Perturbation analysis for online control and optimization of stochastic fluid models

Uses stochastic fluid models (SFMs) for control and optimization (rather than performance analysis) of communication networks, focusing on problems of buffer control. We derive gradient estimators for packet loss and workload related performance metrics with respect to threshold parameters. These estimators are shown to be unbiased and directly observable from a sample path without any knowledge of underlying stochastic characteristics, including traffic and processing rates (i.e., they are nonparametric). This renders them computable in online environments and easily implementable for network management and control. We further demonstrate their use in buffer control problems where our SFM-based estimators are evaluated based on data from an actual system.     

Perturbation analysis and control of two-class stochastic fluid models for communication networks

This paper uses stochastic fluid models (SFMs) for the control and optimization (rather than performance analysis) of communication network nodes processing two classes of traffic: one is uncontrolled and the other is subject to threshold-based buffer control. We derive gradient estimators for packet loss and workload related performance metrics with respect to threshold parameters. These estimators are shown to be unbiased and directly observable from a sample path without any knowledge of underlying stochastic characteristics of the traffic processes. This renders them computable in online environments and easily implementable for network management and control. We further demonstrate their use in buffer control problems where our SFM-based estimators are evaluated based on data from an actual system.     

基于无线传感器网络的低能耗近似查询算法

目前大量无线传感器网络应用都要求查询或上报具有某种集合类型的数据,如节点平均温度值,该文针对其中精确度受限的单步近似查询问题提出了2个低能耗的查询算法,通过基于NS-2扩展的模拟网络环境,对算法进行了验证和性能分析,达到了较好的性能。对影响应用系统性能的重要参数即算法中的精确度参数的最优化配置进行了讨论。     

过程控制研究的一些进展

随着新材料、新工艺的不断涌现,对反馈控制系统性能的要求更加严格和精确,在过程控制领域出现了许多新的挑战性研究问题.首先综述了非线性分布参数过程(例如材料微结构、颗粒分布、流体流动)的控制、生物系统与生物医学过程的分析与控制、城市污水处理集成监控系统等的最新研究进展以及一些未来的研究方向.其次讨论了解决复杂控制问题所需的一些方法和集成工具,主要包括过程系统工程理论和方法、集成物理装置与通讯网络的控制系统组成等.最后指出了目前控制应用相对薄弱的领域是控制理论与工程界关注的重点.     

资源约束网络的优化带宽调度

在资源约束的网络控制系统中, 控制性能和服务质量之间的折衷是不可避免的. 为了寻求它们的最佳结合点, 提出了结合约束条件的多目标规划问题来优化控制性能和网络带宽需求. 考虑算法的非线性逼近能力和计算速度, 采用了神经网络作为优化求解器. 它提供的优化解对每一个控制回路的带宽需求进行动态分配, 使得全局系统性能最大化的同时使带宽需求最小化. 仿真表明在网络控制应用中该优化策略对控制性能和网络带宽需求之间是一种有效的折衷方法.     

催化裂化过程稳态优化控制研究

催化裂化装置是一个高度非线性、时变、长时延、强耦合、分布参数和不确定性的复杂 系统.在研究其过程机理的基础上,定义了一种模糊神经网络用以建模,用自相关函数检验法检 验模型的正确性,再用改进的Frank-Wolfe算法进行稳态优化计算,并以一炼油厂催化裂化装 置为对象进行试验,研究其辨识、建模和稳态优化控制.这种模糊神经网络具有隐层数多、隐层 结点数多、泛化能力和逼近能力强、收敛速度快的优点,更突出的特点还在于可由输出端对输入 求导,为稳态优化计算提供了极大方便,它与改进的Frank-Wolfe算法相结合用于解决非线性 复杂生产过程的建模和稳态优化控制问题是可行的.     

Iterative learning Kalman filter for repetitive processes

In this paper, a discrete-time iterative learning Kalman filter scheme is proposed for repetitive processes to reject repeatable disturbances as well as random noises. The proposed state estimator scheme integrates Kalman filter with iterative learning control. The estimation process contains two stages: a conventional Kalman filter is applied in the first stage; the second stage refines the estimates in an iterative learning fashion, leading to a gradual improvement on the estimation performance. According to the estimates that the first stage feeds to the second stage, the optimal design includes two types – posterior type and priori type. In order to reduce the memory and computation load of the optimal design, two suboptimal estimators are provided as well. The stability of the both suboptimal estimators is also studied. Furthermore, a lower bound is given to estimate the ultimate estimation performance before implementing any estimation. Finally, an illustrative example of injection molding is given to verify the performance of the four estimators developed.     

