面向普适计算的多Agent协同设计系统模型

根据普适计算的特点,提出了一种多agent协同设计系统框架模型.考虑到普适计算环境下移动设备的交互和多通道的信息集成,该模型在已有多agent协同设计系统的框架模型上增加了相应的agent,并且在设计agent的结构中包含了通道识别、上下文生成及多通道信息整合模块,以处理多通道的信息.详细介绍了在该agent模型中基于设计上下文的知识表示、搜索和更新式学习算法,以及协同设计系统中多agent知识共享的方法.该研究对于普适计算在设计领域中的应用有重要的意义.     

Context modelling and a context-aware framework for pervasive service creation: A model-driven approach

Pervasive service creation entails a complex process that involves a diversity of development aspects. Context-awareness is an important facet of pervasive service creation, which deals with the acquisition, rendering, representation and utilisation of context information. In this paper we tackle context-awareness at the application level dealing with the representation and utilisation of context by services. We propose a model-driven approach that facilitates the creation of a context modelling framework and simplifies the design and implementation of pervasive services. To conclude, we demonstrate the benefits of our model-driven approach via the creation of a pervasive museum service and its evaluation using selected software metrics.     

A comprehensive approach to model and use context for adapting applications in pervasive environments

With an increasing diversity of pervasive computing devices integrated in our surroundings and an increasing mobility of users, it will be important for computer systems and applications to be context-aware. Lots of works have already been done in this direction on how to capture context data and how to carry it to the application. Among the remaining challenges are to create the intelligence to analyze the context information and deduce the meaning out of it, and to integrate it into adaptable applications. Our work focuses on these challenges by defining generic context storage and processing model and by studying its impact on the application core. We propose a reusable context ontology model that is based on two levels: a generic level and a domain specific level. We propose a generic adaptation framework to guarantee adaptation of applications to the context in a pervasive computing environment. We also introduce a comprehensive adaptation approach that involves content adaptation and presentation adaptation inline with the adaptation of the core services of applications. Our case study shows that the context model and the application adaptation strategies provide promising service architecture.     

Adaptable,model-driven security engineering for SaaS cloud-based applications

Software-as-a-service (SaaS) multi-tenancy in cloud-based applications helps service providers to save cost, improve resource utilization, and reduce service customization and maintenance time. This is achieved by sharing of resources and service instances among multiple “tenants” of the cloud-hosted application. However, supporting multi-tenancy adds more complexity to SaaS applications required capabilities. Security is one of these key requirements that must be addressed when engineering multi-tenant SaaS applications. The sharing of resources among tenants—i.e. multi-tenancy—increases tenants’ concerns about the security of their cloud-hosted assets. Compounding this, existing traditional security engineering approaches do not fit well with the multi-tenancy application model where tenants and their security requirements often emerge after the applications and services were first developed. The resultant applications do not usually support diverse security capabilities based on different tenants’ needs, some of which may change at run-time i.e. after cloud application deployment. We introduce a novel model-driven security engineering approach for multi-tenant, cloud-hosted SaaS applications. Our approach is based on externalizing security from the underlying SaaS application, allowing both application/service and security to evolve at runtime. Multiple security sets can be enforced on the same application instance based on different tenants’ security requirements. We use abstract models to capture service provider and multiple tenants’ security requirements and then generate security integration and configurations at runtime. We use dependency injection and dynamic weaving via Aspect-Oriented Programming (AOP) to integrate security within critical application/service entities at runtime. We explain our approach, architecture and implementation details, discuss a usage example, and present an evaluation of our approach on a set of open source web applications.     

