The term ‘‘Internet-of-Things’’ is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. Internet-of-Things envisions a future in which digital and physical entities can be linked, by means of appropriate information and communication technologies, to enable a whole new class of applications and services. In this article, we present a survey of technologies, applications and research challenges for Internetof-Things.

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Internet of things: Vision, applications and research challenges

Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-consuming procedures during the operating phase. These problems are primarily due to the open-loop structure often followed in software development. Therefore, there is a high demand for management complexity reduction, management automation, robustness, and achieving all of the desired quality requirements within a reasonable cost and time range during operation. Self-adaptive software is a response to these demands; it is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. These changes may stem from the software system's self (internal causes, e.g., failure) or context (external events, e.g., increasing requests from users). Such a system is required to monitor itself and its context, detect significant changes, decide how to react, and act to execute such decisions. These processes depend on adaptation properties (called self-a properties), domain characteristics (context information or models), and preferences of stakeholders. Noting these requirements, it is widely believed that new models and frameworks are needed to design self-adaptive software. This survey article presents a taxonomy, based on concerns of adaptation, that is, how, what, when and where, towards providing a unified view of this emerging area. Moreover, as adaptive systems are encountered in many disciplines, it is imperative to learn from the theories and models developed in these other areas. This survey article presents a landscape of research in self-adaptive software by highlighting relevant disciplines and some prominent research projects. This landscape helps to identify the underlying research gaps and elaborates on the corresponding challenges.

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Self-adaptive software: Landscape and research challenges

Smartphones have exploded in popularity in recent years, becoming ever more sophisticated and capable. As a result, developers worldwide are building increasingly complex applications that require ever increasing amounts of computational power and energy. In this paper we propose ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud. ThinkAir exploits the concept of smartphone virtualization in the cloud and provides method-level computation offloading. Advancing on previous work, it focuses on the elasticity and scalability of the cloud and enhances the power of mobile cloud computing by parallelizing method execution using multiple virtual machine (VM) images. We implement ThinkAir and evaluate it with a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for a N-queens puzzle application and one order of magnitude for a face detection and a virus scan application. We then show that a parallelizable application can invoke multiple VMs to execute in the cloud in a seamless and on-demand manner such as to achieve greater reduction on execution time and energy consumption. We finally use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements.

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ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading

The paper discusses the Gaia metaoperating system which extends the reach of traditional operating systems to manage ubiquitous computing habitats and living spaces as integrated programmable environments. Gaia exports services to query, access, and use existing resources and context, and provides a framework to develop user-centric, resource-aware, multidevice, context-sensitive, and mobile applications.

A middleware infrastructure for active spaces

Software-based systems today are increasingly expected to dynamically self-adapt to accommodate resource variability, changing user needs, and system faults. Recent work uses closed-loop control based on external models to monitor and adapt system behavior at run time. Taking this externalized approach, the Rainbow framework we have developed uses software architectural models to dynamically monitor and adapt a running system. A key goal and primary challenge of this framework is to support the reuse of adaptation strategies and infrastructure across different systems. We show that the separation of a generic adaptation infrastructure from system-specific adaptation knowledge makes this reuse possible.

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Rainbow: architecture-based self-adaptation with reusable infrastructure

: An important issue for software system development is the documentation of architecture designs. In this report, we describe techniques for the architectural documentation of software-based systems in the context of development processes that use UML for software design. The architectural documentation is organized in four kinds of views: problem domain view, code view, run-time view and deployment view. We examine JavaPhone(TM) as a case study to illustrate the approach: what kinds of information are provided in each kind of view, what forms of notation should be used, what are their limitations, and what uses can be made of this documentation.

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Documenting Software Architectures: Recommendations for Industrial Practice

The future success of the Smart Grid (SG) depends on many technological developments and innovations being created. The SG will be characterized by decentralized generation, improved network communications, demand response, greater reliability and robustness to failures. The future power grid will also have many interacting components needing greater capabilities to coordinate and manage power and information flows in as an efficient manner as possible. Though many power distribution engineering and analysis tools have been developed to operate the grid in a real-time manner, there are still areas requiring improved systems integration and decision-making. One such critical area that has great opportunity for improvement is at the interface between Distribution Management Systems (DMS) operated by distribution utilities and Energy Management Systems (EMS) operated by end consumers. This paper proposes the development of a set of technologies in the form of Decision-Guided Self-Architecting (DGSA) Framework whose purpose is to provide integrated, real-time optimal decision support in the common domain that intersects the operations of DMS and EMS. This is a key area requiring attention as the overall grid may be sub-optimized unless there is well-integrated and coordinated, optimal decision-making across the various power generating, distributing, and consuming entities of the SG. In addition, this paper also describes the supporting technologies upon which the framework is based and presents two key applications of the DGSA Framework: Energy Management Systems and an Energy Investment Advisor. Also presented are two cases that each illustrate various scenarios for how the framework would be implemented and the type of decisions that would be made with such a framework.

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Decision-Guided Self-Architecting Framework for integrated distribution and Energy Management

This paper discusses ongoing changes to the boundaries and roles of design and run time in the software lifecycle. Specifically, it focuses on changes caused by the emergence of situated systems in open pervasive computing environments. Clearly, such changes have a direct repercussion on the roles and tasks of system developers, stakeholders, and users.The paper proposes extensions to current software design notations, concerning (a) service discovery and ways to scope it to user-defined physical locations, and (b) the ability to incorporate and shed features and behaviors at run time, depending on which users are present and on their goals, and including the ability to resolve conflicts between such goals. Five small but illustrative example systems demonstrate the benefits of these extensions.

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Towards multi-design of situated service-oriented systems

We propose an improved approach to dynamic configuration of resource-aware applications. The new anticipatory model of configuration maximizes utility based on three inputs: user preferences, application capability profiles, and resource availability. In this respect, the proposed model is similar to a model of configuration described in [2]. However, the latter addresses the dynamic nature of the problem by reacting to changes (such as decrease in resource availability), and maximizes the utility in a point-wise manner. The newly proposed anticipatory approach explicitly models the duration of the task and leverages possible information about the future (such as stochastic resource availability over the expected duration of the task).We expect that the anticipatory model will improve user's utility, conserve scarce resources, and reduce the amount of disruption to the user resulting from changes when compared to the reactive model. However, the optimization problem underlying the anticipatory model is computationally more difficult than the problem underlying the reactive model. We would like to investigate if the anticipatory approach is feasible and efficient in practice while delivering the above-mentioned improvements. In this paper, we carefully state the model of anticipatory configuration, highlight the sources of complexity in the problem, propose an algorithm to the anticipatory configuration problem, and provide a roadmap for research.

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Anticipatory configuration of resource-aware applications

Ubiquitous computing both emphasizes the role of self-adaptation and poses new challenges to self-adaptation These challenges include the need for new kinds of adaptation and the recast of classic ones, namely self-healing, to fit ubiquitous computing environments Furthermore, because users will play an increasing role in assembling ubicomp systems, a key question is how to enable users to tailor self-adaptation to their needs

João Pedro Sousa

Papers

Documenting Software Architectures: Recommendations for Industrial Practice

Decision-Guided Self-Architecting Framework for integrated distribution and Energy Management

Towards multi-design of situated service-oriented systems

Anticipatory configuration of resource-aware applications

Towards User Tailoring of Self-Adaptation in Ubiquitous Computing