31253731
Explicit Routing for Traffic Engineering in Labeled Optical Burst-Switched WDM Networks
Optical burst switching (OBS) is a promising technique for supporting high-capacity bursty data traffic over optical wavelength-division-multiplexed (WDM) networks. A label-switched path can be established to forward a burst control packet (BCP) if each OBS node is augmented with an IP/MPLS controller. Such a network is called a labeled OBS (LOBS) network, and it can exploit the explicit routing and constraint-based routing properties supported in the MPLS framework to perform traffic and resource engineering. In this paper, we consider the traffic-engineering problem in a LOBS network, using explicit routing to balance network load and to reduce the burst-loss probability. We model the traffic-engineering problem in a LOBS network as an optimization problem and propose two new objective functions to minimize network congestion. Our illustrative numerical examples show that the burst-loss probability can be significantly reduced using optimization technique, when compared with shortest-path routing.
11999738
The LLAMA Middleware Support for Accountable Service-Oriented Architecture
Enterprises are turning to SOA for the flexible deployment of business processes. While current monitoring tools can detect service execution problems in an enterprise's servers and report such problems to human decision makers, they may not closely monitor the external services they use, diagnose the root cause of process problems, and automatically reconfigure the process to replace faulty services. This paper presents the LLAMA middleware support for service process monitoring, run-time management, and configuration. Instances of accountability agents are deployed to selectively monitor some services' performance. These agents in turn allow LLAMA's Accountability Authority (AA) to diagnose process problems and apply any necessary reconfiguration. The project also builds tools to simplify the setup and deployment of LLAMA components. Our experimental results indicate that using LLAMA contributes only a modest amount of system overhead, and that the diagnosis process is swift and sufficiently accurate.
28540271
Adaptive Training of a Kernel-Based Representative and Discriminative Nonlinear Classifier
Adaptive training of a classifier is necessary when feature selection and sparse representation are considered. Previously, we proposed a kernel-based nonlinear classifier for simultaneous representation and discrimination of pattern features. Its batch training has a closed-form solution. In this paper we implement an adaptive training algorithm using an incremental learning procedure that exactly retains the generalization ability of batch training. It naturally yields a sparse representation. The feasibility of the presented methods is illustrated by experimental results on handwritten digit classification.
15427261
Anomaly detection in high-dimensional network data streams: A case study
In this paper, we study the problem of anomaly detection in high-dimensional network streams. We have developed a new technique, called Stream Projected Outlier deTector (SPOT), to deal with the problem of anomaly detection from high-dimensional data streams. We conduct a case study of SPOT in this paper by deploying it on 1999 KDD Intrusion Detection application. Innovative approaches for training data generation, anomaly classification and false positive reduction are proposed in this paper as well. Experimental results demonstrate that SPOT is effective in detecting anomalies from network data streams and outperforms existing anomaly detection methods.
3813171
Expert Finding in a Social Network
This paper addresses the issue of expert finding in a social network. The task of expert finding, as one of the most important research issues in social networks, is aimed at identifying persons with relevant expertise or experience for a given topic. In this paper, we propose a propagation-based approach that takes into consideration of both person local information and network information (e.g. relationships between persons). Experimental results show that our approach can outperform the baseline approach.
3391217
Study on the Co-Existence of 20ms TTI and 5ms TTI in E-DCH of TD-SCDMA HSUPA
The wireless link with fixed parameters is subject to time-varying channel, and the battery energy is limited in mobile terminals. Thus we consider the co-existence of 20msTTI and 5ms TTI in E-DCH of TD-SCDMA HSUPA. In this paper, based on the comparative study on the link performances with different TTI and the analysis on the link simulation results, we discuss the different TTI’s influence on the terminal power consumption. Furthermore, the system level performances of semi-static TTI and traditional single TTI configurations are compared. Finally the conclusions are drawn: 20ms and 5msTTI should co-exist in TD-SCDMA HSUPA, and be adopted respectively in different channel condition to save the terminal power. The data throughput of semi-static TTI configuration is greater than those of both single TTI configurations, and the semi-static TTI selection between 20ms and 5ms is implemented according to the moving speed of a specific UE. Keywords-TD-SCDMA; HSUPA; TTI Co-existence; Terminal power consumption; Semi-static TTI configuration
8817054
The Application of Cultural Algorithm in Load Balancing of Hybrid P2P Network
This paper analyses the defect of the designing model of load balancing in hybrid P2P network, and proposes a new strategy of scheduling for load balancing, which based on culture algorithm of multilayer belief spaces. According to the current load status of each super-node in the system, it can calculate its load weight of the next time section by using the cultural algorithm, and assign the current requests of login with weighted round-robin. The extracted knowledge which is got by selecting the best belief space from the multilayer belief space is used to optimize the constrains of the evolutionary, in order to accelerate the optimization process of the calculation of load weight. Simulation experiments show that the allocation of login request to each node is to be balance much faster through using of cultural algorithm based on multilayer belief spaces.