ࡱ> XZW[ RPbjbjAΐΐ584\X\Xe e f f ffff8Wf3gTffgmjmjmjmjkrKl,wlđƑƑƑƑƑƑ$FQ fGnkkGnGneemjmjh;sssGne8mj fmjđsGnđssڎemj`1QEfp0*Q0NϘr?T"ϘL$ flZl@s)m4]mlllr$lllGnGnGnGn?lllllllll\X kd: Analytical Modelling of Weighted Round Robin Service StrategyDapeng Yang1, Sijun Liu, and Keming Wang1, 31 College of Industrial Engineering, Zhejiang University, Hangzhou, 310058, China2 College of Computer Science, Shenzhen University, Shenzhen, 518060, China3 School of Mechanical Engineering, Tsinghua University, Beijing, 100083, ChinaE-mail: sijunliu@163.comAbstract. This paper presents a mathematical description of Weighted Round Robin service strategy, on which basis all the perspective QoS tools that serve to manage congestion in converged packet networks work. On the basis of the presented mathematical model, it is possible to suitably configure the operational parameters (maximum queue length, distribution of the available transfer capacity) of these tools according to required values of packet delays. The implementation of the analytical model is demonstrated on a real network segment using advanced network data traffic emulator. This paper presents a mathematical description of Weighted Round Robin service strategy, on which basis all the perspective QoS tools that serve to manage congestion in converged packet networks work. On the basis of the presented mathematical model, it is possible to suitably configure the operational parameters (maximum queue length, distribution of the available transfer capacity) of these tools according to required values of packet delays. The implementation of the analytical model is demonstrated on a real network segment using advanced network data traffic emulator.IntroductionIn converged IP networks, it is necessary to provide, apart from the conventional data operation, also the video and voice communication support. Because IP packet architecture was not originally intended for real-time transfers, from the point of view of interactive communication it is necessary to solve the problem of quality guarantee of multimedia servicesQuality of Service (QoS). Suitably implemented QoS tools ought to guarantee a certain quality level of processing of time-sensitive interactive applications at the expense of the quality of processing of other applications that do not necessarily require real-time transfers. Without properly implemented QoS tools, the applications that do not require real-time processing might use up the available transfer capacity and in this way make impossible the transfer of time-sensitive interactive applications. The main problem of IP packet network is thus the appropriate support of multimedia communication taking place in real time and the connected suitable management of potentially occurring congestion. The appropriate implementation of QoS tools mainly requires correct setting of the operation parameters of these tools. In order to provide high Quality-of-Service (QoS) in todays high-speed converged networks, WRR mechanism assigns different priorities to different queues. WRR also ensures fair selection interval among all active queues with minimal delay and jitter [1]. In this scheduling algorithm, a weighting coefficient for each queue determines how many bytes of data the system delivers from the queue before it moves on to the next queue. The WRR mechanism cycles through the queues. For each queue, packets are sent until the number of bytes transmitted exceeds the bandwidth determined by the queues weighting coefficient, or the queue is empty [2, 3]. Then the WRR mechanism moves to the next queue. If a queue is empty, WRR mechanism will send packets from the next queue that has packets ready to send. This mechanism guarantees a minimum bandwidth to each queue, and allows the minimum to be exceeded if one or more of the ports other queues are idle. However, when all the queues are located, each is limited to its maximum bandwidth according to its assigned weightno queue achieves more than a predetermined portion of transfer capacity when the transmission line is under stress [4]. WRR includes several significant benefits. This scheduling mechanism can be implemented in hardware, so it can be applied to high-speed interfaces in both the core and at the edges of the network. WRR mechanism also ensures that all service classes have access to at least some configured amount of network capacity to avoid bandwidth starvation. WRR queuing provides coarse control over the percentage of output port bandwidth allocated to each service class. Classification of traffic by service class provides more equitable management and more stability for network applications than the use of priorities or preferences. WRR queuing is based on the belief that resource reduction is a better mechanism to control congestion than resource denial [1, 2]. Other information about WRR models and detailed description of the status quo can be found in source [4].Experimental MethodThe properties of three core samples used for the experiments are given in Table 1. Table 1The properties of core samplesCore numberLength/ cmDiameter /cmPermeability/mDA#5.242.5110.017B#5.6592.5350.0287C#4.5352.5080.0675These core samples are utilized after washing out any oil in the sample and then drying. Dry nitrogen is used as an experimental gas source. The mass flow meter is used to measure gas flow. Confining pressure is controlled and regulated using a hand pump. The experiments are conducted by changing the pore pressure according to the development mode of the gas reservoir. In accordance with the well depth of the core samples and the data of the oilfield, the initial formation pressure was 30Mpa, and the overburden pressure was 50Mpa. The confining pressure was kept 1Mpa more than the pore pressure. And the confining pressure and the pore pressure were added in the meantime until the pore pressure reach at 30Mpa. Then, the confining pressure was added to 50Mpa. The confining pressure was kept constant, and the pore pressure was gradually reduced as the step was 2Mpa. The permeability was measured by steady state method. Result and AnalysesTo evaluate formation stress sensitivity, we first