In the Internet, because of huge scale and distributed administration, it is of practical importance to infer network-internal characteristics that cannot be measured directly. In this paper, based on a general framework we proposed previously, we present a feasible method of inferring packet loss rates of individual links from end-to-end measurement of unicast probe packets. Compared with methods using multicast probes, unicast-based inference methods are more flexible and widely applicable, whereas they have a problem with imperfect correlation in concurrent events on paths. Our method can infer link loss rates under this problem, and is applicable to various path-topologies including trees, inverse trees and their combinations. We also show simulation results which indicate potential of our unicast-based method.

We propose a new network architecture nemed Holonic Network for personalized multimedia communications, which is characterized by distributed cooperative networking based on autonomous management and all-optical transport networks. We than propose autonomous routing method. Moreover, an information searching method and a route generation method with network maps, which are essential for this network, are proposed. Lastly, we evaluate the proposed network performance by theoretical analysis and system emulation.

This paper proposes a contention-free burst scheduling scheme for optically burst-switched WDM networks. We construct contention-free wavelength planes (λ-planes) by assigning dedicated wavelengths to each ingress node. Bursts are transmitted to their egress nodes on λ-planes, along routes forming a spanning tree. As a result, contention at intermediate core nodes is completely eliminated, and contention at ingress nodes is resolved by means of electric buffers. This paper develops a spanning tree construction algorithm, aiming at balancing input loads among output ports at each ingress node. Furthermore, a wavelength assignment algorithm is proposed, which is based on the amount of traffic lost at ingress nodes. We show that the proposed scheme can decrease the burst loss probability drastically, even if traffic intensities at ingress nodes are different.

We consider a location-aware store-carry-forward routing scheme based on node density estimation (LA Routing in short), which adopts different message forwarding strategies depending on node density at contact locations where two nodes encounter. To do so, each node estimates a node density distribution based on information about contact locations. In this paper, we clarify how the estimation accuracy affects the performance of LA Routing. We also examine the performance of LA Routing when it applies to networks with homogeneous node density. Through simulation experiments, we show that LA Routing is fairly robust against the accuracy of node density estimation and its performance is comparable with Probabilistic Routing even in the case that that node density is homogeneous.

The MapReduce job scheduler implemented in Hadoop is a mechanism to decide which job is allowed to use idle resources in Hadoop. In terms of the mean job response time, the performance of the job scheduler strongly depends on the job arrival pattern, which includes job size (i.e., the amount of required resources) and their arrival order. Because existing schedulers do not utilize information about job sizes, however, those schedulers suffer severe performance degradation with some arrival patterns. In this paper, we propose a scheduler that estimates and utilizes remaining job sizes, in order to achieve good performance regardless of job arrival patterns. Through simulation experiments, we confirm that for various arrival patterns, the proposed scheduler achieves better performance than the existing schedulers.

In this paper, we study robust Principal Component Analysis (PCA)-based anomaly detection techniques in network traffic, which can detect traffic anomalies by projecting measured traffic data onto a normal subspace and an anomalous subspace. In a PCA-based anomaly detection, outliers, anomalies with excessively large traffic volume, may contaminate the subspaces and degrade the performance of the detector. To solve this problem, robust PCA methods have been studied. In a robust PCA-based anomaly detection scheme, outliers can be removed from the measured traffic data before constructing the subspaces. Although the robust PCA methods are promising, they incure high computational cost to obtain the optimal location vector and scatter matrix for the subspace. We propose a novel anomaly detection scheme by extending the minimum covariance determinant (MCD) estimator, a robust PCA method. The proposed scheme utilizes the daily periodicity in traffic volume and attempts to detect anomalies for every period of measured traffic. In each period, before constructing the subspace, outliers are removed from the measured traffic data by using a location vector and a scatter matrix obtained in the preceding period. We validate the proposed scheme by applying it to measured traffic data in the Abiline network. Numerical results show that the proposed scheme provides robust anomaly detection with less computational cost.

Network tomography is a technique for estimating internal network characteristics from end-to-end measurements. In this paper, we focus on loss tomography, which is a network tomography problem for estimating link loss rates. We study a loss tomography problem to detect links with high link loss rates in network environments with dynamically changing link loss rates, and propose a window-based sequential loss tomography scheme. The loss tomography problem is formulated as an underdetermined linear inverse problem, where there are infinitely many candidates of the solution. In the proposed scheme, we use compressed sensing, which can solve the problem with a prior information that the solution is a sparse vector. Measurement nodes transmit probe packets on measurement paths established between them, and calculate packet loss rates of measurement paths (path loss rates) from probe packets received within a window. Measurement paths are classified into normal quality and low quality states according to the path loss rates. When a measurement node finds measurement paths in the low quality states, link loss rates are estimated by compressed sensing. Using simulation scenarios with a few link states changing dynamically from low to high link loss rates, we evaluate the performance of the proposed scheme.

This survey summarizes the state-of-the-art research on network coding, mainly focusing on its applications to computer networking. Network coding generalizes traditional store-and-forward routing techniques by allowing intermediate nodes in networks to encode several received packets into a single coded packet before forwarding. Network coding was proposed in 2000, and since then, it has been studied extensively in the field of computer networking. In this survey, we first summarize linear network coding and provide a taxonomy of network coding research, i.e., the network coding design problem and network coding applications. Moreover, the latter is subdivided into throughput/capacity enhancement, robustness enhancement, network tomography, and security. We then discuss the fundamental characteristics of network coding and diverse applications of network coding in details, following the above taxonomy.