Machine-learning based decomposition and monitoring of behind-the-meter resources

With the rapid integration of behind-the-meter (BTM) resources, monitoring the working status of these resources has become a very important task, followed by distributed terminal load decomposition and monitoring can provide real-time power consumption information for the grid and users, which is an effective means to support energy efficiency management and load forecasting. The traditional monitoring method adopts an intrusive design and is difficult to promote and apply on a large scale, so it is of great significance to study proper methods for non-intrusive BTM resources monitoring (NBRM). Load identification is the key to NBRM, based on the characteristic analysis of typical residential loads. The current mainstream load decomposition methods are analyzed, the advantages and disadvantages of the current mainstream methods based on deep learning are sorted out and finally, the results of each algorithm are compared with common data sets.     

Clustering-based adaptive low-power subframe configuration with load-aware offsetting in dense heterogeneous networks

Heterogeneous Networks (HetNets) and cell densification represent promising solutions for the surging data traffic demand in wireless networks. In dense HetNets, user traffic is steered toward the Low-Power Node (LPN) when possible to enhance the user throughput and system capacity by increasing the area spectral efficiency. However, because of the transmit power differences in different tiers of HetNets and irregular service demand, a load imbalance typically exists among different serving nodes. To offload more traffic to LPNs and coordinate the Inter-Cell Interference (ICI), Third-Generation Partnership Project (3GPP) has facilitated the development of the Cell Range Expansion (CRE), enhanced Inter-Cell Interference Coordination (eICIC) and Further enhanced ICIC (FeICIC). In this paper, we develop a cell clustering-based load-aware offsetting and an adaptive Low-Power Subframe (LPS) approach. Our solution allows the separation of User Association (UA) functions at the User Equipment (UE) and network server such that users can make a simple cell-selection decision similar to that in the maximum Received Signal Strength (max-RSS) based UA scheme, where the network server computes the load-aware offsetting and required LPS periods based on the load conditions of the system. The proposed solution is evaluated using system-level simulations wherein the results correspond to performance changes in different service regions. Results show that our method effectively solves the offloading and interference coordination problems in dense HetNets.     

Web服务器集群的负载均衡中遗传算子的设计

应用遗传算法进行作业调度已被越来越多的学者关注。在Web服务器集群环境中,对于客户端的Web请求分配问题,采用常规的遗传算法进行负载均衡并不总是有效的,好的遗传算子对算法收敛性及收敛到最优解非常重要。基于集群环境中Web请求分配的特点,设计了有针对性的遗传算子,即改进的内外结合交叉算子和主动变异算子。模拟实验结果与分析表明这些算子对Web集群的请求分配是有效的。     

基于内容的网络集群负载平衡算法模型

在论述网络集群负载平衡算法的基础上,基于内容分类的方法,给出基于内容的网络集群负载平衡算法三元组模型。请求分类有利于提高缓存命中率,调度机制说明如何适当地转发请求,动态反馈避免将请求分配到重载的服务器,进而分析了调度机制的八种调度策略和六种基于内容的调度转发技术。该模型利用缓存内容来提高集群的吞吐量和响应时间,可部署多种服务类型。     

并行MLFMA分布层的构造及其负载均衡

研究了并行多层快速多极子算法（MLFMA）的分布层,提出了一种衡量其负载不均衡程度的方法,并在此基础上提出了分布树的一种新的构造算法。通过预先估算按分布层的不同层均分所得分布层的负载不均衡度,选取了一种负载不均衡度较小的方法来构造分布树。实验结果表明,该算法相比传统的按分布层的最细层均分所得分布树的算法,能更为有效地提高整个多层快速多极子算法的并行效率。     

Impact of a simple load balancing approach and an incentive-based scheme on MANET performance

Most reactive mobile ad hoc network (MANET) routing protocols such as AODV and DSR do not perform search for new routes until the network topology changes. But, low node mobility does not affect the MANET connectivity and the same routes may be used for a long time. This may cause concentration of traffic on few mobile stations (MSs), which results in congestion and hence longer end-to-end delay. In addition, continuous use of MSs may cause their battery power to get exhausted rapidly. Expiration of MS energy causes disruption of connections traversing through the MSs and could generate many simultaneous new routing requests. Therefore, we propose a load balancing approach called Simple Load Balancing Approach (SLBA), which can be transparently added to any current reactive routing protocol such as AODV and DSR. SLBA minimizes the traffic concentration by allowing each MS to drop RREQ or to give up packet forwarding depending on its own traffic load. Meanwhile, MSs may deliberately give up forwarding packets to save their own energy. For encouraging MSs to volunteer in forwarding packets, we introduce a reward scheme for packet forwarding, named Protocol-Independent Fairness Algorithm (PIFA). We compare the performance of AODV and DSR with and without SLBA and PIFA. Simulation results indicate that SLBA can distribute traffic very well and improve the MANET performance. PIFA is also observed to prevent MANET partitioning and any performance degradation due to selfish nodes.     

Multiresolution-based bilinear recurrent neural network

A multiresolution-based bilinear recurrent neural network (MBLRNN) is proposed in this paper. The proposed MBLRNN is based on the BLRNN that has robust abilities in modeling and predicting time series. The learning process is further improved by using a multiresolution-based learning algorithm for training the BLRNN so as to make it more robust for the prediction of time series data. The proposed MBLRNN is applied to the problems of network traffic prediction and electric load forecasting. Experiments and results on both practical problems show that the proposed MBLRNN outperforms both the traditional multilayer perceptron type neural network (MLPNN) and the BLRNN in the prediction accuracy.

美术馆计算机信息网络系统设计

利用当今主流的组网技术、网络安全技术、主机技术及存储备份技术,结合以应用为导向的设计思想,对某市美术馆计算机信息网络系统进行了方案设计。整个设计采用分功能区模块化的设计方法,具有贯穿整体的集成性、扩展性、兼容性。