基于观测的POMDP 优化算法及其仿真

在分析马尔可夫决策过程（Markov Decision Process, MDP）性能灵敏度的基础上,讨论了部分可观 测马尔可夫决策过程（Partially Observable Markov Decision Process, POMDP）的性能优化问题．给出了POMDP 性能灵敏度分析公式,并以此为基础提出了两种基于观测的POMDP 优化算法：策略梯度优化算法和策略迭 代优化算法．最后以准许控制问题为仿真实例,验证了这两个算法的有效性．     

Improved estimation in applications involving multiple independent goals

An estimation procedure is presented for applications where each estimated signal is subsequently used individually, e.g., for process supervision or sensor calibration, as opposed to all signals used together, e.g., for optimization or control. The method employs multiple independent estimators in parallel, each of which is tuned to serve a single goal. It yields more accurate estimates than the conventional procedure, does not require trial-and-error specification of relative goal weights, and is less sensitive to under- or over-parameterization of the estimator. These advantages are demonstrated on a realistic simulation of a chemical batch reactor.     

基于半监督学习的动态神经网络结构设计

针对神经网络初始结构的设定依赖于工作者的经验、自适应能力较差等问题,提出一种基于半监督学习(SSL)算法的动态神经网络结构设计方法。该方法采用半监督学习方法利用已标记样例和无标记样例对神经网络进行训练,得到一个性能较为完善的初始网络结构,之后采用全局敏感度分析法(GSA)对网络隐层神经元输出权值进行分析,判断隐层神经元对网络输出的影响程度,即其敏感度值大小,适时地删减敏感度值很小的神经元或增加敏感度值较大的神经元,实现动态神经网络结构的优化设计,并给出了网络结构变化过程中收敛性的证明。理论分析和Matlab仿真实验表明,基于SSL算法的神经网络隐层神经元会随训练时间而改变,实现了网络结构动态设计。在液压厚度自动控制(AGC)系统应用中,大约在160 s时系统输出达到稳定,输出误差大约为0.03 mm,与监督学习(SL)方法和无监督学习(USL)方法相比,输出误差分别减小了0.03 mm和0.02 mm,这表明基于SSL算法的动态网络在实际应用中能有效提高系统输出的准确性。     

WATuning: A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning

Configuration tuning is essential to optimize the performance of systems (e.g., databases, key-value stores). High performance usually indicates high throughput and low latency. At present, most of the tuning tasks of systems are performed artificially (e.g., by database administrators), but it is hard for them to achieve high performance through tuning in various types of systems and in various environments. In recent years, there have been some studies on tuning traditional database systems, but all these methods have some limitations. In this article, we put forward a tuning system based on attention-based deep reinforcement learning named WATuning, which can adapt to the changes of workload characteristics and optimize the system performance efficiently and effectively. Firstly, we design the core algorithm named ATT-Tune for WATuning to achieve the tuning task of systems. The algorithm uses workload characteristics to generate a weight matrix and acts on the internal metrics of systems, and then ATT-Tune uses the internal metrics with weight values assigned to select the appropriate configuration. Secondly, WATuning can generate multiple instance models according to the change of the workload so that it can complete targeted recommendation services for different types of workloads. Finally, WATuning can also dynamically fine-tune itself according to the constantly changing workload in practical applications so that it can better fit to the actual environment to make recommendations. The experimental results show that the throughput and the latency of WATuning are improved by 52.6％ and decreased by 31％, respectively, compared with the throughput and the latency of CDBTune which is an existing optimal tuning method.     

Performance deterioration of optimal regulators incorporating state estimators

In this paper, the performance deterioration of optimal regulators caused by the introduction of deterministic state-estimators such as Kalman estimators and Luenberger observers is investigated. It is shown that, in the case of Kalman estimators, the performance deterioration cannot be made arbitrarily small unless the optimal control law can be implemented by direct output feedback. In the case of Luenberger observers with minimum order, a necessary and sufficient condition for the performance deterioration to be made arbitrarily small is derived.     

