Comparison of component frameworks for real-time embedded systems

The use of components significantly helps in development of real-time embedded systems. There have been a number of component frameworks developed for this purpose, and some of them have already became well established in this area. Even though these frameworks share the general idea of component-based development, they significantly differ in the range of supported features and maturity. This makes it relatively difficult to select the right component framework and thus poses a significant obstacle in adoption of the component-based development approach for developing real-time embedded systems. To provide guidance in choosing a component framework, or at least relevant concepts when building a custom framework, we present a survey, which illustrates distinguishing features and provides comparison of selected modern component-based frameworks for real-time embedded systems. Compared to other existing surveys, this survey focuses specifically on criteria connected with real-time and embedded systems. Further, to be practically relevant, we restrict the survey only to the frameworks that support the full development life cycle (i.e. from design till execution support). In this context, the survey illustrates the complexity of development in each framework by giving specification and code samples.     

Leveraging global resources: a process maturity framework for managing distributed development

The popular and well-tested software process frameworks such as the Software Engineering Institute's Capability Maturity Model and ISO 9001, details key process areas for software development. Focusing more on continuous improvement in code development processes, generic-process frameworks such as the CMM lack key process areas (KPA) that address capabilities for managing distributed software projects, such as establishing mutual knowledge and managing geographically dispersed social networks. We identify 24 new KPAs that address the wide-ranging capabilities needed for managing distributed software development and arrange them in an evolutionary order similar to the CMM framework. The evolutionary or phased approach in improving software management capabilities helps firms systematically assess their situations and plan for improvements. We also report the results of a statistically tested maturity assessment survey and test the overall rigor of our model against industry expert opinion and objective data collected from real-world projects implemented at SAP AG, a leading global-enterprise software firm.     

Hybreed: A software framework for developing context-aware hybrid recommender systems

This article introduces Hybreed, a software framework for building complex context-aware applications, together with a set of components that are specifically targeted at developing hybrid, context-aware recommender systems. Hybreed is based on a concept for processing context that we call dynamic contextualization. The underlying notion of context is very generic, enabling application developers to exploit sensor-based physical factors as well as factors derived from user models or user interaction. This approach is well aligned with context definitions that emphasize the dynamic and activity-oriented nature of context. As an extension of the generic framework, we describe Hybreed RecViews, a set of components facilitating the development of context-aware and hybrid recommender systems. With Hybreed and RecViews, developers can rapidly develop context-aware applications that generate recommendations for both individual users and groups. The framework provides a range of recommendation algorithms and strategies for producing group recommendations as well as templates for combining different methods into hybrid recommenders. Hybreed also provides means for integrating existing user or product data from external sources such as social networks. It combines aspects known from context processing frameworks with features of state-of-the-art recommender system frameworks, aspects that have been addressed only separately in previous research. To our knowledge, Hybreed is the first framework to cover all these aspects in an integrated manner. To evaluate the framework and its conceptual foundation, we verified its capabilities in three different use cases. The evaluation also comprises a comparative assessment of Hybreed’s functional features, a comparison to existing frameworks, and a user study assessing its usability for developers. The results of this study indicate that Hybreed is intuitive to use and extend by developers.     

High-performance Earth system modeling with NASA/GSFC’s Land Information System

The Land Information System software (LIS; http://lis.gsfc.nasa.gov/, 2006) has been developed to support high-performance land surface modeling and data assimilation. LIS integrates parallel and distributed computing technologies with modern land surface modeling capabilities, and establishes a framework for easy interchange of subcomponents, such as land surface physics, input/output conventions, and data assimilation routines. The software includes multiple land surface models that can be run as a multi-model ensemble on global or regional domains with horizontal resolutions ranging from 2.5° to 1 km. The software may execute serially or in parallel on various high-performance computing platforms. In addition, the software has well-defined, standard-conforming interfaces and data structures to interface and interoperate with other Earth system models. Developed with the support of an Earth science technology office (ESTO) computational technologies project round~3 cooperative agreement, LIS has helped advance NASA’s Earth–Sun division’s software engineering principles and practices, while promoting portability, interoperability, and scalability for Earth system modeling. LIS was selected as a co-winner of NASA’s 2005 software of the year award.     

