软件功能需求驱动的商业构件评估

提出了一种由软件功能需求驱动的评估和选择商业构件（COTS）的新方法。该方法首先基于FCD方法将系统的功能需求分解到各个功能模块。对每个功能模块,识别出一组候选构件,用户根据给定的模板评价候选构件对功能需求的满足度和构件功能的有用性。最优构件组合在给定成本约束下,具有最大的全局需求满足度。选择最优组合的过程是求解一系列优化问题的过程。最后用一个例子进一步说明和验证了该方法。     

Requirements-driven database systems benchmark method

Benchmarks are the vital tools in the performance measurement, evaluation, and comparison of relational database management systems (RDBMS). Standard benchmarks such as the TP1, TPC-A, TPC-B, TPC-C, TPC-D, TPC-H, TPC-R, TPC-W, Wisconsin, and AS³AP benchmarks have been used to assess the performance of relational database management systems. These benchmarks are synthetic and domain-specific. Test results from these benchmarks are estimates of possible system performance for certain pre-determined application types. Database system performance on actual database domain may vary significantly from those in the standard benchmarks. In this paper, we describe a new benchmark method that is computer-assisted and developed from the perspective of the user's requirements.     

基于岗位需求驱动的Java实训课程体系构建研究

针对目前高职院校实训课程体系技能培养不能很好满足企业相应岗位需求的问题,以自身企业行业实践和校企合作开发经验为基础,提出从岗位需求分析出发,涵盖设计指导原则、实训框架设计、阶段项目考核方式的实训课程体系构建模式,实践教学结果证明,该模式在学生技能培养提高和岗位适应能力方面都有非常明显的效果。     

Requirements-driven incremental adoption of variability management techniques and tools: an industrial experience report

In theory, software product line engineering has reached a mature state. In practice though, implementing a variability management approach remains a tough case-by-case challenge for any organization. To tame the complexity of this undertaking, it is inevitable to handle variability from multiple perspectives and to manage variability consistently across artifacts, tools, and workflows. Especially, a solid understanding and management of the requirements to be met by the products is an inevitable prerequisite. In this article, we share experiences from the ongoing incremental adoption of explicit variability management at TRW Automotive’s department for automotive slip control systems—located in Koblenz, Germany. On the technical side, the three key drivers of this adoption effort are (a) domain modeling and scoping, (b) handling of variability in requirements and (c) tighter integration of software engineering focus areas (e.g., domain modeling, requirements engineering, architectural modeling) to make use of variability-related data. In addition to implementation challenges with using and integrating concrete third-party tools, social and workflow-related issues are covered as well. The lessons learned are presented, discussed, and thoroughly compared with the state of the art in research.     

Heuristic algorithms for effective broker deployment

In the pervasive e-business applications covering large geographical areas and involving many RFID readers or sensors, broker deployment strategies have a direct effect on the deployment cost and collaboration efficiency. By analyzing the deployment cost and collaboration basis, this paper proposes a model for the broker deployment problem, and presents two heuristic algorithms for the multi-object optimization of broker deployment, where one is for the deployment area which contains the zones forbidden to place brokers in, and the other is for the deployment area without any forbidden zone. Experiments are conducted to demonstrate the effectiveness of the proposed algorithms. The experimental results also show the deployment algorithms have the advantage of being low cost of deployment. Moreover, the brokers deployed carry relatively balanced loads and messages are forwarded from an event source to a broker over a small number of hops.     

Neural networks in quality function deployment

Quality Function Deployment (QFD) is a method of product planning in the early phases of the development of new products (pre-CAD phase). A major drawback of its application is the need to input a large amount of data and the necessity to estimate values on a rather subjective basis in order to complete the House of Quality. This data is plentiful and often designers lack the knowledge with satisfying accuracy. This paper suggests a machine learning approach in which a neural network automatically determines the data by learning from examples. Unlike conventional neural networks which are treated as black boxes, the topology and the weight values are not random but represent real circumstances and can directly be interpreted in the terms of the application. A final section discusses problems arising from the small number of training sets which is usually available in the field of product design.     

国家航空电信网的建设研究

航空电信网是CNS/ATM系统的重要组成部分,是下一代航空通信系统的全面解决方案.建立一个全球化的、开放结构的互联网络,使空中交通管制系统、航空公司、航空行政管理部门的各种网络有机地连接成一个整体,达到系统互联、信息互通、资源共享的目标是目前研究的热点问题.通过对航空电信网的基本概念、网络组成、协议结构等特点的阐述,分析了当前航空电信网研究中存在的关键问题.结合现行空中管制体制建设的特点,对航空电信网建设提出建议.     

Reliability-driven deployment optimization for embedded systems

One of the crucial aspects that influence reliability of embedded systems is the deployment of software components to hardware nodes. If the hardware architecture is designed prior to the customized software architecture, which is often the case in product-line manufacturing (e.g. in the automotive domain), the system architect needs to resolve a nontrivial task of finding a (near-)optimal deployment balancing the reliabilities of individual services implemented on the software level.In this paper, we introduce an approach to automate this task. As distinct to related approaches, which typically stay with quantification of reliability for a specific deployment, we target multi-criteria optimization and provide the architect with near-optimal (non-dominated) deployment alternatives with respect to service reliabilities. Toward this goal, we annotate the software and hardware architecture with necessary reliability-relevant attributes, design a method to quantify the quality of individual deployment alternatives, and implement the approach employing an evolutionary algorithm.     