Model-driven auto-scaling of green cloud computing infrastructure

Cloud computing can reduce power consumption by using virtualized computational resources to provision an application’s computational resources on demand. Auto-scaling is an important cloud computing technique that dynamically allocates computational resources to applications to match their current loads precisely, thereby removing resources that would otherwise remain idle and waste power. This paper presents a model-driven engineering approach to optimizing the configuration, energy consumption, and operating cost of cloud auto-scaling infrastructure to create greener computing environments that reduce emissions resulting from superfluous idle resources. The paper provides four contributions to the study of model-driven configuration of cloud auto-scaling infrastructure by (1) explaining how virtual machine configurations can be captured in feature models, (2) describing how these models can be transformed into constraint satisfaction problems (CSPs) for configuration and energy consumption optimization, (3) showing how optimal auto-scaling configurations can be derived from these CSPs with a constraint solver, and (4) presenting a case study showing the energy consumption/cost reduction produced by this model-driven approach.     

A model-driven approach to develop high performance web applications

The evolution of web technologies in the last few years has contributed to the improvement of web applications, and with the appearance of AJAX and Web 2.0 technology, a new breed of applications for the Internet has emerged: widgets, gadgets and mashups are some examples of this trend. However, as web applications become more and more complex, development costs are increasing in an exponential rate, and we believe that considering a software engineering methodology in the development process of this type of applications, contributes to the solution of this problem. In order to solve this question, this paper proposes a model-driven architecture to support web application development from the design to the implementation model. With this aim, the following tasks have been performed: first a new profile extends UML with new concepts extracted from the web domain, then a new framework supports web application development by composing heterogeneous web elements, and finally, a transformation model generates web applications from the UML extension proposed. The main contribution of this work is a cost and complexity reduction due to the incorporation of a model-driven architecture into the web application development process, but other advantages that can be mentioned are a high performance degree achieved by a prefetching cache mechanism, and a high reusability, since web elements can be reused in different web applications.     

基于模型的嵌入式系统软件工程语言AADL与UML的比较

随着嵌入式系统规模、复杂度和性能需求的提升,嵌入式系统开发的重点从代码级提前到模型级,模型驱动体系结构成为嵌入式系统开发的主流。统一建模语言(UML)和结构分析和设计语言(AADL)是模型驱动系统工程的标准,从方法、扩展机制和应用领域三个方面对两者进行研究和比较,讨论模型分析、模型转换等相关技术。最后,探讨了UML和AADL的发展与研究方向     

Requirements engineering in health care: the example of chemotherapy planning in paediatric oncology

Health care is characterized by highly complex processes of patient care that require unusual amount of communication between different health care professionals of different institutions. Sub-optimal processes can significantly impact on the patient’s health, increase the consumption of services and resources and in severe cases can lead to the patient death. For these reasons, requirements engineering for the development of information technology in health care is a complex process as well: without constant and rigorous evaluation, the impact of new systems on the quality of care is unknown and it is possible that badly designed systems significantly harm patients. To overcome these limitations, we present and discuss an approach to requirements engineering that we applied for the development of applications for chemotherapy planning in paediatric oncology. Chemotherapy planning in paediatric oncology is complex and time-consuming and errors must be avoided by all means. In the multi-hospital/multi-trial-centre environment of paediatric oncology, it is especially difficult and time-consuming to analyse requirements. Our approach combines a grounded theory approach with evolutionary prototyping based on the constant development and refinement of a generic domain model, in this case a domain model for chemotherapy planning in paediatric oncology. The prototypes were introduced in medical centres and final results show that the developed generic domain model is adequate.     

一个普适计算环境中的定位隐私保护模型*

随着普适计算技术的发展,在普适环境中的应用服务也日益增多。这些服务通常都是基于用户位置的服务,然而,用户的定位信息中往往直接或间接地包含着用户的隐私内容。针对如何在获取个性化定制服务的同时保护用户的定位隐私这一问题展开讨论,提出了一种定位信息采集和假名使用的策略,并基于该策略建立了一个普适计算环境中的定位隐私保护模型。     

On the use of models for high-performance scientific computing applications: an experience report

