Network Coordinate System Research Articles

A Survey on Network Coordinate Systems

Network coordinate systems play an important role in applications such as P2P file sharing, online network games, and IP positioning. Many network applications require a large amount of network delay data. However, plenty of network delay measurements will cause the network heavily loaded and unreachable problems between network nodes. The proposed network coordinate system provides an important means to solve these problems. Firstly the concept and application of the network coordinate system are introduced. Secondly, the existing representative network coordinate systems are elaborated in detail, such as system based on Euclidean distance and matrix decomposition, and then the advantages of each system are compared. Finally, the factors affecting the accuracy of network coordinate system prediction are analyzed, and the problems are pointed out which the network coordinate system is worth researching in the next step.

A Survey of Round Trip Time Prediction Systems

The performance of many networked applications improves when Round Trip Time (RTT) is reduced. With lower RTTs, human-to-human interactions become more realistic, query-response activities (such as DNS lookups) are more responsive, and transport protocols (such as transmission control protocol) can better utilise a path’s available capacity for long-lived file transfers. Additionally, RTT prediction systems are beneficial in distributed networks in order to improve performance and resource allocation. RTT prediction systems predict RTT for all nodes in the network without additional exchange of packets. We discuss 13 RTT prediction systems under three classes: 1) localisation measurement systems; 2) network coordinate systems; and 3) matrix factorisation systems. RTT prediction systems have been applied with positive results to various applications and services, such as peer-to-peer networks (e.g., BitTorrent clients, content distribution networks, and Internet of Things) or client-server networks (e.g., game server discovery).

Edge Provisioning with Flexible Server Placement

We present $\sf {Tentacle}$ , a decision support framework to provision edge servers for online services providers (OSPs). $\sf {Tentacle}$ takes advantage of the increasingly flexible edge server placement, which is enabled by new technologies such as edge computing platforms, cloudlets and network function virtualization, to optimize the overall performance and cost of edge infrastructures. The key difference between $\sf {Tentacle}$ and traditional server placement approaches lies on that $\sf {Tentacle}$ can discover proper unforeseen edge locations which significantly improve the efficiency and reduce the cost of edge provisioning. We show how $\sf {Tentacle}$ effectively identifies promising edge locations which are close to a collection of users merely with inaccurate network distance estimation methods, e.g., geographic coordinate (GC) and network coordinate systems (NC). We also show how $\sf {Tentacle}$ comprehensively considers various pragmatic concerns in edge provisioning, such as traffic limits by law or ISP policy, edge site deployment and resource usage cost, over-provisioning for fault tolerance, etc., with a simple optimization model. We simulate $\sf {Tentacle}$ using real network data at global and county-wide scales. Measurement-driven simulations show that with a given cost budget $\sf {Tentacle}$ can improve user performance by around 10-45 percent at global scale networks and 15-35 percent at a country-wide scale network.

NCShield: Protecting Decentralized, Matrix Factorization-Based Network Coordinate Systems

Network Coordinate (NC) systems provide a scalable means for Internet distance prediction and are useful for various Internet-based services, such as cloud or web-based services. Decentralized, matrix factorization-based NC (MFNC) systems have received particular attention recently. They can serve large-scale distributed services (as opposed to centralized NC systems) and do not need to satisfy the triangle inequality (as opposed to Euclidean-based NC systems). However, because of their decentralized nature, MFNC systems are vulnerable to various malicious attacks. In this paper, we provide the first study on attacks toward MFNC systems, and propose a trust and reputation-based approach called NCShield to counter such attacks. It is fully decentralized and can easily be customized. Different from previous approaches, NCShield is able to distinguish between legitimate distance variations and malicious distance alterations. Using four representative data sets from the Internet, we show that NCShield can defend against not only the typical disorder, repulsion and isolation attacks, but also more advanced attacks such as frog-boiling attacks. For example, when selecting node pairs with a shorter distance than a predefined threshold in an online game scenario, even if 30 percent of nodes are malicious, NCShield can reduce the false positive rate from 45.5 to 3.7 percent.