Exploring optimal combination of a file system and an I/O scheduler for underlying solid state disks

Performance and energy consumption of a solid state disk （SSD） highly depend on file systems and I/O schedulers in operating systems. To find an optimal combination of a file system and an I/O scheduler for SSDs, we use a metric called the aggregative indicator （AI）, which is the ratio of SSD performance value （e.g., data transfer rate in MB/s or throughput in IOPS） to that of energy consumption for an SSD. This metric aims to evaluate SSD performance per energy consumption and to study the SSD which delivers high performance at low energy consumption in a combination of a file system and an I/O scheduler. We also propose a metric called Cemp to study the changes of energy consumption and mean performance for an Intel SSD （SSD-I） when it provides the largest AI, lowest power, and highest performance, respectively. Using Cemp, we attempt to find the combination of a file system and an I/O scheduler to make SSD-I deliver a smooth change in energy consumption. We employ Filebench as a workload generator to simulate a wide range of workloads （i.e., varmail, fileserver, and webserver）, and explore optimM combinations of file systems and I/O schedulers （i.e., optimal values of AI） for tested SSDs under different workloads. Experimental results reveal that the proposed aggregative indicator is comprehensive for exploring the optimal combination of a file system and an I/O scheduler for SSDs, compared with an individual metric.     

城市污水处理过程动态多目标智能优化控制研究

城市污水处理过程(Municipal wastewater treatment process,MWWTP)是一个典型的复杂流程工业过程,其优化运行涉及到多个动态性能指标.为了实现城市污水处理运行过程的优化控制,本文提出了一种城市污水处理过程动态多目标智能优化控制方法(Dynamic multiobjective in...     

A framework based approach to the development of network awareapplications

Modern networks provide a QoS (quality of service) model to go beyond best-effort services, but current QoS models are oriented towards low-level network parameters (e.g., bandwidth, latency, jitter). Application developers, on the other hand, are interested in quality models that are meaningful to the end-user and, therefore, struggle to bridge the gap between network and application QoS models. Examples of application quality models are response time, predictability or a budget (for transmission costs). Applications that can deal with changes in the network environment are called network-aware. A network-aware application attempts to adjust its resource demands in response to network performance variations. This paper presents a framework-based approach to the construction of network-aware programs. At the core of the framework is a feedback loop that controls the adjustment of the application to network properties. The framework provides the skeleton to address two fundamental challenges for the construction of network-aware applications: how to find out about dynamic changes in network service quality; and how to map application-centric quality measures (e.g., predictability) to network-centric quality measures (e.g., QoS models that focus on bandwidth or latency). Our preliminary experience with a prototype network-aware image retrieval system demonstrates the feasibility of our approach. The prototype illustrates that there is more to network-awareness than just taking network resources and protocols into account and raises questions that need to be addressed (from a software engineering point of view) to make a general approach to network-aware applications useful     

On the System Performance of Mobile Edge Computing in an Uplink NOMA WSN With a Multiantenna Access Point Over Nakagami-m Fading

In this paper, we study the system performance of mobile edge computing (MEC) wireless sensor networks (WSNs) using a multiantenna access point (AP) and two sensor clusters based on uplink nonorthogonal multiple access (NOMA). Due to limited computation and energy resources, the cluster heads (CHs) offload their tasks to a multiantenna AP over Nakagami-m fading. We proposed a combination protocol for NOMA-MEC-WSNs in which the AP selects either selection combining (SC) or maximal ratio combining (MRC) and each cluster selects a CH to participate in the communication process by employing the sensor node (SN) selection. We derive the closed-form exact expressions of the successful computation probability (SCP) to evaluate the system performance with the latency and energy consumption constraints of the considered WSN. Numerical results are provided to gain insight into the system performance in terms of the SCP based on system parameters such as the number of AP antennas, number of SNs in each cluster, task length, working frequency, offloading ratio, and transmit power allocation. Furthermore, to determine the optimal resource parameters, i.e., the offloading ratio, power allocation of the two CHs, and MEC AP resources, we proposed two algorithms to achieve the best system performance. Our approach reveals that the optimal parameters with different schemes significantly improve SCP compared to other similar studies. We use Monte Carlo simulations to confirm the validity of our analysis.      