normalize the permeability and the effective stress. Figure 1-3 present the relationship between dimensionless permeability and dimensionless effective stress. Figure 1. The relationship between dimensionless permeability and dimensionless effective stress of A#.Figure 2. The relationship between dimensionless permeability and dimensionless effective stress of B#.Echo signal analysis in the 2D frequency domainConsider radar emitting a series of chirp pulses  EMBED Equation.DSMT4  (1)where EMBED Equation.DSMT4   EMBED Equation.DSMT4  is the time,  EMBED Equation.DSMT4  is the number of pulses,  EMBED Equation.DSMT4  is the pulse repetition interval,  EMBED Equation.DSMT4  is the carrier frequency,  EMBED Equation.DSMT4  is the pulse width,  EMBED Equation.DSMT4  is the frequency modulation rate and  EMBED Equation.DSMT4 ,  EMBED Equation.DSMT4  is the bandwidth (the pulse compression ratio EMBED Equation.DSMT4 ).Now suppose this pulse train illuminates a moving target at range  EMBED Equation.DSMT4 , and the echo signal is recorded in a two-dimensional array  EMBED Equation.DSMT4 , where  EMBED Equation.DSMT4  is known as the fast time and  EMBED Equation.DSMT4  is known as the slow time. After downconversion, the received baseband signal can be written as  EMBED Equation.DSMT4  (2)where  EMBED Equation.DSMT4  is constant and determined by the RCS of the target,  EMBED Equation.DSMT4  is the coherent integrated time, the range of the target can be expressed as  EMBED Equation.DSMT4  (3)where  EMBED Equation.DSMT4  is the initial range,  EMBED Equation.DSMT4  is the radial velocity of the target,  EMBED Equation.DSMT4  is the radial acceleration of the target. The Fourier transform of Equation (2) over the fast time  EMBED Equation.DSMT4  can be written as  EMBED Equation.DSMT4  (4)where  EMBED Equation.DSMT4  is the fast frequency,  EMBED Equation.DSMT4  is the Fourier transform of  EMBED Equation.DSMT4 , and we know that  EMBED Equation.DSMT4  (5)After matched filtering(pulse compression) in fast-time-domain corresponding to Equation (4), we can obtain  EMBED Equation.DSMT4  (6)We can see that the position of the signal peak varies with  EMBED Equation.DSMT4 . That is to say, range cell migration occurs. A novel method of long term coherent integration detectionThe process of the novel method and the mathematical derivationThe basic idea of the method in this paper is that transform  EMBED Equation.DSMT4  to  EMBED Equation.DSMT4  by keystone transform in the fast frequency-slow time domain firstly to correct the range cell migration. Then quadratic phase compensation function  EMBED Equation.DSMT4  is multiplied, and we can get the expression  EMBED Equation.DSMT4  in fast frequency-slow time domain. Next, we use the expression  EMBED Equation.DSMT4  to construct the objective function EMBED Equation.DSMT4 , thus, we use this function to search the acceleration in the compensation function. The acceleration corresponding to the maximum value of the objective function is the optimal estimator  EMBED Equation.DSMT4  of the targets real acceleration  EMBED Equation.DSMT4 . At the same time, the maximum value of the objective function equals to the coherent integration value. The flowchart of this method is shown in Figure 1.What we can know from (15) is that the range cell migration is already corrected, and the coherent integration is performed in slow time domain. The peak of the spectrum is the intersection of  EMBED Equation.DSMT4  and  EMBED Equation.DSMT4 , and the corresponding value is the time delay of the targets initial range and the Doppler frequency of the initial velocity, respectively.Because the acceleration of the quadratic phase compensation function  EMBED Equation.DSMT4  is unknown, it must be estimated. We use the variable step length search method in this paper. Firstly, we construct the objective function:The problems of realizing the novel method The implementation method of the keystone transform. There are many approaches to realize the keystone transform such as sinc interpolation algorithm, DFT-IFFT algorithm, Chirp-Z-IFFT algorithm, etc[7,10]. The realization of these methods can be inquired in the references, so we dont introduce here. We use the Chirp-Z-IFFT algorithm in this paper because the sinc interpolation algorithm and the DFT-IFFT algorithm are more complicated. The keystone transform of undersampled in the slow time domain. Range cell migration correction using keystone transform can still be done even if the data is undersampled in the slow time domain. But it must be multiplied by a fold factor. The expression of the fast frequency-slow time after quadratic phase compensation is ConclusionsDVE technology has the characteristics of regional and all objects in the virtual world need part into different regions. The operation of the virtual world also needs server support. On the one hand, users in DVE system are moving. On the other hand, these users can enter and leave the network at any time. Therefore, this needs to provide a good and effective mechanism to solve the problem of regional division and server distribution in the system. This paper presents a cluster-based server divide method to realize part region and server distribution. In order to achieve clustering division, we use DCA method to calculate each users view which combines the MinPts value with Theorem 1. In addition, theoretical analysis and experimental simulation is carried out to verify the validity and rationality of the DCA algorithm in this paper.AcknowledgementsThe results discussed in this paper have been obtained by the CBELSA/TAPS and Crystal Barrel/TAPS collaborations. They are part of the theses works of M. Dieterle, I. Jaegle, Y. Magrhbi, F. Pheron, D. Werthmuller, and L. Witthauer. 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Appl. Phys. 92 3683 [14] Kurata M 1982 Numerical Analysis for Semiconductor Devices (Lexington, MA: Heath)[15] Selberherr S 1984 Analysis and Simulation of Semiconductor Devices (Berlin: Springer) To whom any correspondence should be addressed.ABDEFGHIJKLMNOPSgdpF8?&P /R ;0?PAPBP . 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