This paper reports on a four-year project that aims to raise the abstraction level through the use of model-driven engineering (MDE) techniques in the development of scientific applications relying on high-performance computing. The development and maintenance of high-performance scientific computing software is reputedly a complex task. This complexity results from the frequent evolutions of supercomputers and the tight coupling between software and hardware aspects. Moreover, current parallel programming approaches result in a mixing of concerns within the source code. Our approach relies on the use of MDE and consists in defining domain-specific modeling languages targeting various domain experts involved in the development of HPC applications, allowing each of them to handle their dedicated model in a both user-friendly and hardware-independent way. The different concerns are separated thanks to the use of several models as well as several modeling viewpoints on these models. Depending on the targeted execution platforms, these abstract models are translated into executable implementations by means of model transformations. To make all of these effective, we have developed a tool chain that is also presented in this paper. The approach is assessed through a multi-dimensional validation that focuses on its applicability, its expressiveness and its efficiency. To capitalize on the gained experience, we analyze some lessons learned during this project.     

Path planning in construction sites: performance evaluation of the Dijkstra,A1, and GA search algorithms

This paper presents the application of path planning in construction sites according to multiple objectives. It quantitatively evaluates the performance of three optimisation algorithms namely: Dijkstra, A¹, and Genetic algorithms that are used to find multi-criteria paths in construction sites based on transportation and safety-related cost. During a construction project, site planners need to select paths for site operatives and vehicles, which are characterised by short distance, low risks and high visibility. These path evaluation criteria are combined using a multi-objective approach. The criteria can be optimised to present site planners with the shortest path, the safest path, the most visible path or a path that reflects a combination of short distance, low risk and high visibility. The accuracy of the path solutions and the time complexities of the optimisation algorithms are compared and critically analysed.     

Path planning in construction sites: performance evaluation of the Dijkstra, A, and GA search algorithms

This paper presents the application of path planning in construction sites according to multiple objectives. It quantitatively evaluates the performance of three optimisation algorithms namely: Dijkstra, A^*, and Genetic algorithms that are used to find multi-criteria paths in construction sites based on transportation and safety-related cost. During a construction project, site planners need to select paths for site operatives and vehicles, which are characterised by short distance, low risks and high visibility. These path evaluation criteria are combined using a multi-objective approach. The criteria can be optimised to present site planners with the shortest path, the safest path, the most visible path or a path that reflects a combination of short distance, low risk and high visibility. The accuracy of the path solutions and the time complexities of the optimisation algorithms are compared and critically analysed.     

A centre–free approach for resource allocation with lower bounds

Since complexity and scale of systems are continuously increasing, there is a growing interest in developing distributed algorithms that are capable to address information constraints, specially for solving optimisation and decision-making problems. In this paper, we propose a novel method to solve distributed resource allocation problems that include lower bound constraints. The optimisation process is carried out by a set of agents that use a communication network to coordinate their decisions. Convergence and optimality of the method are guaranteed under some mild assumptions related to the convexity of the problem and the connectivity of the underlying graph. Finally, we compare our approach with other techniques reported in the literature, and we present some engineering applications.     

MDA环境下可变性元建模方法研究

为模型提供可变性支持对于模型驱动软件开发的实践具有重要意义。可变性建模有利于捕捉领域概念及其特征的共性和差异性,为构造应用模型簇提供了一种模型复用的途径。在研究特征建模的可变性机制和MDA标准建模语言对可变性表示不足的基础上,提出了一种可变性元建模方法,构造可变性概要以支持原子、成组等可变性描述,开发了可变性验证和模型转换算法。通过实际项目的验证,该方法为基于可变性建模的模型内容复用提供了有效支持。     

Supporting pervasive computing applications with active context fusion and semantic context delivery

Future pervasive computing applications are envisioned to adapt the applications’ behaviors by utilizing various contexts of an environment and its users. Such context information may often be ambiguous and also heterogeneous, which make the delivery of unambiguous context information to real applications extremely challenging. Thus, a significant challenge facing the development of realistic and deployable context-aware services for pervasive computing applications is the ability to deal with these ambiguous contexts. In this paper, we propose a resource optimized quality assured context mediation framework based on efficient context-aware data fusion and semantic-based context delivery. In this framework, contexts are first fused by an active fusion technique based on Dynamic Bayesian Networks and ontology, and further mediated using a composable ontological rule-based model with the involvement of users or application developers. The fused context data are then organized into an ontology-based semantic network together with the associated ontologies in order to facilitate efficient context delivery. Experimental results using SunSPOT and other sensors demonstrate the promise of this approach.     