IMAGE NETWORK GENERATION OF UNCALIBRATED UAV IMAGES WITH LOW-COST GPS DATA

Abstract. The use of unmanned air vehicle (UAV) images acquired by a non-metric digital camera to establish an image network is difficult in cases without accurate camera model parameters. Although an image network can be generated by continuously calculating camera model parameters during data processing as an incremental structure from motion (SfM) methods, the process is time consuming. In this study, low-cost global position system (GPS) information is employed in image network generation to decrease computational expenses. Each image is considered as reference, and its neighbor images are determined based on GPS coordinates during processing. The reference image and its neighbor images constitute an image group, which is used to generate a free network through image matching and relative orientation. Data are then transformed from the free network coordinate system of each group into the GPS coordinate system by using the GPS coordinates of each image. After the exterior elements of each image are determined in the GPS coordinate system, the initial image network is established. Finally, self-calibration bundle adjustment constrained by GPS coordinates is conducted to refine the image network. The proposed method is validated on three fields. Results confirm that the method can achieve good image network when accurate camera model parameters are unavailable.

IMAGE NETWORK GENERATION OF UNCALIBRATED UAV IMAGES WITH LOW-COST GPS DATA

The use of unmanned air vehicle (UAV) images acquired by a non-metric digital camera to establish an image network is difficult in cases without accurate camera model parameters. Although an image network can be generated by continuously calculating camera model parameters during data processing as an incremental structure from motion (SfM) methods, the process is time consuming. In this study, low-cost global position system (GPS) information is employed in image network generation to decrease computational expenses. Each image is considered as reference, and its neighbor images are determined based on GPS coordinates during processing. The reference image and its neighbor images constitute an image group, which is used to generate a free network through image matching and relative orientation. Data are then transformed from the free network coordinate system of each group into the GPS coordinate system by using the GPS coordinates of each image. After the exterior elements of each image are determined in the GPS coordinate system, the initial image network is established. Finally, self-calibration bundle adjustment constrained by GPS coordinates is conducted to refine the image network. The proposed method is validated on three fields. Results confirm that the method can achieve good image network when accurate camera model parameters are unavailable.

A Cooperative Approach to Sensor Localisation in Distributed Fusion Networks

We consider self-localisation of networked sensor platforms, which are located disparately and collect cluttered and noisy measurements from an unknown number of objects (or, targets). These nodes perform local filtering of their measurements and exchange posterior densities of object states over the network to improve upon their myopic performance. Sensor locations need to be known, however, in order to register the incoming information in a common coordinate frame for fusion. In this work, we are interested in scenarios in which these locations need to be estimated solely based on the multi-object scene. We propose a cooperative scheme which features nodes using only the information they already receive for distributed fusion: we first introduce node-wise separable parameter likelihoods for sensor pairs, which are recursively updated using the incoming multi-object information and the local measurements. Second, we establish a network coordinate system through a pairwise Markov random field model which has the introduced likelihoods as its edge potentials. The resulting algorithm consists of consecutive edge potential updates and Belief Propagation message passing operations. These potentials are capable of incorporating multi-object information without the need to find explicit object-measurement associations and updated in linear complexity with the number of measurements. We demonstrate the efficacy of our algorithm through simulations with multiple objects and complex measurement models.

DISCS: A Distributed Coordinate System Based on Robust Nonnegative Matrix Completion

Many distributed applications, such as BitTorrent, need to know the distance between each pair of network hosts in order to optimize their performance. For small-scale systems, explicit measurements can be carried out to collect the distance information. For large-scale applications, this approach does not work due to the tremendous amount of measurements that have to be completed. To tackle the scalability problem, network coordinate system (NCS) was proposed to solve the scalability problem by using partial measurements to predict the unknown distances. However, the existing NCS schemes suffer seriously from either low prediction precision or unsatisfactory convergence speed. In this paper, we present a novel distributed network coordinate system (DISCS) that utilizes a limited set of distance measurements to achieve high-precision distance prediction at a fast convergence speed. Technically, DISCS employs the innovative robust nonnegative matrix completion method to improve the prediction accuracy. Through extensive experiments based on various publicly-available data sets, we found that DISCS outperforms the state-of-the-art NCS schemes in terms of prediction precision and convergence speed, which clearly shows the high usability of DISCS in real-life Internet applications.