Frameworks for designing and implementing dependable systems using Coordinated Atomic Actions: A comparative study

This paper¹ presents ways of implementing dependable distributed applications designed using the Coordinated Atomic Action (CAA) paradigm. CAAs provide a coherent set of concepts adapted to fault tolerant distributed system design that includes structured transactions, distribution, cooperation, competition, and forward and backward error recovery mechanisms triggered by exceptions. DRIP (Dependable Remote Interacting Processes) is an efficient Java implementation framework which provides support for implementing Dependable Multiparty Interactions (DMI). As DMIs have a softer exception handling semantics compared with the CAA semantics, a CAA design can be implemented using the DRIP framework. A new framework called CAA-DRIP allows programmers to exclusively implement the semantics of CAAs using the same terminology and concepts at the design and implementation levels. The new framework not only simplifies the implementation phase, but also reduces the final system size as it requires less number of instances for creating a CAA at runtime. The paper analyses both implementation frameworks in great detail, drawing a systematic comparison of the two. The CAAs behaviour is described in terms of Statecharts to better understand the differences between the two frameworks. Based on the results of the comparison, we use one of the frameworks to implement a case study belonging to the e-health domain.     

图形分布处理支撑环境DGPSE

本文详细介绍图形分布处理支撑环境ＤＧＰＳＥ的设计和实现技术．ＤＧＰＳＥ有效支持高性能分布式图形应用系统的开发和分布并行图形处理算法的研究．它具有以下特点：支持多种分布处理模型，通讯方式灵活，图形支撑功能强．使用ＤＧＰＳＥ已实现了一分布式图形应用系统和一组分布式图形算法．的设计和实现．南京大学计算机系硕士论文，１９９１．１３潘志庆，石教英．Ｆｒａｃｔａｌ生成的并行算法研究．全国第７届ＣＡＤ和图形学会议论文集，无锡，１９９２．１４石教英，劳志强，潘志庚．多面体扫描转换的分布并行算法．全国第７届ＣＡＤ和图形学会议论文集，无锡，１９９２．     

A Comparative Study and Evaluation of Parallel Programming Models for Shared-Memory Parallel Architectures

Nowadays, shared-memory parallel architectures have evolved and new programming frameworks have appeared that exploit these architectures: OpenMP, TBB, Cilk Plus, ArBB and OpenCL. This article focuses on the most extended of these frameworks in commercial and scientific areas. This paper shows a comparative study of these frameworks and an evaluation. The study covers several capacities, such as task deployment, scheduling techniques, or programming language abstractions. The evaluation measures three dimensions: code development complexity, performance and efficiency, measure as speedup per watt. For this evaluation, several parallel benchmarks have been implemented with each framework. These benchmarks are created to cover certain scenarios, like regular memory access or irregular computation. The conclusions show some highlights, like the fact that some frameworks (OpenMP, Cilk Plus) are better for transforming quickly a sequential code, others (TBB) have a small footprint which is ideal for small problems, and others (OpenCL) are suited for heterogeneous architectures but they require a very complex development process. The conclusions also show that the vectorization support is more critical than multitasking to achieve efficiency for those problems where this approach fits.     

A communication-reduced and computation-balanced framework for fast graph computation

The bulk synchronous parallel (BSP) model is very user friendly for coding and debugging parallel graph algorithms. However, existing BSP-based distributed graph-processing frameworks, such as Pregel, GPS and Giraph, routinely suffer from high communication costs. These high communication costs mainly stem from the fine-grained message-passing communication model. In order to address this problem, we propose a new computation model with low communication costs, called LCC-BSP. We use this model to design and implement a high-performance distributed graph-processing framework called LCC-Graph. This framework eliminates high communication costs in existing distributed graph-processing frameworks. Moreover, LCC-Graph also balances the computation workloads among all compute nodes by optimizing graph partitioning, significantly reducing the computation time for each superstep. Evaluation of LCC-Graph on a 32-node cluster, driven by real-world graph datasets, shows that it significantly outperforms existing distributed graph-processing frameworks in terms of runtime, particularly when the system is supported by a high-bandwidth network. For example, LCC-Graph achieves an order of magnitude performance improvement over GPS and GraphLab.     

Building with ParadisEO reusable parallel and distributed evolutionary algorithms

Numerous parallel and distributed evolutionary algorithms (PDEAs) and their implementations have been proposed and are available on the Web. A robust approach to make easier their code and design reuse is the framework approach. In this paper, we present some existing frameworks for PDEAs and their development requirements, and propose a new C++ open source framework, named Parallel and distributed Evolving Objects (ParadisEO). ParadisEO is basically devoted to the reusable and flexible design of parallel and distributed metaheuristics, but we focus here only on PDEAs. Compared to other related frameworks, ParadisEO allows more reuse flexibility, and provides more implemented parallel and distributed models. Furthermore, these models can be exploited by the user in a transparent way, and deployed as well on shared memory multi-processors as on distributed memory machines. The architecture has been experimented on two real-world applications: the radio network design and the spectroscopic data mining. The experimental results demonstrate the efficiency and robustness of the different models.     

