充分利用社会资源进行专业教学——室内设计教学一得

室内设计是一门社会应用性课程,进行实题实做的实践教学是很有必要的,将教室融入到社会的大课堂中去,让学生参与室内装修工程的设计、施工和管理的整个过程来完成教学任务。全方位的锻炼学生的各种能力。实践教学的过程是一个面对设计、施工中碰到的具体问题的真实过程。认真扎实地走完整个室内设计实践教学过程,那么,教学任务完成了,教学目的达到了,实践教学过程就是教学所要的结果。     

基于元数据的目录服务体系研究与实现

随着网络技术和计算机技术的迅猛发展,世界各国对于电子政务建设日趋重视,电子政务在国外蓬勃发展,而我国的电子政务起步比较晚,经济发展呈现不平衡态势。针对政务信息化建设中出现的“信息孤岛”等问题,在现有的政务信息资源注册管理模型基础上,提出了基于元数据的目录服务,分析研究了政务信息资源目录服务体系的框架。并提出了基于元数据的政务信息资源的目录服务的实现方法,最终在上海市水务局实现试点应用。     

增加GSM系统网络容量的研究

本文针对日益激化的移动终端用户需求与受限频率资源的GSM系统网络容量间的矛盾,提出了GSM理论网络容量与载干比C／I间的计算方法,对GSM系统网络容量与移动通话质量间的关系进行了定量分析,同时提出了AMR-HR、COMMONBCCH等新技术,在尽可能维护无线通话质量的情况下提升GSM网络容量。     

基于资源的需求描述方法

针对需求分析中的系统需求描述问题,本文提出了一种以资源描述需求的方法,分析了这种方法与传统需求描述方法的异同;实现了对资源及需求的形式化描述,并详细讲述了获取需求的过程;最后给出了需求的存储形式和需求同资源的映射算法。     

Optimal and Effective Resource Management in Edge Computing

Edge computing is a cloud computing extension where physical computers are installed closer to the device to minimize latency. The task of edge data centers is to include a growing abundance of applications with a small capability in comparison to conventional data centers. Under this framework, Federated Learning was suggested to offer distributed data training strategies by the coordination of many mobile devices for the training of a popular Artificial Intelligence (AI) model without actually revealing the underlying data, which is significantly enhanced in terms of privacy. Federated learning (FL) is a recently developed decentralized profound learning methodology, where customers train their localized neural network models independently using private data, and then combine a global model on the core server together. The models on the edge server use very little time since the edge server is highly calculated. But the amount of time it takes to download data from smartphone users on the edge server has a significant impact on the time it takes to complete a single cycle of FL operations. A machine learning strategic planning system that uses FL in conjunction to minimise model training time and total time utilisation, while recognising mobile appliance energy restrictions, is the focus of this study. To further speed up integration and reduce the amount of data, it implements an optimization agent for the establishment of optimal aggregation policy and asylum architecture with several employees’ shared learners. The main solutions and lessons learnt along with the prospects are discussed. Experiments show that our method is superior in terms of the effective and elastic use of resources.     

Network resource management mechanisms in SDN enabled WSNs: A comprehensive review

Wireless technologies usually have very limited computing, memory, and battery power that require the optimal management of network resources to increase network performance. The optimization of these network resources provides an efficient network topology, traffic control, routing, and data aggregation. This study presents a qualitative and quantitative investigation to evaluate the efficient network resource management mechanisms for software defined wireless sensor networks (SDN-enabled WSNs) from the beginning of network design to reliable data delivery. In this paper, a taxonomy of network resource management research studies is proposed. A detailed analysis of SDN-enabled WSNs architecture, SDN controllers, topology discovery, routing approaches, flow rules installation, and data aggregation is also discussed. Furthermore, the comparative analysis of resource provisioning methods along with various simulation tools is presented. Moreover, this review outlines open research challenges and prospective future directions for network resource management in SDN-enabled WSNs.     

Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

In today’s world, Cloud Computing (CC) enables the users to access computing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located in remote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and task scheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud is employed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the current research work develops a Cost-Effective Optimal Task Scheduling Model (CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) model is used in the proposed work for hybrid clouds. Moreover, the algorithm works on the basis of multi-intentional task completion process with optimal resource allocation. The model was successfully simulated to validate its effectiveness based on factors such as processing time, make span and efficient utilization of virtual machines. The results infer that the proposed model outperformed the existing works and can be relied in future for real-time applications.     

基于HAGA的D2D-NOMA资源分配优化算法

针对无线系统带宽资源有限、基站负载压力大、传输时延长等问题,提出一种基于非正交多址接入技术的D2D系统吞吐量最大化资源分配算法。在不同用户的服务质量约束条件下,建立D2D系统吞吐量最大化资源分配模型。该模型的优化目标是一个混合整数非线性规划问题,将其解耦为信道匹配与功率分配2个子问题并分别进行处理,利用自适应惩罚函数法处理约束条件并提出一种基于爬山策略的自适应遗传算法以对问题进行求解。仿真结果表明,与GA、AGA算法相比,该算法能够有效提高D2D系统的吞吐量,且收敛性能更好。     

AutoProc: An automatic proactive replication scheme for P2P storage

Due to the prevalence of peer departures, P2P storage systems need to constantly generate replicas for high durability. However, few existing systems are able to select the right replication rate that balances replication cost and durability, e.g., minimizing replication cost without threatening the durability target. In this paper, we propose a method called AutoProc to automate the replication process in P2P storage systems. AutoProc employs two key techniques to enable cost-effective replication: correct...     

流水线前端资源分配及其性能影响研究

动态超标量处理器的性能取决于流水线各段资源的配置情况,通常认为资源越多对性能提升越有利。对流水线前端资源的分配进行研究,发现各类资源的合理搭配更重要,资源配置过多或搭配不当可能产生负面影响。分析各类资源的潜在互相干扰、影响性能的现象、过程及深层次原因,并给出一些参考建议。