A Hierarchical Internet Path Available Bandwidth Inference Mechanism

Distributed applications such as cloud computing and CDN need the Internet state information to optimize their performance. Although the present network coordinate systems are effective in latency inference, they cannot infer performance metric such as available bandwidth. Therefore, this paper attempts to use a novel mechanism to concentrate on how to infer the Internet path available bandwidth. Based on theoretical and Internet measurement data analysis result, we discover that Internet path available bandwidth has ultra metric space feature, based on which we propose an available bandwidth inference mechanism called PathGuru. In PathGuru, each node uses the partial measurement data to and from landmarks to obtain the outgoing and the incoming bandwidth vectors, and then the available bandwidth between any two nodes can be inferred according to the two vectors as well as the ultra metric space constraint. Experimental results on the Internet measurement dataset indicate that PathGuru has reasonable inference accuracy and can also deal with the available bandwidth symmetry violation problem.

Hijacking the Vuze BitTorrent network: all your hop are belong to us

Vuze is a popular file-sharing client. When looking for content, Vuze selects from its list of neighbours, a set of 20 nodes to be contacted; the selection is performed such that the neighbours closest to the content in terms of Vuze ID are contacted first. To improve efficiency of its searches, Vuze implements a network coordinate system: from the set of 20 to-be-contacted nodes, queries are sent to the closest nodes in terms of network distance, which is calculated by the difference in network coordinates. However, network coordinate systems are inherently insecure and a malicious peer can lie about its coordinate to appear closest to every peer in the network. This allows the malicious peer to bias next-hop choices for victim peers such that queries will be sent to the attacker, thus hijacking every search query. In our experiments, almost 20% of the search queries are hijacked; the cost of performing this attack is minimal – less than $112/month.

Accurate geolocation using network coordinates

Geolocation is an important factor in everyday life and used in location-based services, such as weather alerts and navigation. The most straightforward method is for the user to report its own location. However, this is not always possible. Attempts to geolocate users based on IP addresses and network measurements have resulted in fairly accurate geolocation, with low error. We show that newer network coordinate systems can provide even better accuracy in geolocating users. Compared to ping or older network coordinate systems, these new systems produce a much lower error in estimating the actual physical distance between a pair of IP addresses. The median error for direct network measurement is 4.2%, while the median error for Treeple is 1.0%. Our results for Treeple show a median localisation accuracy of 12.40 miles, more accurate compared to leading geolocation methods such as Octant.

Towards Network-Aware Composition of Big Data Services in the Cloud

Several Big data services have been developed on the cloud to meet increasingly complex needs of users. Most times a single Big data service may not be capable in satisfying user requests. As a result, it has become necessary to aggregate services from different Big data providers together in order to execute the user's request. This in turn has posed a great challenge; how to optimally compose services from a given set of Big data providers without affecting if not optimizing Quality of Service (QoS). With the advent of cloud-based Big data applications composed of services spread across different network environments, QoS of the network has become important in determining the true performance of composite services. However current studies fail to consider the impact of QoS of network on composite service selection. Therefore a novel network-aware genetic algorithm is proposed to perform composition of Big data services in the cloud. The algorithm adopts an extended QoS model which separates QoS of network from service QoS. It also uses a novel network coordinate system in finding composite services that have low network latency without compromising service QoS. Results of evaluation indicate that the proposed approach finds low latency and QoS-optimal compositions when compared with current approaches.

