FALPS:大规模P2P系统网络仿真平台

随着P2P技术的发展,传统的网络仿真平台已经不能满足研究需要。如何设计支持大规模P2P内容分发仿真的平台是亟待解决的问题。通过建立BackboneNet模型,并采取了"事件"合并、非尾片段丢弃"、事件队列"大小控制三个关键算法设计了一种用于大规模P2P内容分发系统的包级离散事件驱动网络仿真平台FALPS。该平台具有内存消耗低、速度快、精确度高的特点,可用于仿真具有105数量级节点规模的P2P系统。     

Spike-driven synaptic plasticity: theory, simulation, VLSI implementation

We present a model for spike-driven dynamics of a plastic synapse, suited for aVLSI implementation. The synaptic device behaves as a capacitor on short timescales and preserves the memory of two stable states (efficacies) on long timescales. The transitions (LTP/LTD) are stochastic because both the number and the distribution of neural spikes in any finite (stimulation) interval fluctuate, even at fixed pre- and postsynaptic spike rates. The dynamics of the single synapse is studied analytically by extending the solution to a classic problem in queuing theory (Takacs process). The model of the synapse is implemented in aVLSI and consists of only 18 transistors. It is also directly simulated. The simulations indicate that LTP/LTD probabilities versus rates are robust to fluctuations of the electronic parameters in a wide range of rates. The solutions for these probabilities are in very good agreement with both the simulations and measurements. Moreover, the probabilities are readily manipulable by variations of the chip's parameters, even in ranges where they are very small. The tests of the electronic device cover the range from spontaneous activity (3-4 Hz) to stimulus-driven rates (50 Hz). Low transition probabilities can be maintained in all ranges, even though the intrinsic time constants of the device are short (approximately 100 ms). Synaptic transitions are triggered by elevated presynaptic rates: for low presynaptic rates, there are essentially no transitions. The synaptic device can preserve its memory for years in the absence of stimulation. Stochasticity of learning is a result of the variability of interspike intervals; noise is a feature of the distributed dynamics of the network. The fact that the synapse is binary on long timescales solves the stability problem of synaptic efficacies in the absence of stimulation. Yet stochastic learning theory ensures that it does not affect the collective behavior of the network, if the transition probabilities are low and LTP is balanced against LTD.     

An overhead reducing technique for Time Warp

In this paper, we introduce a technique to reduce the number of state savings and the event queue size of Time Warp. By reducing the state saving and the sizes of event queues, we can decrease the overhead and the maximum memory requirement in Time Warp. We exploit the look-ahead technique to get a lower bound time stamp of the next event and to determine if an event is safe to be executed. No State saving is carried out when the event execution is safe. This lower bound can be used to discard saved states even though the time stamps are greater than the global virtual time (GVT). We prove that the proposed technique is correct under both aggressive and lazy cancellation schemes. This technique can be implemented with minimal additional overhead. Benchmark results on logic simulation show that the mechanism can reduce the number of state savings and memory size requirements significantly.     

多人在线网络游戏服务器的设计与开发

服务器的效率是网络游戏能否提供高质量的网络服务的重要因素之一.本文针对这一问题,提出了模板化回调策略改良现有的网络游戏的会话信息交互.文中基于VisualStudio与C++,重点剖析服务器架设过程中的关键技术和策略,设计开发了经典网络人物扮演游戏服务器,分别由“数据库”、“账号服务器”、“网络服务”和“副本”组成.实验结果表明,采用模板化回调技术,有效降低服务器信息交互的延迟.实践证明,本文所讨论的关键技术可应用于场景漫游、游戏开发等多个领域,具有一定的实际应用价值.     

延迟容忍传感器网络中面向发布/订阅系统的事件传输

提出了一种延迟容忍无线传感器网络(delay tolerant sensor network,简称DTSN)中基于团体的发布/订阅系统事件传输协议CET(community-based event transmitting protocol).CET的核心思想是,网络中所有传感器节点依据它们的相互连通性形成若干个固定的团体(community),并基于这些团体进行事件的传输.CET协议由事件传输和队列管理两部分组成.在事件传输策略中,事件除了尽可能地传递给移动订阅者之外,移动订阅者保存的某些事件还回传给团体内的传感器节点以提高事件的传输成功率.队列管理则根据事件的成功传输次数和生存时间来共同决定存储队列中事件的重要程度和丢弃原则,以降低网络传输能耗.仿真分析表明,与直接收集DG(direct gathering)相比,CET能够以较低的事件传输能耗和传输延迟获得较高的事件传输成功率.     

Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning

In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimizes by gradient ascent the likelihood of postsynaptic firing at one or several desired firing times. We find that the optimal strategy of up- and downregulating synaptic efficacies depends on the relative timing between presynaptic spike arrival and desired postsynaptic firing. If the presynaptic spike arrives before the desired postsynaptic spike timing, our optimal learning rule predicts that the synapse should become potentiated. The dependence of the potentiation on spike timing directly reflects the time course of an excitatory postsynaptic potential. However, our approach gives no unique reason for synaptic depression under reversed spike timing. In fact, the presence and amplitude of depression of synaptic efficacies for reversed spike timing depend on how constraints are implemented in the optimization problem. Two different constraints, control of postsynaptic rates and control of temporal locality, are studied. The relation of our results to spike-timing-dependent plasticity and reinforcement learning is discussed.     

Dynamically Reconfigurable Silicon Array of Spiking Neurons With Conductance-Based Synapses

A mixed-signal very large scale integration (VLSI) chip for large scale emulation of spiking neural networks is presented. The chip contains 2400 silicon neurons with fully programmable and reconfigurable synaptic connectivity. Each neuron implements a discrete-time model of a single-compartment cell. The model allows for analog membrane dynamics and an arbitrary number of synaptic connections, each with tunable conductance and reversal potential. The array of silicon neurons functions as an address-event (AE) transceiver, with incoming and outgoing spikes communicated over an asynchronous event-driven digital bus. Address encoding and conflict resolution of spiking events are implemented via a randomized arbitration scheme that ensures balanced servicing of event requests across the array. Routing of events is implemented externally using dynamically programmable random-access memory that stores a postsynaptic address, the conductance, and the reversal potential of each synaptic connection. Here, we describe the silicon neuron circuits, present experimental data characterizing the 3 mm times 3 mm chip fabricated in 0.5-mum complementary metal-oxide-semiconductor (CMOS) technology, and demonstrate its utility by configuring the hardware to emulate a model of attractor dynamics and waves of neural activity during sleep in rat hippocampus     

Intrinsic stabilization of output rates by spike-based Hebbian learning.

We study analytically a model of long-term synaptic plasticity where synaptic changes are triggered by presynaptic spikes, postsynaptic spikes, and the time differences between presynaptic and postsynaptic spikes. The changes due to correlated input and output spikes are quantified by means of a learning window. We show that plasticity can lead to an intrinsic stabilization of the mean firing rate of the postsynaptic neuron. Subtractive normalization of the synaptic weights (summed over all presynaptic inputs converging on a postsynaptic neuron) follows if, in addition, the mean input rates and the mean input correlations are identical at all synapses. If the integral over the learning window is positive, firing-rate stabilization requires a non-Hebbian component, whereas such a component is not needed if the integral of the learning window is negative. A negative integral corresponds to anti-Hebbian learning in a model with slowly varying firing rates. For spike-based learning, a strict distinction between Hebbian and anti-Hebbian rules is questionable since learning is driven by correlations on the timescale of the learning window. The correlations between presynaptic and postsynaptic firing are evaluated for a piecewise-linear Poisson model and for a noisy spiking neuron model with refractoriness. While a negative integral over the learning window leads to intrinsic rate stabilization, the positive part of the learning window picks up spatial and temporal correlations in the input.     

基于突触离子通道动力学神经元网络的高效并行仿真算法

在计算神经科学领域,大规模神经元网络的并行仿真对探索和揭示生物大脑中信息传递机制有着重要作用。为加速大规模神经元网络仿真,提出一种模块独立性强、耦合度低的基于突触递质-受体离子通道动力学的神经元网络的并行算法。通过分析化学突触信息传递机理及递质分子、受体离子通道动力学特征,提出了递质-受体计算分离的思想,增强了突触前神经元引起的递质分子浓度计算与突触后绑定状态的受体浓度计算之间的独立性,降低突触电流计算中突触前神经元状态和突触后神经元状态之间的耦合度。基于上述思想,设计并实现了一种生物神经网络并行算法。仿真结果表明了该算法的高效性。     

Analysis and design of controllers for AQM routers supporting TCPflows

In active queue management (AQM), core routers signal transmission control protocol (TCP) sources with the objective of managing queue utilization and delay. It is essentially a feedback control problem. Based on a recently developed dynamic model of TCP congestion-avoidance mode, this paper does three things: 1) it relates key network parameters such as the number of TCP sessions, link capacity and round-trip time to the underlying feedback control problem; 2) it analyzes the present de facto AQM standard: random early detection (RED) and determines that REDs queue-averaging is not beneficial; and 3) it recommends alternative AQM schemes which amount to classical proportional and proportional-integral control. We illustrate our results using ns simulations and demonstrate the practical impact of proportional-integral control on managing queue utilization and delay     

