NeuSoft Research

Shenyang, China

NeuSoft Research

Shenyang, China
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Wen Y.,Northeastern University China | Li Z.,Neusoft Research | Jin S.,Sun Yat Sen University | Lin C.,Northeastern University China | liu Z.,Northeastern University China
IEEE Access | Year: 2017

In recent years, with the development of cloud computing technology, the size of a data center is expanding rapidly. To minimize the energy consumption of a data center, we propose an energy-efficient virtual resource dynamic integration (VRDI) method. In the proposed VRDI method, first, by monitoring the load patterns of the physical machines (PMs) and the corresponding thresholds of PMs calculated using the statistical data, we propose a PM selection algorithm to find a set of PMs which should be integrated. Further, we propose a VM selection algorithm based on minimum migration policy to select the VMs that are deployed on the integrated PMs. Finally, to solve the target VM placement, we propose a VM placement algorithm based on an improved genetic algorithm. Using the encoding, crossover and mutation operations of the genetic algorithm, we obtain an effective solution for the VM placement problem. The experiments show that the proposed VRDI method can reduce the energy consumption of data center and ensure the Quality of Service(QoS) of the cloud applications developed on the VMs. OAPA

He G.-Y.,Northeastern University China | He G.-Y.,NeuSoft Research | Wen Y.-Y.,NeuSoft Research | Zhao H.,Northeastern University China
Jisuanji Xuebao/Chinese Journal of Computers | Year: 2012

Using classic One-Way Function as the basis, a SPIT prevention method is proposed and verified in this paper. Adopting the resource challenge mechanism, spitter is forced to consume huge system resource to solve the puzzle in order to send the session invite. The puzzle design algorithm avoids deficiencies of relevant research, makes the method more security and reliable. The puzzle solving algorithm consumes both the CPU and Memory of spitter, then reduces the consumption gap of puzzle solving during terminals with different configuration. Experiment and analysis show the efficiency and applicability of the method.

Wen Y.-Y.,Northeastern University China | Wen Y.-Y.,Neusoft Research | Zhao B.,Northeastern University China | Zhao H.,Neusoft Research
Tongxin Xuebao/Journal on Communications | Year: 2014

According to the selfishness of rational mobile ad hoc network nodes showed during the packet forwarding, the selfish behavior statically and dynamically based on game theory were analyzed and modeled. A stern tit for tat strategy (STFT) was proposed to motivate node cooperation, and an infinite repeated game model was established to analyze the node behavior. Then, an incentive-compatible condition was obtained analytically. The dynamic process of selfish node turning to cooperate using the evolutionary game theory was studied, and the evolutionary stability of STFT was proved. Simulation results show that, even if the ratio of selfish nodes is one, by setting punishment parameters reasonably, the overall network performance can be improved 80% at most.

Yingyou W.,Northeastern University China | Yingyou W.,Neusoft Research | Zhi L.,Northeastern University China | Zhi L.,Neusoft Research | And 2 more authors.
Ad-Hoc and Sensor Wireless Networks | Year: 2016

Node localization plays an important role in many current and envisioned WSN applications. Most of existing algorithms are not applicable for networks in a concave area. To address this problem, a novel greedy optimization localization algorithm (GOLA) is proposed. The key of GOLA is the design of neighborhood function which only uses distances between neighbor nodes to generate a new set of estimated locations from an old one, two correction operations are used to alleviate the flip ambiguity. Furthermore, a method of initial solution generation is given. Both rangebased and range-free localization can use GOLA to estimate locations of nodes if distances between neighbor nodes are measured by hardware or estimated by distance estimation algorithms. Simulation results indicate that GOLA can achieve more accurate and reliable localization results in a concave area. ©2016 Old City Publishing, Inc.

Sun J.S.,Northeastern University China | Wen Y.Y.,Northeastern University China | Wen Y.Y.,Neusoft Research | Liu Y.H.,Neusoft Research | And 2 more authors.
Applied Mechanics and Materials | Year: 2014

Delay and packet loss caused by the basic mobile IPv6 handover protocol can not guarantee the service quality of real-time communication. In order to improve the performance of fast handover of the mobile IPv6, we proposed a fast handover scheme based on hierarchical mobility which combines micro mobility and the link layer trigger. By shielding local mobility of the mobile node, the scheme can greatly reduce the signaling information traffic from mobile node to HA and CN. Through the link layer trigger mechanism, the scheme builds a tunnel in advance to avoid packet loss. In order to verify the performance of the proposed scheme, we did some simulation experiments in the NS-2 environment. The simulation results show that the scheme reduces handover delay and packet loss effectively. © (2014) Trans Tech Publications, Switzerland.

Yingyou W.,Northeastern University China | Yingyou W.,Neusoft Research | Zhi L.,Northeastern University China | Zhi L.,Neusoft Research | And 3 more authors.
International Journal of Distributed Sensor Networks | Year: 2015

Range-free localization plays an important role in low-cost and large scale wireless sensor networks. Many existing range-free localization methods encounter high localization error, especially for the network with a coverage hole. One reason for high localization error is unreasonable distance estimation method. Another reason is that unknown nodes use the shortest distance which has large cumulative distance error to estimate their positions. In this paper, a two-stage centralized range-free localization algorithm (TCRL) is proposed. In the first stage, we design a novel rational distance estimation method to alleviate the distance estimation error between neighbor nodes based on the connectivity information and geometric features. In the second stage, a novel neighborhood function is derived from the estimated distances between neighbor nodes. Then a new localization strategy is proposed based on greedy idea. Finally, the proposed algorithm is compared with the same type algorithms in two network scenarios, namely, random deployment and random deployment with a coverage hole. The simulation results show that TCRL achieves more accurate and reliable results than most of existing range-free methods in the two network scenarios. © 2015 Wen Yingyou et al.

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