2156360
Quantifying Path Exploration in the Internet
Previous measurement studies have shown the existence of path exploration and slow convergence in the global Internet routing system, and a number of protocol enhancements have been proposed to remedy the problem. However, existing measurements were conducted only over a small number of testing prefixes. There has been no systematic study to quantify the pervasiveness of Border Gateway Protocol (BGP) slow convergence in the operational Internet, nor any known effort to deploy any of the proposed solutions. In this paper, we present our measurement results that identify BGP slow convergence events across the entire global routing table. Our data shows that the severity of path exploration and slow convergence varies depending on where prefixes are originated and where the observations are made in the Internet routing hierarchy. In general, routers in tier-1 Internet service providers (ISPs) observe less path exploration, hence they experience shorter convergence delays than routers in edge ASs; prefixes originated from tier-1 ISPs also experience less path exploration than those originated from edge ASs. Furthermore, our data show that the convergence time of route fail-over events is similar to that of new route announcements and is significantly shorter than that of route failures. This observation is contrary to the widely held view from previous experiments but confirms our earlier analytical results. Our effort also led to the development of a path-preference inference method based on the path usage time, which can be used by future studies of BGP dynamics.
627898
Fault-tolerant data delivery for multicast overlay networks
Overlay networks represent an emerging technology for rapid deployment of novel network services and applications. However, since public overlay networks are built out of loosely coupled end-hosts, individual nodes are less trustworthy than Internet routers in carrying out the data forwarding function. Here we describe a set of mechanisms designed to detect and repair errors in the data stream. Utilizing the highly redundant connectivity in overlay networks, our design splits each data stream to multiple sub-streams which are delivered over disjoint paths. Each sub-stream carries additional information that enables receivers to detect damaged or lost packets. Furthermore, each node can verify the validity of data by periodically exchanging Bloom filters, the digests of recently received packets, with other nodes in the overlay. We have evaluated our design through both simulations and experiments over a network testbed. The results show that most nodes can effectively detect corrupted data streams even in the presence of multiple tampering nodes.
5219400
Timer Interaction in Route Flap Damping
Route Flap Damping is a mechanism generally used in network routing protocols. Its goal is to limit the global impact of unstable routes by temporarily suppressing routes with rapid changes over short time periods. Although route damping is a clearly defined and simple procedure at each router, its effect in a large network setting is not well understood. We show that the current damping design leads to the intended behavior only under persistent route flapping. When the number of flaps is small, the global routing dynamics deviates significantly from the expected behavior with a longer convergence delay. Previous work observed that a single route flap can falsely trigger route suppression due to path exploration. However our simulations show that this false suppression only accounts for 30% of the convergence delay after a single route flap. Our study reveals previously unknown interactions between reuse timers at different routers. Route suppression and reuse at different routers are triggered at different times and thus affect the number of updates received by other routers. In turn, this impacts other routers' damping behavior. We propose to use Root Cause Notification to eliminate both false suppression and undesirable timer interaction
14677925
Universal IP multicast delivery
A ubiquitous and efficient multicast data delivery service is essential to the success of large-scale group communication applications. The original IP multicast design is to enhance network routers with multicast capability [S. Deering, D. Cheriton, Multicast routing in datagram internetworks and extended LANs, ACM Transactions on Computer Systems 8(2) (1990) 85-110]. This approach can achieve great transmission efficiency and performance but also poses a critical dependency on universal deployment. A different approach, overlay multicast, moves multicast functionality to end hosts, thereby removing the dependency on router deployment, albeit at the cost of noticeable performance penalty compared to IP multicast. In this paper we present the Universal Multicast (UM) framework, along with a set of mechanisms and protocols, to provide ubiquitous multicast delivery service on the Internet. Our design can fully utilize native IP multicast wherever it is available, and automatically build unicast tunnels to connect IP Multicast "islands" to form an overall multicast overlay. The UM design consists of three major components: an overlay multicast protocol (HMTP) for inter-island routing, an intra-island multicast management protocol (HGMP) to glue overlay multicast and native IP multicast together, and a daemon program to implement the functionality at hosts. In addition to performance evaluation through simulations, we have also implemented parts of the UM framework. Our prototype implementation has been used to broadcast several workshops and the ACM SIGCOMM 2004 conference live on the Internet. We present some statistics collected during the live broadcast and describe mechanisms we adopted to support end hosts behind Network Address Translation (NAT) gateways and firewalls.
8791084
Collecting the internet AS-level topology
At the inter-domain level, the Internet topology can be represented by a graph with Autonomous Systems (ASes) as nodes and AS peerings as links. This AS-level topology graph has been widely used in a variety of research efforts. Conventionally this topology graph is derived from routing tables collected by Route Views or RIPE RIS. In this work, we assemble the most complete AS-level topology by extending the conventional method along two dimensions. First, in addition to using data from RouteViews and RIPE RIS, we also collect data from many other sources, including route servers, looking glasses, and routing registries. Second, in addition to using routing tables, we also accumulate topological information from routing updates over time. The resulting topology graph on a recent day contains 44% more links and 3% more nodes than that from using RouteViews routing tables alone. Our data collection and topology generation process have been automated, and we publish the latest topology on the web on a daily basis.