Combined use of GIS and mixed-integer linear programming for identifying optimal agricultural areas for sewage sludge amendment: A case study of Catalonia

This work proposes a systematic decision-making tool for identifying the best geographical areas for sewage sludge (SS) amendment in terms of economic and environmental criteria. Our approach integrates GIS and multi-objective mixed-integer linear programming (MILP) within a unified framework that allows exploring in a rigorous and systematic manner a large number of alternatives for sewage sludge amendment from which the best ones (according to the decision-makers' preferences) are finally identified. The capabilities of our methodology are illustrated through its application to a case study based on Catalonia (NE of Spain). The tool presented provides as output a set of optimal alternatives for sewage sludge distribution, each one achieving a unique combination of economic and environmental performance. Our ultimate goal is to guide decision-makers toward the adoption of more sustainable patterns for sewage sludge amendment.     

A service-oriented framework for agent-based simulations of collaborative supply chains

Current collaborative practices of supply chain management are limited to some known configurations where a dominant member sets the pace for the collaboration extent. Extending collaborative models to supply chains without a dominant member requires defining a fair assessment of costs and benefits and how they are distributed among members. To understand collaborative models and their mechanisms, simulation-based approaches are recommended as they can afford the complexity of real scenarios. However, building ad-hoc simulation models for studying complex supply chain interactions can be prohibitive in terms of both cost and time. Therefore, the availability of simulation frameworks, to be used easily by business managers and facilitating the development of those models, has a strong incentive in the quest of current business efforts to increase their supply chain performance. The objective of this work is to present a systematic and reusable serviceoriented framework for agent based simulation to support the analysis of collaborative interactions in supply chains. Results of a requirement analysis performed to this aim are described, and the fulfillment of identified requirements by the proposed framework, and capabilities thereof, are discussed.     

基于分布式对象的分布式仿真

该文首先比较了进行分布式并行仿真的几种典型手段,在此基础上,指出了分布式对象的应用价值,并利用该技术,提出了一个可重用的分布式并行仿真框架。     

Guiding the selection of service-oriented software engineering methodologies

Service-oriented computing is a paradigm for effectively delivering software services in a dynamic environment. Accordingly, many service-oriented software engineering (SOSE) methodologies have been proposed and practiced in both academia and industry. Some of these methodologies share common features (e.g. cover similar life-cycle phases) but are presented for different purposes, ranging from project management to system modernization, and from business analysis to technical solutions development. Given this diversity in the methodologies available in the literature, it is very hard for a company to decide which methodology would fit best for its specific needs. With this aim, we took a feature analysis approach and devised a framework for comparing the existing SOA methodologies. Different from existing comparison frameworks, ours specifically highlights aspects that are specific to SOA and aims to differentiate the methodologies that are truly service-oriented from those that deal little with service aspects. As such, the criteria defined in the framework can be used as a checklist for selecting a SOSE methodology.     

Trends in cooperative distributed problem solving

The authors present an overview of cooperative distributed problem solving (CDPS), an emerging research area that combines aspects of AI (artificial intelligence) and distributed processing. CDPS can be used to study how a loosely coupled network of sophisticated problem-solving nodes can solve a complex problem which consists of a set of interdependent subproblems. Subproblems arise because of spatial, temporal, and functional distribution of data, knowledge, and processing capabilities. Application areas include distributed interpretation, distributed planning and control, cooperating expert systems, and computer-supported human cooperation. The authors survey the important approaches and empirical investigations that have been developed. The approaches covered include negotiation, functionally accurate cooperation, organizational structuring, multiagent planning, sophisticated local control, and theoretical frameworks     

Managing complexity in massively parallel, adaptive, multiphysics applications

A new generation of scientific and engineering applications are being developed to support multiple coupled physics, adaptive meshes, and scaling in massively parallel environments. The capabilities required to support multiphysics, adaptivity, and massively parallel execution are individually complex and are especially challenging to integrate within a single application. Sandia National Laboratories has managed this challenge by consolidating these complex physics-independent capabilities into the Sierra Framework which is shared among a diverse set of application codes. The success of the Sierra Framework has been predicated on managing the integration of complex capabilities through a conceptual model based upon formal mathematical abstractions. Set theory is used to express and analyze the data structures, operations, and interactions of these complex capabilities. This mathematically based, conceptual modeling approach to managing complexity is not specific to the Sierra Framework—it is generally applicable to any scientific and engineering application framework.     

