Middleware support for the deployment of ubiquitous software components

This paper describes the design of CODE-Component Oriented Distributed Environment, which is a framework for building component-based distributed applications. This system uses the DCOM technology to provide a flexible infrastructure that supports communication among processes spread across a local area network (Intranet). A component-oriented architecture is also defined to make an easy and intuitive access to LAN-distributed processes, through a high level of abstraction. The encapsulation of these processes into software components allows developers to compose them in different ways in order to develop correct and cost-effective distributed applications. Another feature is the visual creation of distributed applications by drag & drop. We identify the requirements of a software layer that supports LAN client-server distributed completing, and we suggest a design that meets those requirements. Though our implementation uses C++, the fundamental ideas apply to any object-oriented language that supports (D)COM programming, messaging and threads.

In the Ubiquitous Computing Environment (UCE) context, the successful provision of user-required services necessitates the collaboration of various system components, encompassing hardware parts, software components, and network connections. The utilization of UCE has presented some difficulties to customers seeking services in a diverse environment, including excessive Memory Usage (MU) and prolonged Component Construction Time (CCT). To optimize the user's experience, fuzzy agent employs a non-intrusive approach in online deep-rooted learning to get insights into user behavior. Integrating the advancements mentioned above aims to enhance the connectivity between users and information technology devices by utilizing an invisible network of UCE devices, creating dynamic computational environments that can effectively meet the users' needs. This work presents a unique methodology called Fuzzy Agent Middleware to Enhance User Experiences (FAM-EUI), which aims to improve user experiences in contexts where computer technology is seamlessly incorporated into everyday activities. This research endeavors to tackle the issues associated with imprecise data and enhance user-friendly interactions by integrating fuzzy logic with intelligent agents. The results highlight the potential of FAM in enhancing user interaction within ubiquitous soft computing, leading to improved efficiency and user-centered computing systems. This study provides valuable insights into integrating soft computing and agent-based technologies to enhance ubiquitous computing paradigms.

Next generation embedded systems will be composed of large numbers of heterogeneous devices. These will typically be resource-constrained, will use different operating systems, and will be connected through different types of network interfaces. Additionally, they may be mobile and/or form ad-hoc networks with their peers, and will need to be adaptive to changing conditions based on context-awareness. As an example of these system we consider disaster recovery scenarios where large numbers of different devices need to interconnect in an ad-hoc manner. In this respect, our goal is the provisioning of a middleware framework for such system environments. Our approach is based on a small and efficient middleware kernel supporting highly modularised and customisable component-based middleware services. These services can be tailored for specific embedded environments, and are runtime-reconfigurable to support adaptivity. This paper describes a demonstration that highlights some of the features available in our middleware. In particular, we focus on heterogeneity handling by showing our middleware running on resource-rich as well as resource-constrained devices, and on adaptivity features by demonstrating runtime reprogramming and on-the-fly component deployment

The challenging nature of error handling constantly escalates as a growing number of environments consists of networked devices and software components. In these environments, errors cover a uniquely large spectrum of situations related to each layer ranging from hardware to distributed platforms, to software components. Handling errors becomes a daunting task for programmers, whose outcome is unpredictable. Scaling up error handling requires to raise the level of abstraction beyond the code level and the try-catch construct, approaching error handling at the software architecture level. We propose a novel approach that relies on an Architecture Description Language (ADL), which is extended with error-handling declarations. To further raise the level of abstraction, our approach revolves around a domain-specific architectural pattern commonly used in pervasive computing. Error handling is decomposed into components dedicated to platform-wide, error-recovery strategies. At the application level, descriptions of functional components include declarations dedicated to error handling. We have implemented a compiler for an ADL extended with error-handling declarations. It produces customized programming frameworks that drive and support the programming of error handling. Our approach has been validated with a variety of applications for building automation.

A spreading activation-based label propagation algorithm for overlapping community detection in dynamic social networks

The drift away from monolithic towards distributed, service-oriented application scenarios results in a couple of new challenges concerning the management of these systems. One particular area is the management of dependencies between services. Dependencies can have different appearances, for instance, functional dependencies, semantic integrity conditions, or even dependencies to hardware resources. In this paper we consider services which are provided by software components. The usage of software components promises to allow for flexible and highly tailorable software systems. In the context of our research we consider dependencies in order to enhance the tailorability of component- based, distributed applications. We explain different occurrences of dependencies as well as the necessity of dependency management for adaptations in component-based applications. The paper focuses on a component architecture which allows the deployment of components as well as the management of dependencies between components.