快速复杂网络聚类图形处理器并行算法

研究复杂网络拓扑属性的聚类算法需要处理大量节点和连接边,因此对计算性能要求高,否则无法处理现实中的表示为复杂网络的系统。利用图形处理器(GPU)的并行聚类算法是解决该问题的重要方法。利用原语技术设计并行快速聚类算法,原语法不仅降低并行算法的复杂性而且提高聚类的普适性;再从线程调度策略和缓存管理两个方面提出优化的方法来解决负载均衡和数据重用性问题。通过实验对比并行快速聚类算法与优化算法的性能,结果显示并行快速聚类优化后的算法取得较好加速比。     

基于软件定义网络的媒体分发网络的接入控制

针对软件定义网络（SDN）环境下的媒体分发网络的接入控制问题,提出了一种综合考虑服务节点和传输链路服务性能优化问题的接入控制方案。该方案利用SDN控制器对路由器的直接管控和对全网的感知能力,对应用层上服务节点服务性能和网络层上服务节点到用户之间的传输链路的服务性能进行联合优化,减少了链路拥塞对数据传输和用户服务质量的影响。首先,为SDN服务系统的接入控制过程建立部分可观Markov决策过程（POMDP）模型;然后,使用基于观测的随机策略作为系统的接入控制策略;最后,通过策略梯度算法对接入控制策略进行优化,求解出模型的最优策略。仿真结果表明,与尽力而为的服务策略相比,基于POMDP模型的最优接入控制策略使系统性能提高了10%,验证了所提方法的有效性。     

Optimization of the shape (and topology) of the initial conditions for diffusion parameter identification

The design of an experiment, e.g., the setting of initial conditions, strongly influences the accuracy of the whole process of determining model parameters from data. We impose a sensitivity-based approach for choosing optimal experimental design variables and study the optimization of the shape (and topology) of the initial conditions for an inverse problem of a diffusion parameter identification. Our approach, although case independent, is illustrated at the FRAP (Fluorescence Recovery After Photobleaching) experimental technique. The core idea resides in the maximization of a sensitivity measure, which depends on a specific experimental setting of initial conditions. By a numerical optimization, considering radially symmetric shapes only, we find an interesting pattern of increasingly complicated (with respect to connectivity) optimal initial shapes. The proposed modification of the FRAP experimental protocol is rather surprising but entirely realistic and the resulting enhancement of the parameter estimate accuracy is significant.     

Adaptive RTRL based neurocontroller for damping subsynchronous oscillations using TCSC

Modern interconnected electrical power systems are complex and require perfect planning, design and operation. Hence the recent trends towards restructuring and deregulation of electric power supply has put great emphasis on the system operation and control. Flexible AC transmission system (FACTS) devices such as thyristor controlled series capacitor (TCSC) are capable of controlling power flow, improving transient stability and mitigating subsynchronous resonance (SSR). In this paper an adaptive neurocontroller is designed for controlling the firing angle of TCSC to damp subsynchronous oscillations. This control scheme is suitable for non-linear system control, where the exact linearised mathematical model of the system is not required. The proposed controller design is based on real time recurrent learning (RTRL) algorithm in which the neural network (NN) is trained in real time. This control scheme requires two sets of neural networks. The first set is a recurrent neural network (RNN) which is a fully connected dynamic neural network with all the system outputs fed back to the input through a delay. This neural network acts as a neuroidentifier to provide a dynamic model of the system to evaluate and update the weights connected to the neurons. The second set of neural network is the neurocontroller which is used to generate the required control signals to the thyristors in TCSC. This is a single layer neural network. Performance of the system with proposed neurocontroller is compared with two linearised controllers, a conventional controller and with a discrete linear quadratic Gaussian (DLQG) compensator which is an optimal controller. The linear controllers are designed based on a linearised model of the IEEE first benchmark system for SSR studies in which a modular high bandwidth (six-samples per cycle) linear time-invariant discrete model of TCSC is interfaced with the rest of the system. In the proposed controller, since the response time is highly dependent on the number of states of the system, it is often desirable to approximate the system by its reduced model. By using standard Hankels norm approximation technique, the system order is reduced from 27 to 11th order by retaining the dominant dynamic characteristics of the system. To validate the proposed controller, computer simulation using MATLAB is performed and the simulation studies show that this controller can provide simultaneous damping of swing mode as well as torsional mode oscillations, which is difficult with a conventional controller. Moreover the fast response of the system can be used for real-time applications. The performance of the controller is tested for different operating conditions.