A joint feature selection framework for multivariate resource usage prediction in cloud servers using stability and prediction performance

Resource provisioning in cloud servers depends on future resource utilization of different jobs. As resource utilization trends vary dynamically, effective resource provisioning requires prediction of future resource utilization. The problem becomes more complicated as performance metrics related to one resource may depend on utilization of other resources also. In this paper, different multivariate frameworks are proposed for improving the future resource metric prediction in cloud. Different techniques for identifying the set of resource metrics relevant for the prediction of desired resource metric are analyzed. The proposed multivariate feature selection and prediction frameworks are validated for CPU utilization prediction in Google cluster trace. Joint analysis based on the prediction performance of the multivariate framework as well as its stability is used for selecting the most suitable feature selection framework. The results of the joint analysis indicate that features selected using the Granger causality technique perform best for multivariate resource usage prediction.     

JiniSolve:一种基于Jini的元计算架构的基本体系结构及其初步应用

随着不同种类体系结构高性能计算机系统的不断出现和网络技术的迅猛发展,人们越来越希望能够打破平台和地域的限制,共享分布在广域网上的各种高性能计算机的计算能力。但广域网络分布式计算平台的异构性和资源组织协调的复杂性一直困扰着这方面工作的发展。作者从Java字节码的平台无关性和Jini架构的网络资源运行时动态自组织能力中得到启发,提出了JiniSolve网络异构平台分布式计算架构(一种元计算架构),以克服这两个障碍。JiniSolve架构能够支持所有三种网络分布式计算的模式———远程计算、MobileAgent计算和代码下载计算,其中对MobileAgent计算的支持是以往类似元计算架构所不能做到的。同时,JiniSolve架构又具有运行时动态异步自组织能力,从而使整个系统具有更高的可适应性,可用性,可靠性和易用性。文章将介绍JiniSolve架构的基本体系结构,分析其优点并给出一个实验性的应用举例。     

Perspective: A Standards-Based System for Manufacturing Information Integration

This paper presents an infrastructure and a prototype system for a manufacturing information system, which is distributed its nature and is able to store, index, manage, retrieve and present business data, inventory data, and manufacturing processes data. The system works with all kinds of information, such as continuous (i.e., stream oriented) data, production (e.g., decision support) data, legacy data, and multimedia data (say, drawings, pictures, audio signals, voice annotations, and video streams). A key criterion is support for content-based information retrieval across all application areas. The main objective is to provide support for automated information transactions. The prototype of our architecture uses JAVA, STEP (ISO 10303) standard, the Internet, and CORBA. A fully functional system, called Perspective, for retrieval of part and manufacturing process has been designed and implemented in a distributed environment. Among other capabilities, the system can retrieve a set of parts or manufacturing processes based on similarity to some desired criteria.     

A Load-Fairness Prioritization-Based Matching Technique for Cloud Task Scheduling and Resource Allocation

In a cloud environment, consumers search for the best service provider that accomplishes the required tasks based on a set of criteria such as completion time and cost. On the other hand, Cloud Service Providers (CSPs) seek to maximize their profits by attracting and serving more consumers based on their resource capabilities. The literature has discussed the problem by considering either consumers’ needs or CSPs’ capabilities. A problem resides in the lack of explicit models that combine preferences of consumers with the capabilities of CSPs to provide a unified process for resource allocation and task scheduling in a more efficient way. The paper proposes a model that adopts a Multi-Criteria Decision Making (MCDM) method, called Analytic Hierarchy Process (AHP), to acquire the information of consumers’ preferences and service providers’ capabilities to prioritize both tasks and resources. The model also provides a matching technique to assign each task to the best resource of a CSP while preserves the fairness of scheduling more tasks for resources with higher capabilities. Our experimental results prove the feasibility of the proposed model for prioritizing hundreds of tasks/services and CSPs based on a defined set of criteria, and matching each set of tasks/services to the best CSPS.     

Comparison of four freely available frameworks for image processing and visualization that use ITK

Most image processing and visualization applications allow users to configure computation parameters and manipulate the resulting visualizations. SCIRun, VoIView, MeVisLab, and the Medical Interaction Toolkit (MITK) are four image processing and visualization frameworks that were built for these purposes. All frameworks are freely available and all allow the use of the ITK C++ library. In this paper, the benefits and limitations of each visualization framework are presented to aid both application developers and users in the decision of which framework may be best to use for their application. The analysis is based on more than 50 evaluation criteria, functionalities, and example applications. We report implementation times for various steps in the creation of a reference application in each of the compared frameworks. The data-flow programming frameworks, SCIRun and MeVisLab, were determined to be best for developing application prototypes, while VoIView was advantageous for nonautomatic end-user applications based on existing ITK functionalities, and MITK was preferable for automated end-user applications that might include new ITK classes specifically designed for the application