Transforming an enterprise model into a use case model in business process systems

One of the responsibilities of requirements engineering is to transform stakeholder requirements into system and software requirements. For enterprise systems, this transformation must consider the enterprise context where the system will be deployed. Although there are some approaches for detailing stakeholder requirements, some of them even considering the enterprise context, this task is executed manually. Based on model-driven engineering concepts, this study proposes a semi-automatic transformation from an enterprise model to a use case model. The enterprise model is used as a source of information about the stakeholder requirements and domain knowledge, while the use case model is used as software requirements model. This study presents the source and target metamodels, a set of transformation rules, and a tool to support the transformation. An experiment analyzes the use of the proposed transformation to investigate its benefits and if it can be used in practice, from the point of view of students in the context of a requirements refinement. The results indicate that the approach can be used in practice, as it did not influence the quality of the generated use cases. However, the empirical analysis does not indicate benefits of using the transformation, even if the qualitative results were positive.     

普适环境下基于域模型的跨域资源控制机制

重点研究普适计算环境下资源管理中的跨域问题,聚焦在跨域资源访问,跨域用户漫游和跨域事务转移的机制方面,提出了评估方法并对提出的机制进行了实验分析,验证了在大规模无缝的普适计算环境下,所基于的域管理模型和研究的跨域机制能获得更高的运行效率。     

MUPPIT: a method for using proper patterns in model transformations

Software and Systems Modeling - Model transformation plays an important role in developing software systems using the model-driven engineering paradigm. Examples of applications of model...     

普适计算环境下的虚拟机技术研究

普适计算环境中资源和服务利用率较低,传统应用程序在该环境下无法兼容。为此,提出一种适用于普适计算环境的虚拟机技术。使用设备请求代理屏蔽复杂的普适计算网络环境,对上层应用提供统一硬件平台,支持多个普适计算应用同时运行。实验结果表明,该虚拟化技术能兼容传统应用,提高普适计算资源和服务的利用率。     

Cauchy Grey Wolf Optimiser for continuous optimisation problems

ABSTRACT

Grey Wolf Optimiser (GWO) is a recently developed optimisation approach to solve complex non-linear optimisation problems. It is relatively simple and leadership-hierarchy based approach in the class of Swarm Intelligence based algorithms. For solving complex real-world non-linear optimisation problems, the search equation provided in GWO is not of sufficient explorative behaviour. Therefore, in the present paper, an attempt has been made to increase the exploration capability along with the exploitation of a search space by proposing an improved version of classical GWO. The proposed algorithm is named as Cauchy-GWO. In Cauchy-GWO Cauchy operator has been integrated in which first two new wolves are generated with the help of Cauchy distributed random numbers and then another new wolf is generated by taking the convex combination of these new wolves. The performance of Cauchy-GWO is exhibited on standard IEEE CEC 2014 benchmark problem set. Statistical analysis of the results on CEC 2014 benchmark set and popular evaluation criteria, Performance Index (PI) proves that Cauchy-GWO outperforms GWO in terms of error values defined in IEEE CEC 2014 benchmarks collection. Later on in the paper, GWO and Cauchy-GWO algorithms have been used to solve three well-known engineering application problems and two problems of reliability. From the analysis conducted in the present paper, it can be concluded that the proposed algorithm, Cauchy-GWO is reliable and efficient algorithm to solve continuous benchmark test problems, as well as real-life applications problems.