National University of Defense Technology

About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Computer science & Radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.

FISSIONE: a scalable constant degree and low congestion DHT scheme based on Kautz graphs

Abstract: The distributed hash table (DHT) scheme has become the core component of many large-scale peer-to-peer networks. Degree, diameter, and congestion are important measures of DHT schemes. Many proposed DHT schemes are based on traditional interconnection topologies, one being the Kautz graph, which is a static topology with many good properties such as optimal diameter, optimal fault-tolerance, and low congestion. In this paper, we propose FISSIONE: the first effective DHT scheme based on Kautz graphs. FISSIONE is constant degree, O(log N) diameter, and (1 + o(1))-congestion-free. FISSIONE shows that a DHT scheme with constant degree and constant congestion can still achieve O(log N) diameter, which is better than the lower bound /spl Omega/(N/sup 1/d/) conjectured before. The average degree of FISSIONE is 4, the diameter is less than 2 log N, and the maintenance message cost is less than 3 log N. The average routing path length is about log N and is shorter than CAN or Koorde with the same degree when the peer-to-peer network is large-scale. FISSIONE can achieve good load balance, high performance, and low congestion and these properties are carefully evaluated by formal proofs or simulations in the paper.

Specific Emitter Identification Based on Nonlinear Dynamical Characteristics

Abstract: Specific emitter identification (SEI) designates the unique transmitter of a given signal, using only external feature measurements called the RF fingerprints of the signal. SEI is often used in military and civilian spectrum-management operations. The SEI technique has also been applied to enhance the security of wireless network, such as VHF radio networks, Wi-Fi networks, cognitive radios, and cellular networks. A novel SEI method based on nonlinear dynamical characteristics is proposed in this paper. The method works based on the actual signal's inherent nonlinear dynamical characteristics. The permutation entropy is extracted as the signal's RF fingerprint to identify the unique transmitter. The quadrature phase-shift keying (QPSK) signals from four wireless network cards and differential quadrature phase-shift keying (DQPSK) signals from three digital radios are utilized to evaluate the performance of the method. Experimental results demonstrate that the proposed method is effective. On the other hand, the proposed method is convenient to implement in a PC.

Priority-Based Consolidation of Parallel Workloads in the Cloud

Abstract: The cloud computing paradigm is attracting an increased number of complex applications to run in remote data centers. Many complex applications require parallel processing capabilities. Parallel applications of certain nature often show a decreasing utilization of CPU resources as parallelism grows, mainly because of the communication and synchronization among parallel processes. It is challenging but important for a data center to achieve a certain level of utilization of its nodes while maintaining the level of responsiveness of parallel jobs. Existing parallel scheduling mechanisms normally take responsiveness as the top priority and need nontrivial effort to make them work for data centers in the cloud era. In this paper, we propose a priority-based method to consolidate parallel workloads in the cloud. We leverage virtualization technologies to partition the computing capacity of each node into two tiers, the foreground virtual machine (VM) tier (with high CPU priority) and the background VM tier (with low CPU priority). We provide scheduling algorithms for parallel jobs to make efficient use of the two tier VMs to improve the responsiveness of these jobs. Our extensive experiments show that our parallel scheduling algorithm significantly outperforms commonly used algorithms such as extensible argonne scheduling system in a data center setting. The method is practical and effective for consolidating parallel workload in data centers.

Superior solution guided particle swarm optimization combined with local search techniques

Abstract: Particle swarm optimization (PSO) is an evolutionary algorithm known for its simplicity and effectiveness in solving various optimization problems. PSO should have strong yet balanced exploration and exploitation capabilities to enhance its performance. A superior solution guided PSO (SSG-PSO) framework integrated with an individual level based mutation operator and different local search techniques is proposed in this study. In SSG-PSO, a collection of superior solutions is maintained and updated with the evolutionary process, such that each particle can comprehensively learn from the recorded superior solutions. In addition, to maintain the diversity of the particle swarm, SSG-PSO is combined with an individual level based mutation operator, which will be invoked when a particle is trapped in a local optimum (determined by the fitness and position states of the particle), thereby improving the adaptation and flexibility of each individual particle. Moreover, two gradient-based local search techniques, namely, the Broyden-Fletcher-Goldfarb-Shanno (BFGS) and Davidon-Fletcher-Powell (DFP) Quasi-Newton methods, and two derivative-free local search techniques, namely, pattern search and Nelder-Mead simplex search, are incorporated into SSG-PSO. The performances of SSG-PSO and that of its local search enhanced variants are extensively and comparatively studied on a suit of benchmark optimization functions.

CGD: Multi-View Clustering via Cross-View Graph Diffusion

Abstract: Graph based multi-view clustering has been paid great attention by exploring the neighborhood relationship among data points from multiple views. Though achieving great success in various applications, we observe that most of previous methods learn a consensus graph by building certain data representation models, which at least bears the following drawbacks. First, their clustering performance highly depends on the data representation capability of the model. Second, solving these resultant optimization models usually results in high computational complexity. Third, there are often some hyper-parameters in these models need to tune for obtaining the optimal results. In this work, we propose a general, effective and parameter-free method with convergence guarantee to learn a unified graph for multi-view data clustering via cross-view graph diffusion (CGD), which is the first attempt to employ diffusion process for multi-view clustering. The proposed CGD takes the traditional predefined graph matrices of different views as input, and learns an improved graph for each single view via an iterative cross diffusion process by 1) capturing the underlying manifold geometry structure of original data points, and 2) leveraging the complementary information among multiple graphs. The final unified graph used for clustering is obtained by averaging the improved view associated graphs. Extensive experiments on several benchmark datasets are conducted to demonstrate the effectiveness of the proposed method in terms of seven clustering evaluation metrics.