P3P deployment on websites

We studied the deployment of computer-readable privacy policies encoded using the standard W3C platform for privacy preferences (P3P) format to inform questions about P3P’s usefulness to end users and researchers. We found that P3P adoption is increasing overall and that P3P adoption rates greatly vary across industries. We found that P3P had been deployed on 10% of the sites returned in the top-20 results of typical searches, and on 21% of the sites returned in the top-20 results of e-commerce searches. We examined a set of over 5000 websites in both 2003 and 2006 and found that P3P deployment among these sites increased over that time period, although we observed decreases in some sectors. In the Fall of 2007 we observed 470 new P3P policies created over a 2-month period. We found high rates of syntax errors among P3P policies, but much lower rates of critical errors that prevent a P3P user agent from interpreting them. We also found that most P3P policies have discrepancies with their natural language counterparts. Some of these discrepancies can be attributed to ambiguities, while others cause the two policies to have completely different meanings. Finally, we show that the privacy policies of P3P-enabled popular websites are similar to the privacy policies of popular websites that do not use P3P.     

Hedonic coalition formation for optimal deployment

This paper presents a distributed algorithmic solution, termed Coalition formation and deployment algorithm , to achieve network configurations where agents cluster into coincident groups that are distributed optimally over the environment. The motivation for this problem comes from spatial estimation tasks executed with unreliable sensors. We propose a probabilistic strategy that combines a repeated game governing the formation of coalitions with a spatial motion component governing their location. For a class of probabilistic coalition switching laws, we establish the convergence of the agents to coincident groups of a desired size in finite time and the asymptotic convergence of the overall network to the optimal deployment, both with probability 1. We also investigate the algorithm’s time and communication complexity. Specifically, we upper bound the expected completion time of executions that use the proportional-to-number-of-unmatched-agents coalition switching law under arbitrary and complete communication topologies. We also upper bound the number of messages required per timestep to execute our strategy. The proposed algorithm is robust to agent addition and subtraction. From a coalitional game perspective, the algorithm is novel in that the players’ information is limited to the neighboring clusters. From a motion coordination perspective, the algorithm is novel because it brings together the basic tasks of rendezvous (individual agents into clusters) and deployment (clusters in the environment). Simulations illustrate the correctness, robustness, and complexity results.     

数字媒体发布系统轻量级部署

与传统媒体传播相比,以数字信息技术为基础,以互动传播为特点、具有创新形态的新型媒体型态得到了快速发展。新媒体的特征具有交互性与即时性,海量性与共享性,多媒体与超文本,个性化与社群化,随着科技的飞速发展,新媒体越来越受到人们的关注。本文以数字媒体发布系统为例,介绍了新媒体系统的分布式轻量级部署,期望在媒体行业的数字化平台建设方面提供一些参考。     

Toward architecture-based context-aware deployment and adaptation

Software systems are increasingly expected to dynamically self-adapt to the changing environments. One of the main adaptation mechanisms is dynamic recomposition of application components. This paper addresses the key issues that arise when context knowledge is used to steer the run-time (re)composition process so as to match the new environmental conditions. In order to integrate such knowledge into this process, A Continuous Context-Aware Deployment and Adaptation (ACCADA) framework is proposed. To support run-time component composition, the essential runtime abstractions of the underlying component model are studied. By using a layered modeling approach, our framework gradually incorporates design-time as well as run-time knowledge into the component composition process. Service orientation is employed to facilitate the changes of adaptation policy. Results show that our framework has significant advantages over traditional approaches in light of flexibility, resource usage and lines of code. Although our experience was based on the OSGi middleware, we believe our findings to be general to architecture-based management systems using reflective component models.     

Cost engineering with quality function deployment

Conventional Quality Function Deployment (QFD) is technically one-sided. Prioritization of technical attributes, if carried out at all, attempts to maximize customer satisfaction without considering the costs incurred. However, product design is usually a techno-economic process, hence there is always a tradeoff between quality goals and limited budgets. Based on a prioritization method suggested by Wasserman [1], this paper integrates design costs into the QFD framework. This proposed approach enables designers to optimize product development resources towards customer satisfaction and conduct analytical investigations to facilitate decision making in product design and development.     

On the modelling of CDNaaS deployment

With the increasing demand for over the top media content, understanding user perception and Quality of Experience (QoE) estimation have become a major business necessity for service providers. Online video broadcasting is a multifaceted procedure and calculation of performance for the components that build up a streaming platform requires an overall understanding of the Content Delivery Network as a service (CDNaaS) concept. Therefore, to evaluate delivery quality and predicting user perception while considering NFV (Network Function Virtualization) and limited cloud resources, a relationship between these concepts is required. In this paper, a generalized mathematical model to calculate the success rate of different tiers of online video delivery system is presented. Furthermore, an algorithm that indicates the correct moment to switch between CDNs is provided to improve throughput efficiency while maintaining QoE and keeping the cloud hosting costs as lowest possible.

     

Energy-efficient deployment of Intelligent Mobile sensor networks

Many visions of the future include people immersed in an environment surrounded by sensors and intelligent devices, which use smart infrastructures to improve the quality of life and safety in emergency situations. Ubiquitous communication enables these sensors or intelligent devices to communicate with each other and the user or a decision maker by means of ad hoc wireless networking. Organization and optimization of network resources are essential to provide ubiquitous communication for a longer duration in large-scale networks and are helpful to migrate intelligence from higher and remote levels to lower and local levels. In this paper, distributed energy-efficient deployment algorithms for mobile sensors and intelligent devices that form an Ambient Intelligent network are proposed. These algorithms employ a synergistic combination of cluster structuring and a peer-to-peer deployment scheme. An energy-efficient deployment algorithm based on Voronoi diagrams is also proposed here. Performance of our algorithms is evaluated in terms of coverage, uniformity, and time and distance traveled until the algorithm converges. Our algorithms are shown to exhibit excellent performance.