A vote-based intrusion-tolerance algorithm for network latency estimation

To enhance the survivability of a network coordinate system NCS in an unsafe environment, the physical meaning of the latency estimation error is re-explained as the reputation of reference node instead of spring force. Thus, a vote-based model for network latency estimation is proposed, and categorised as a kind of l

Immersive voice communication for massively multiplayer online games

In this paper, we propose a voice communication scheme, called immersive voice communication (IVC), to provide massively multiplayer online game (MMOG) players with the immersive experience to hear the voice of neighbor players within the area of interest (AOI). IVC is a peer-to-peer based scheme, so it does not impose too many extra loads on the original MMOG system. It further uses a relation model to classify neighbors of a player into listeners and the overhearers, and allocates less bandwidth to the latter than to the former for reducing the voice data traffic without sacrificing the user experience. IVC is also a latency- and bandwidth-aware scheme. It adopts the network coordinate system (NCS) to help construct the adaptive k-ary tree (Ak-tree) to reduce the voice data transmission latency and efficiently utilize the bandwidth. As shown by the simulation results, the proposed scheme outperforms other related schemes. We also implement IVC and integrate it with a spatialized voice rendering mechanism to realize an MMOG-like gallery guiding system for evaluating the user experience of IVC under the technology acceptance model (TAM). The TAM analysis results show that users consider IVC helpful and easy to use, and thus have high intention to use IVC.

Conditional clustered matrix factorization based network coordinate system

Network coordinate (NC) systems that use Euclidean distances suffer from the existence of Triangle Inequality Violations (TIVs). Matrix factorization (MF) based NC system is an alternative approach towards better prediction accuracy and can remove TIV. However, the prediction accuracy for short links in these systems still suffers from low prediction accuracy compared with the overall prediction accuracy. Two-layer systems have been proposed to improve the prediction accuracy for short links. They divide the whole space into several location-based clusters and run NC systems on both global layer and local layer. However, these systems do not improve the prediction accuracy for short links in the clusters with a few hosts. In this paper, the Conditional Clustered Network Coordinate (CCNC) system is proposed. It divides the space into a number of clusters in a balanced, dynamic, and decentralized way. In the proposed system, any node can join or disjoin the system without affecting the system accuracy. The performance of the CCNC system is evaluated with the King data set and the PlanetLab data set to be compared against two well known NC systems: Phoenix and Pancake. The simulation results show that CCNC outperforms Phoenix and Pancake significantly in terms of estimation accuracy, expected time to construct the clusters, and the communication overhead. Moreover, CCNC converges very fast and it is simple, scalable, dynamic, and robust under different dimension values.

Distributed Regional Weighted Multidimensional Scaling for Network Coordinate System

Network Coordinate System embeds all nodes into a specific metric space and estimate the latency between any node-pair through computing the corresponding metric distance with low-cost. But the metric space is usually uniform while the Internet is heterogeneous since different autonomous systems (AS) always have different routing strategies. To make full use of the clustering characteristic for improving the latency estimating accuracy without any prior knowledge, this paper judges the reference nodes are in the same AS with the target node or not through the metric distance between the target node and the source, and weights them by the Gaussian kernel function. Then the source node can computes a new coordinate to estimates the latency between the target node and itself more precisely. The experiments show this mechanism can reduce the Stress criterion of the entire system by 19.38% without any additional communication overhead, its computation cost is also very small at the same time. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.3817

Computing the nearest Euclidean distance matrix with low embedding dimensions

Euclidean distance embedding appears in many high-profile applications including wireless sensor network localization, where not all pairwise distances among sensors are known or accurate. The classical Multi-Dimensional Scaling (cMDS) generally works well when the partial or contaminated Euclidean Distance Matrix (EDM) is close to the true EDM, but otherwise performs poorly. A natural step preceding cMDS would be to calculate the nearest EDM to the known matrix. A crucial condition on the desired nearest EDM is for it to have a low embedding dimension and this makes the problem nonconvex. There exists a large body of publications that deal with this problem. Some try to solve the problem directly and some are the type of convex relaxations of it. In this paper, we propose a numerical method that aims to solve this problem directly. Our method is strongly motivated by the majorized penalty method of Gao and Sun for low-rank positive semi-definite matrix optimization problems. The basic geometric object in our study is the set of EDMs having a low embedding dimension. We establish a zero duality gap result between the problem and its Lagrangian dual problem, which also motivates the majorization approach adopted. Numerical results show that the method works well for the Euclidean embedding of Network coordinate systems and for a class of problems in large scale sensor network localization and molecular conformation.