14591402
An analysis of convergence delay in path vector routing protocols
Path vector routing protocols such as the Border Gateway Protocol (BGP) are known to suffer from slow convergence following a change in the network topology or policy. Although a number of convergence enhancements have been proposed recently, there has been no general analytical framework to assess and compare the various proposed algorithms. In this paper we present such a general framework to analyze the upper bounds of path vector protocols' convergence delay under shortest path routing policy and single link failure. Our framework takes into account important factors such as network connectivity, failure location, and routing message processing delay. It can be used to analyze both standard BGP and all the proposed convergence improvement algorithms in the case of shortest path routing policy and single link failure. It enables us to obtain previously unavailable analytical results, including the delay bounds of path fail-over for standard BGP and its convergence enhancements. Our analysis shows that BGP fail-over delay bounds are mainly determined by two factors: (1) the distance between the failure location and the destination, and (2) the length of the longest alternate path to reach the destination after the failure. These two factors are captured formally by our analysis and can explain why existing convergence enhancements often provide only limited improvements in fail-over events. Moreover, explicitly modeling message processing delay reveals insights into the impacts of connectivity richness (i.e., node degree and total number of links in the network), and also the effectiveness of different enhancements. These new results enable one to better understand and compare the behavior of various path vector protocols under different topology structures, network sizes, and message delays.
14861274
Learning in Region-Based Image Retrieval
In this paper, several effective learning algorithms using global image representations are adjusted and introduced to regionbased image retrieval (RBIR). First, the query point movement technique is considered. By assembling all the segmented regions of positive examples together and resizing the regions to emphasize the latest positive examples, a composite image is formed as the new query. Second, the application of support vector machines (SVM) in relevance feedback for RBIR is investigated. Both the one class SVM as a class distribution estimator and two classes SVM as a classifier are taken into account. For the latter, two representative display strategies are studied. Last, a region re-weighting algorithm is proposed inspired by those feature re-weighting ones. Experimental results on a database of 10,000 general-purpose images demonstrate the effectiveness of the proposed learning algorithms.
10593169
ProMiR II: a web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs
ProMiR is a web-based service for the prediction of potential microRNAs (miRNAs) in a query sequence of 60–150 nt, using a probabilistic colearning model. Identification of miRNAs requires a computational method to predict clustered and nonclustered, conserved and nonconserved miRNAs in various species. Here we present an improved version of ProMiR for identifying new clusters near known or unknown miRNAs. This new version, ProMiR II, integrates additional evidence, such as free energy data, G/C ratio, conservation score and entropy of candidate sequences, for more controllable prediction of miRNAs in mouse and human genomes. It also provides a wider range of services, e.g. the prediction of miRNA genes in long nonrelated sequences such as viral genomes. Importantly, we have validated this method using several case studies. All data used in ProMiR II are structured in the MySQL database for efficient analysis. The ProMiR II web server is available at .
9400771
Identification of biochemical networks by S-tree based genetic programming
MOTIVATION
Most previous approaches to model biochemical networks have focused either on the characterization of a network structure with a number of components or on the estimation of kinetic parameters of a network with a relatively small number of components. For system-level understanding, however, we should examine both the interactions among the components and the dynamic behaviors of the components. A key obstacle to this simultaneous identification of the structure and parameters is the lack of data compared with the relatively large number of parameters to be estimated. Hence, there are many plausible networks for the given data, but most of them are not likely to exist in the real system.


RESULTS
We propose a new representation named S-trees for both the structural and dynamical modeling of a biochemical network within a unified scheme. We further present S-tree based genetic programming to identify the structure of a biochemical network and to estimate the corresponding parameter values at the same time. While other evolutionary algorithms require additional techniques for sparse structure identification, our approach can automatically assemble the sparse primitives of a biochemical network in an efficient way. We evaluate our algorithm on the dynamic profiles of an artificial genetic network. In 20 trials for four settings, we obtain the true structure and their relative squared errors are <5% regardless of releasing constraints about structural sparseness. In addition, we confirm that the proposed algorithm is robust within +/-10% noise ratio. Furthermore, the proposed approach ensures a reasonable estimate of a real yeast fermentation pathway. The comparatively less important connections with non-zero parameters can be detected even though their orders are below 10(-2). To demonstrate the usefulness of the proposed algorithm for real experimental biological data, we provide an additional example on the transcriptional network of SOS response to DNA damage in Escherichia coli. We confirm that the proposed algorithm can successfully identify the true structure except only one relation.
36606756
Optimization of combustion control based on fuzzy logic
In this paper, to reach energy-saving goal, fuzzy system is employed to get optimal setpoints of furnace temperature. In the proposed approach, the optimal temperature trajectory of a slab in the furnace is determined through fuzzy system and the setpoint of furnace temperature is calculated according to this optimal trajectory. Because the approach calculates the setpoints of furnace temperature at each computation period, it can deal with disturbance of system to a certain extent. The simulation results show validity of the proposed method.
1232795
Efficient text classification by weighted proximal SVM
In this paper, we present an algorithm that can classify large-scale text data with high classification quality and fast training speed. Our method is based on a novel extension of the proximal SVM mode (Fung and Mangasarian, 2001). Previous studies on proximal SVM have focused on classification for low dimensional data and did not consider the unbalanced data cases. Such methods will meet difficulties when classifying unbalanced and high dimensional data sets such as text documents. In this work, we extend the original proximal SVM by learning a weight for each training error. We show that the classification algorithm based on this model is capable of handling high dimensional and unbalanced data. In the experiments, we compare our method with the original proximal SVM (as a special case of our algorithm) and the standard SVM (such as SVM light) on the recently published RCV1-v2 dataset. The results show that our proposed method had comparable classification quality with the standard SVM. At the same time, both the time and memory consumption of our method are less than that of the standard SVM.