A distributed application may comprise a range of processors, from two to thousands. For the processors of an application comprising many different machines to work together, they must often coordinate. Coordination may be as simple as agreeing upon a basic configuration, e.g., a list of server addresses and ports for example. In the context of distributed systems, even agreeing upon a basic configuration can be complicated: servers may be down during configuration changes, servers may need to adapt quickly to dynamic configuration changes, and during changes servers may have conflicting configurations. Most distributed applications also require more sophisticated coordination primitives such as leader election, group membership, and rendezvous.

If the hype is to be believed, we have come very close to the realisation of a ubiquitous computing environment. There are already a wide variety of devices, networking technologies and bespoke services; and yet the vision of anywhere anytime computing is proving somewhat elusive. Software abstractions and metaphors that were developed for desktop applications do not extend to ubiquitous computing. Because of the frequency of contextual changes and the paucity of resources, new distributed applications require much more flexible support for controlled reconfiguration, self-adaptation, and recovery of components.We present a lightweight component management Middleware that provides flexibility by allowing design, deployment, and run-time reconfigurability. At design and deployment time, the developer can design a system by structuring software components according to a specific scenario. Then, at run-time, she can dynamically reconfigure the system, adjust to new environments, or dynamically add mechanisms that enables self-adaptation.

The fast development on microelectronics has promoted the increase on the computational power of hardware components. On the other hand, we are facing a significant improvement on energy consumption as well as the reduction of the physical size of such components. These improvements and the emergence of wireless networking technologies are enabling the development of small and powered mobile devices. Due to this scenario, the so-called pervasive computing paradigm, introduced by Mark Weiser in 1991 (Weiser, 1991) is becoming a reality. Such a paradigm envisions a world where environments are inhabited by computing devices, all of them seamlessly integrated into peoples’ lives, and effectively helping to carry on their daily tasks. Among others, one major characteristic of Weiser’s vision is that each device in an environment becomes a potential client or provider of resources. Not surprisingly, pervasive computing environments are becoming dynamic repositories of computational resources, all of them available to mobile users from the palm of their hands. However, devices can unpredictably join and leave such environments. Thus, resources can be dynamically made available or unavailable. Such a scenario has a great impact on the way that resources are found and used. In the case of static environments, such as the Web, it is reasonable to look up and access resources, such as Web pages, knowing the address of their providers beforehand. On the other hand, for dynamic environments, such as the pervasive computing ones, this is not a reasonable approach. This is due to the fact that one cannot guarantee that the provider of a resource will be available at any moment, because it may have left the environment or simply turned off. A better approach would be to discover these resources based on their descriptions, or any other feature that does not require the client to know the specific address of their providers. To this end, some of the current pervasive computing solutions, like Wings (Loureiro, Bublitz, Oliveira, Barbosa, Perkusich, Almeida, & Ferreira, 2006), Green (Sivaharan, Blair, & Coulson, 2005), RUNES (Costa, Coulson, Mascolo, Picco, & Zachariadis, 2005), and Scooby (Robinson, Wakeman, & Owen, 2004), are making use of a novel approach from the branch of distributed applications, the service-oriented computing paradigm (Papazoglou, 2003; Huhns & Singh, 2005). This is due to the fact that such a paradigm provides a crucial element for pervasive computing systems, the ability for dynamically binding to remote resources (Bellur & Narenda, 2005), which enables mobile devices to find needed services on demand. However, pervasive environments may be structured in different ways. They can range from wired networks to completely wireless ones, where communication among the devices is performed in an ad hoc way. Such a characteristic indicates that the way services are provisioned in a pervasive computing environment should fit in its organization, in order to enhance the access to the services available. Considering the above discussion, in this article we provide a review on service provision and its applicability in pervasive computing. More precisely, we will list the existing service provision approaches and discuss the characteristics and problems associated with each one, as well as their usage in pervasive computing environments. We start by providing introductory concepts of service-oriented and pervasive computing, respectively in the service-oriented computing and pervasive computing sections. Next, we present the service provision techniques available and how they can be applied for pervasive computing environments. The main current solutions within this scope will be introduced in the service oriented technologies section. Some of the future trends associated with research for service provision in pervasive computing environments will be presented in the future research trends section. Finally, in the conclusions sect

Minimizing the computing infrastructure (such as processors) in a distributed real-time embedded (DRE) system deployment helps reduce system size, weight, power consumption, and cost. To support software components and applications on the computing infrastructure, the hardware must provide enough processors to ensure that all applications can be scheduled without missing real-time deadlines. In addition to ensuring scheduling constraints, sufficient resources (such as memory) must be available to the software. It is hard to identify the best way(s) of deploying software components on hardware processors to minimize computing infrastructure and meet complex DRE constraints. This paper provides the following contributions to the study of the deployment of software components to hardware in DRE systems: (1) we present an algorithmic deployment technique that minimizes the required number of processors, while adhering to real-time scheduling, resource, and co-location constraints, (2) we show how this technique can be augmented with a harmonic period heuristic to further reduce the number of required processors, and (3) we present empirical data from applying three different deployment algorithms for processor minimization to a flight avionics DRE system.