Reducing the variability of neural responses: a computational theory of spike-timing-dependent plasticity

Experimental studies have observed synaptic potentiation when a presynaptic neuron fires shortly before a postsynaptic neuron and synaptic depression when the presynaptic neuron fires shortly after. The dependence of synaptic modulation on the precise timing of the two action potentials is known as spike-timing dependent plasticity (STDP). We derive STDP from a simple computational principle: synapses adapt so as to minimize the postsynaptic neuron's response variability to a given presynaptic input, causing the neuron's output to become more reliable in the face of noise. Using an objective function that minimizes response variability and the biophysically realistic spike-response model of Gerstner (2001), we simulate neurophysiological experiments and obtain the characteristic STDP curve along with other phenomena, including the reduction in synaptic plasticity as synaptic efficacy increases. We compare our account to other efforts to derive STDP from computational principles and argue that our account provides the most comprehensive coverage of the phenomena. Thus, reliability of neural response in the face of noise may be a key goal of unsupervised cortical adaptation.     

Synaptic redistribution and variability of signal release probability of Hebbian neurons at low-firing frequencies in a dynamic stochastic neural network

This paper presents the finding of the research we conducted to evaluate the variability of signal release probability at Hebb’s presynaptic neuron under different firing frequencies in a dynamic stochastic neural network. A modeled neuron consisted of thousands of artificial units, called ‘transmitters’ or ‘receptors’ which formed dynamic stochastic synaptic connections between neurons. These artificial units were two-state stochastic computational units that updated their states according to the signal arriving time and their local excitation. An experiment was conducted with three stages by updating the firing frequency of Hebbian neuron at each stage. According to our results, synaptic redistribution has improved the signal transmission for the first few signals in the signal train by continuously increasing and decreasing the number of postsynaptic ‘active-receptors’ and presynaptic ‘active-transmitters’ within a short time period. In long-run, at low-firing frequency, it has increased the steady state efficacy of the synaptic connection between the Hebbian presynaptic and the postsynaptic neuron in terms of the signal release probability of ‘active-transmitters’ in the presynaptic neuron as observed in biology. This ‘low-firing’ frequency of the presynaptic neuron has been identified by the network by comparing it with the ongoing frequency oscillation of the network.     

Detecting and estimating signals over noisy and unreliable synapses: information-theoretic analysis

The temporal precision with which neurons respond to synaptic inputs has a direct bearing on the nature of the neural code. A characterization of the neuronal noise sources associated with different sub-cellular components (synapse, dendrite, soma, axon, and so on) is needed to understand the relationship between noise and information transfer. Here we study the effect of the unreliable, probabilistic nature of synaptic transmission on information transfer in the absence of interaction among presynaptic inputs. We derive theoretical lower bounds on the capacity of a simple model of a cortical synapse under two different paradigms. In signal estimation, the signal is assumed to be encoded in the mean firing rate of the presynaptic neuron, and the objective is to estimate the continuous input signal from the postsynaptic voltage. In signal detection, the input is binary, and the presence or absence of a presynaptic action potential is to be detected from the postsynaptic voltage. The efficacy of information transfer in synaptic transmission is characterized by deriving optimal strategies under these two paradigms. On the basis of parameter values derived from neocortex, we find that single cortical synapses cannot transmit information reliably, but redundancy obtained using a small number of multiple synapses leads to a significant improvement in the information capacity of synaptic transmission.     

Smart priority queue algorithms for self-optimizing event storage

Low run-time overhead, self-adapting storage policies for priority queues called smart priority queue (SPQ) techniques are developed and evaluated. The proposed SPQ policies employ a low-complexity linear queue for near-head activities and a rapid-indexing variable bin-width calendar queue for distant events. The SPQ configuration is determined by monitoring queue access behavior using cost-scoring factors and then applying heuristics to adjust the organization of the underlying data structures. To illustrate and evaluate the method, an SPQ-based scheduler for discrete event simulation has been implemented and was used to assess the resulting efficiency, components of access time, and queue usage distributions of the existing and proposed algorithms. Results indicate that optimizing storage to the spatial distribution of queue access can decrease HOLD operation cost between 25% and 250% over existing algorithms such as calendar queues.     

