is effective even if alternative route candidates are limited to one quarter of the number of routes.

An information-theoretic characterization of weighted α-proportional fairness in network resource allocation

Abstract This paper provides a characterization of fairness concepts in network resource allocation problems from the viewpoint of information theory. The fundamental idea underlying this paper is to characterize the utility functions in optimization problems, which motivate fairness concepts, based on a trade-off between user and network satisfaction. User satisfaction is evaluated using information divergence measures, which were originally used in information theory, to evaluate the difference between an implemented resource allocation and a requested resource allocation. The requested resource allocation is assumed to be ideal in some sense from the user’s point of view. Network satisfaction is evaluated based on the efficiency of resource usage in the implemented resource allocation. The results in this paper indicate that the well-known fairness concept called weighted α-proportional fairness can be characterized using the α-divergence measure, which is a general class of information divergence measures, as an equilibrium of the trade-off. Some typical examples of applications demonstrate how the presented characterization works.

Unsupervised Ensemble Anomaly Detection through Time-Periodical Packet Sampling

We propose an anomaly detection method that trains a baseline model describing the normal behavior of network traffic without using manually labeled traffic data. The trained baseline distribution is used as the basis for comparison with the audit network traffic. The proposed method can be carried out in an unsupervised manner through the use of time-periodical packet sampling for a different purpose from which it was intended. That is, we take advantage of the lossy nature of packet sampling for the purpose of extracting normal packets from the unlabeled original traffic data. By using real traffic traces, we show that the proposed method is comparable in terms of false positive and false negative rates on detecting anomalies regarding TCP SYN packets to the conventional method that requires manually labeled traffic data to train the baseline model. In addition, in order to mitigate the possible performance variation due to probabilistic nature of sampled traffic data, we devised an ensemble anomaly detection method that exploits multiple baseline models in parallel. Experimental results show that the proposed ensemble anomaly detection performs well and is not affected by the variability of time-periodical packet sampling.

Universal power laws in the threshold network model: A theoretical analysis based on extreme value theory

We theoretically and numerically investigated the threshold network model with a generic weight function where there were a large number of nodes and a high threshold. Our analysis was based on extreme value theory, which gave us a theoretical understanding of the distribution of independent and identically distributed random variables within a sufficiently high range. Specifically, the distribution could be generally expressed by a generalized Pareto distribution, which enabled us to formulate the generic weight distribution function. By using the theorem, we obtained the exact expressions of degree distribution and clustering coefficient which behaved as universal power laws within certain ranges of degrees. We also compared the theoretical predictions with numerical results and found that they were extremely consistent.