Multi-manifold model of the Internet delay space

The network coordinate systems (NCSes) can assist the network applications to improve their performance by choosing preferred severs or constructing optimal overlay networks. However, the current NCSes cannot predict the end-to-end delay accurately, because they have neglected the inherent characters of the Internet delay space. By analyzing typical datasets, the Internet delay space was found to have obvious multi-manifold property. Thus, a multi-manifold model of the Internet delay space and a Principal Component Analysis (PCA) based Multi-manifold Partition algorithm of the Internet Delay Space (MPIDS-PCA) were proposed. Beginning with choosing landmarks randomly, MPIDS-PCA partitions the whole dataset into several sub datasets with low dimensionality by iterations. After the partition, the whole delay dataset and sub datasets are embedded into a hierarchical coordinate system Vivaldi-M. The experimental results show that MPIDS-PCA can gurantee the low dimensionality of sub datasets and Vivaldi-M can achieve better prediction accuracy.

Phoenix: A Weight-Based Network Coordinate System Using Matrix Factorization

Network coordinate (NC) systems provide a lightweight and scalable way for predicting the distances, i.e., round-trip latencies among Internet hosts. Most existing NC systems embed hosts into a low dimensional Euclidean space. Unfortunately, the persistent occurrence of Triangle Inequality Violation (TIV) on the Internet largely limits the distance prediction accuracy of those NC systems. Some alternative systems aim at handling the persistent TIV, however, they only achieve comparable prediction accuracy with Euclidean distance based NC systems. In this paper, we propose an NC system, so-called Phoenix, which is based on the matrix factorization model. Phoenix introduces a weight to each reference NC and trusts the NCs with higher weight values more than the others. The weight-based mechanism can substantially reduce the impact of the error propagation. Using the representative aggregate data sets and the newly measured dynamic data set collected from the Internet, our simulations show that Phoenix achieves significantly higher prediction accuracy than other NC systems. We also show that Phoenix quickly converges to steady state, performs well under host churn, handles the drift of the NCs successfully by using regularization, and is robust against measurement anomalies. Phoenix achieves a scalable yet accurate end-to-end distances monitoring. In addition, we study how well an NC system can characterize the TIV property on the Internet by introducing two new quantitative metrics, so-called RE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RPL</sub> and AE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RPL</sub> . We show that Phoenix is able to characterize TIV better than other existing NC systems.

The Frog-Boiling Attack

A network coordinate system assigns Euclidean “virtual” coordinates to every node in a network to allow easy estimation of network latency between pairs of nodes that have never contacted each other. These systems have been implemented in a variety of applications, most notably the popular Vuze BitTorrent client. Zage and Nita-Rotaru (at CCS 2007) and independently, Kaafar et al. (at SIGCOMM 2007), demonstrated that several widely-cited network coordinate systems are prone to simple attacks, and proposed mechanisms to defeat these attacks using outlier detection to filter out adversarial inputs. Kaafar et al. goes a step further and requires that a fraction of the network is trusted. More recently, Sherr et al. (at USENIX ATC 2009) proposed Veracity, a distributed reputation system to secure network coordinate systems. We describe a new attack on network coordinate systems, Frog-Boiling, that defeats all of these defenses. Thus, even a system with trusted entities is still vulnerable to attacks. Moreover, having witnesses vouch for your coordinates as in Veracity does not prevent our attack. Finally, we demonstrate empirically that the Frog-Boiling attack is more disruptive than the previously known attacks: systems that attempt to reject “bad” inputs by statistical means or reputation cannot be used to secure a network coordinate system.