A simulator for adaptive parallel applications

Dynamically allocating computing nodes to parallel applications is a promising technique for improving the utilization of cluster resources. Detailed simulations can help identify allocation strategies and problem decomposition parameters that increase the efficiency of parallel applications. We describe a simulation framework supporting dynamic node allocation which, given a simple cluster model, predicts the running time of parallel applications taking CPU and network sharing into account. Simulations can be carried out without needing to modify the application code. Thanks to partial direct execution, simulation times and memory requirements are reduced. In partial direct execution simulations, the application's parallel behavior is retrieved via direct execution, and the duration of individual operations is obtained from a performance prediction model or from prior measurements. Simulations may then vary cluster model parameters, operation durations and problem decomposition parameters to analyze their impact on the application performance and identify the limiting factors. We implemented the proposed techniques by adding direct execution simulation capabilities to the Dynamic Parallel Schedules parallelization framework. We introduce the concept of dynamic efficiency to express the resource utilization efficiency as a function of time. We verify the accuracy of our simulator by comparing the effective running time, respectively the dynamic efficiency, of parallel program executions with the running time, respectively the dynamic efficiency, predicted by the simulator under different parallelization and dynamic node allocation strategies.     

An algorithm for modifying neurotransmitter release probability based on pre- and postsynaptic spike timing

The precise times of occurrence of individual pre- and postsynaptic action potentials are known to play a key role in the modification of synaptic efficacy. Based on stimulation protocols of two synaptically connected neurons, we infer an algorithm that reproduces the experimental data by modifying the probability of vesicle discharge as a function of the relative timing of spikes in the pre- and postsynaptic neurons. The primary feature of this algorithm is an asymmetry with respect to the direction of synaptic modification depending on whether the presynaptic spikes precede or follow the postsynaptic spike. Specifically, if the presynaptic spike occurs up to 50 ms before the postsynaptic spike, the probability of vesicle discharge is upregulated, while the probability of vesicle discharge is downregulated if the presynaptic spike occurs up to 50 ms after the postsynaptic spike. When neurons fire irregularly with Poisson spike trains at constant mean firing rates, the probability of vesicle discharge converges toward a characteristic value determined by the pre- and postsynaptic firing rates. On the other hand, if the mean rates of the Poisson spike trains slowly change with time, our algorithm predicts modifications in the probability of release that generalize Hebbian and Bienenstock-Cooper-Munro rules. We conclude that the proposed spike-based synaptic learning algorithm provides a general framework for regulating neurotransmitter release probability.     

Critical analysis of dimension reduction by a moment closure method in a population density approach to neural network modeling

Computational techniques within the population density function (PDF) framework have provided time-saving alternatives to classical Monte Carlo simulations of neural network activity. Efficiency of the PDF method is lost as the underlying neuron model is made more realistic and the number of state variables increases. In a detailed theoretical and computational study, we elucidate strengths and weaknesses of dimension reduction by a particular moment closure method (Cai, Tao, Shelley, & McLaughlin, 2004; Cai, Tao, Rangan, & McLaughlin, 2006) as applied to integrate-and-fire neurons that receive excitatory synaptic input only. When the unitary postsynaptic conductance event has a single-exponential time course, the evolution equation for the PDF is a partial differential integral equation in two state variables, voltage and excitatory conductance. In the moment closure method, one approximates the conditional kth centered moment of excitatory conductance given voltage by the corresponding unconditioned moment. The result is a system of k coupled partial differential equations with one state variable, voltage, and k coupled ordinary differential equations. Moment closure at k = 2 works well, and at k = 3 works even better, in the regime of high dynamically varying synaptic input rates. Both closures break down at lower synaptic input rates. Phase-plane analysis of the k = 2 problem with typical parameters proves, and reveals why, no steady-state solutions exist below a synaptic input rate that gives a firing rate of 59 s(1) in the full 2D problem. Closure at k = 3 fails for similar reasons. Low firing-rate solutions can be obtained only with parameters for the amplitude or kinetics (or both) of the unitary postsynaptic conductance event that are on the edge of the physiological range. We conclude that this dimension-reduction method gives ill-posed problems for a wide range of physiological parameters, and we suggest future directions.     

一种具有ECN能力的智能分组丢弃算法

作为端到端拥塞控制机制的有效补充,主动队列管理旨在保证高链路利用率的同时维持较低的排队延迟.FIPD(fuzzy intelligent packet dropping)算法作为一种有效的机制,为主动队列管理提供了全新的方法,但是,FIPD也有其本身固有的缺点,比如居高不下的分组丢失率.旨在克服FIPD这些固有缺点的同时,提出一种新的主动队列管理方案FIPE(FIPD with ECN).首先对FIPD算法进行了总结,并对其本身的优缺点进行了分析,针对FIPD算法分组丢失率高居不下等缺点,引进了众所周知的     

改进的RED队列管理算法：RED-r

为了避免RED缺陷,提出一种改进的RED算法——RED-r。该算法采用二次圆函数来计算丢包概率,减少了RED的设置参数,实现了在网络大延时和小延时时的队列稳定,且在小延时能获得比PID队列更平滑的效果。NS2仿真验证了RED-r算法的有效性。