Formal analysis of feature degradation in fault-tolerant automotive systems

Preface. 1. Introduction. 1.1 What is COP? 1.2 Why is COP important? 1.3 What is a component? 1.4 Hardware components and software components. 1.5 From OOP to COP. 1.6 Component-based software engineering. 1.7 Summary. 1.8 Self-review questions. 1.9 Exercises. 2. A Theory of Components. 2.1 Principles of COP. 2.2 Infrastructures of COP. 2.3 Component models. 2.4 Connection models. 2.5 Deployment models. 2.6 Unifying component technologies. 2.7 Summary. 2.8 Self-review questions. 2.9 Exercises. 3. COP with JavaBeans. 3.1 Overview of JavaBeans technology. 3.2 The component model of JavaBeans. 3.3 The connection model of JavaBeans. 3.4 The deployment model of JavaBeans. 3.5 Examples and lab practice. 3.6 Summary. 3.7 Self-review questions. 3.8 Exercises. 4. Enterprise JavaBeans Components. 4.1 The EJB a rchitecture. 4.2 The component model of EJB. 4.3 The connection model of EJB. 4.4 The deployment model of EJB. 4.5 Examples and lab practice. 4.6 Summary. 4.7 Self-review questions. 4.8 Exercises. 4.9 Programming exercises. 5. CORBA Components. 5.1 The COBRA component infrastructure. 5.2 The CORBA component model (CCM). 5.3 The connection model of CORBA and CCM. 5.4 The deployment model of CORBA and CCM. 5.5 Examples and lab practice. 5.6 Summary. 5.7 Self-review questions. 5.8 Exercises. 5.9 Programming exercises. 6. .NET Components. 6.1 The .NET framework. 6.2 The component model of .NET. 6.3 The connection model of .NET. 6.4 .NET component deployments. 6.5 Visual Studio .NET. 6.6 Examples and lab practice. 6.7 Summary. 6.8 Self-review questions. 6.9 Exercises. 6.10 Programming exercises. 7. COP with OSGi Components. 7.1 Overview of OSGi technology. 7.2 The component model of OSGi. 7.3 The connection model of OSGi. 7.4 The deployment model of OSGi. 7.5 Examples and lab practice. 7.6 Summary. 7.7 Self-review questions. 7.8 Exercises. 8. Web Services Components. 8.1 The Web Services framework. 8.2 The component model of Web Services. 8.3 The connection model of Web Services. 8.4 Web Services component deployment. 8.5 Examples and lab practice. 8.6 Summary. 8.7 Self-review questions. 8.8 Exercises. 8.9 Programming exercises. Appendix.

Anomaly analysis and visualization for dynamic networks through spatiotemporal graph segmentations

Resource-constrained embedded and mobile devices are becoming increasingly common. Since few years, some mobile and ubiquitous devices such as wireless sensor, able to be aware of their physical environment, appeared. Such devices enable proposing applications which adapt to user’s need according the context evolution. It implies the collaboration of sensors and software components which differ on their nature and their communication mechanisms. This paper proposes a unified component model in order to easily design applications based on software components and sensors without taking care of their nature. Then it presents a state of the art of communication problems linked to heterogeneous components and proposes an interaction mechanism which ensures information exchanges between wireless sensors and software components.

: Advances in wireless communication together with the growing number of mobile end devices hold the potential of ubiquitous access to sophisticated internet services; however, such access must cope with an inherent mismatch between the low-bandwidth, limited-resource characteristics of mobile devices and the high-bandwidth expectations of many content-rich services. One promising way of bridging this gap is by deploying application-specific components on the path between the device and service, which perform operations such as protocol conversion and content transcoding. Although several researchers have proposed infrastructures allowing such deployment, most rely on static, hand-tuned deployment strategies restricting their applicability in dynamic situations. In this paper, we present an automatic approach for the dynamic deployment of such transcoding components, which can additionally be dynamically reconfigured as required. Our approach relies on three components: (a) a high-level integrated type-based specification of components and network resources, essential for late binding components to paths; (b) an automatic path creation strategy that selects and maps components so as to optimize a global metric; and (c) system support for low-overhead path reconfiguration, consisting of both restrictions on component interfaces and protocols satisfying application semantic continuity requirements. We comprehensively evaluate the effectiveness of our approach over a range of network and end-device characteristics using both a web-access scenario where client performance is for reduced access time, and a streaming scenario where client preference is for increased throughput. Our results verify that (1) automatic path creation and reconfiguration is achievable and does in fact yield substantial performance benefits; and (2) that despite their flexibility, both path creation and reconfiguration can be supported with low run-time overhead.