Frank Eliassen

Using architecture models for runtime adaptability

Every software system has architecture. The architecture strongly influences the software systems properties, including maintainability and runtime properties such as performance and reliability. By describing the architecture in models, we can make the architecture explicit. Developers typically use software architecture models at design time to capture the significant decisions about a software systems organization and to describe and establish a common understanding about the systems abstract properties. In the MADAM (mobility- and adaptation-enabling middleware) project, we aim to facilitate adaptive application development for mobile computing. We follow an architecture-centric approach where we represent architecture models at runtime to allow generic middleware components to reason about and control adaptation.

MUSIC: Middleware Support for Self-Adaptation in Ubiquitous and Service-Oriented Environments

Self-adaptive component-based architectures facilitate the building of systems capable of dynamically adapting to varying execution context. Such a dynamic adaptation is particularly relevant in the domain of ubiquitous computing, where numerous and unexpected changes of the execution context prevail. In this paper, we introduce an extension of the MUSIC component-based planning framework that optimizes the overall utility of applications when such changes occur. In particular, we focus on changes in the service provider landscape in order to plug in interchangeably components and services providing the functionalities defined by the component framework. The dynamic adaptations are operated automatically for optimizing the application utility in a given execution context. Our resulting planning framework is described and validated on a motivating scenario of the MUSIC project.

Chameleon: Adaptive Peer-to-Peer Streaming with Network Coding

Layered streaming can be used to adapt to the available download capacity of an end-user, and such adaptation is very much required in real world HTTP media streaming. The multiple layer codec has become more refined, as SVC (the scalable extension of the H.264/AVC standard) has been standardized with a bit rate overhead of around 10% and an indistinguishable visual quality, compared to the state of the art single layer codec. Peer-to-peer streaming systems have also become the reality. The important question is how such layered coding can be used in real world peer-to-peer streaming systems. This paper tries to explore the feasibility of using network coding to make layered peer-to-peer streaming much more realistic, by combining network coding and SVC in a fine granularity manner. We present Chameleon, our new peer-to-peer streaming algorithm designed to incorporate network coding seamlessly with SVC. Key components with different design options of Chameleon are presented and experimentally evaluated, with the objective of investigating benefits of network coding in combination with SVC. We carry out extensive experiments on real stream data to (i) evaluate the performance of Chameleon in terms of playback skips and delivered video quality, and (ii) understand its insights. Our results demonstrate the feasibility of the approach and bring us one step closer to real adaptive peer-to-peer streaming.

A development framework and methodology for self-adapting applications in ubiquitous computing environments

Today software is the main enabler of many of the appliances and devices omnipresent in our daily life and important for our well being and work satisfaction. It is expected that the software works as intended, and that the software always and everywhere provides us with the best possible utility. This paper discusses the motivation, technical approach, and innovative results of the MUSIC project. MUSIC provides a comprehensive software development framework for applications that operate in ubiquitous and dynamic computing environments and adapt to context changes. Context is understood as any information about the user needs and operating environment which vary dynamically and have an impact on design choices. MUSIC supports several adaptation mechanisms and offers a model-driven application development approach supported by a sophisticated middleware that facilitates the dynamic and automatic adaptation of applications and services based on a clear separation of business logic, context awareness and adaptation concerns. The main contribution of this paper is a holistic, coherent presentation of the motivation, design, implementation, and evaluation of the MUSIC development framework and methodology.

Composing components and services using a planning-based adaptation middleware

Self-adaptive component-based architectures provide methods and mechanisms to support the dynamic adaptation of their structure under evolving execution context. Dynamic adaptation is particularly relevant in the domain of ubiquitous computing, which is subject to numerous unexpected changes of the execution context. In this paper, we focus on changes in the service provider landscape: business services may dynamically come and go, and their quality of service may vary. We introduce an extension of the MADAM component-based planning framework that optimizes the overall utility of applications when such changes occur. MADAM planning is based on dynamic configuration of component frameworks. The extended planning framework supports seamless configuration of component frameworks based on both local and remote components and services. In particular, components and services can be plugged in interchangeably to provide functionalities defined by the component framework. The extended planning framework is illustrated and validated on a use case scenario.

Energy minimization approach for optimal cooperative spectrum sensing in sensor-aided cognitive radio networks

In a sensor-aided cognitive radio network, collaborating battery-powered sensors are deployed to aid the network in cooperative spectrum sensing. These sensors consume energy for spectrum sensing and therefore deplete their life-time, thus we study the key issue in minimizing the sensing energy consumed by such group of collaborating sensors. The IEEE P802.22 standard specifies spectrum sensing accuracy by the detection and false alarm probabilities, hence we address the energy minimization problem under this detection accuracy constraint. Firstly, we derive the bounds for the number of sensors to simultaneously guarantee the thresholds for high detection probability and low false alarm probability. With these bounds, we then formulate the optimization problem to find the optimal sensing interval and the optimal number of sensor that minimize the energy consumption. Thirdly, the approximated analytical solutions are derived to solve the optimization accurately and efficiently in polynomial time. Finally, numerical results show that the minimized energy is significantly lower than the energy consumed by a group of randomly selected sensors. The mean absolute error of the approximated optimal sensing interval compared with the exact value is less than 4% and 8% under good and bad SNR conditions, respectively. The approximated optimal number of sensors is shown to be very close to the exact number.

A Utility-Based Adaptivity Model for Mobile Applications

Mobile environments are characterized by resource fluctuations and limitations, and variations in user preferences. Therefore mobile applications need to be adaptive to retain usability, usefulness and reliability. In our approach to support adaptivity, we combine context awareness, reflection and component composition planning. The planning is done by generic middleware and supports dynamic discovery, utility-based and context-aware evaluation, and selection of the best implementation alternative of a given mobile application. In this paper we present a formal model of our approach and use this model to show the expressiveness of utility-based adaptation policies. To demonstrate the feasibility and expressiveness of our approach we include a case study based on a real adaptive application built using our model and middleware.

A Pluggable and Reconfigurable Architecture for a Context-Aware Enabling Middleware System

Context awareness is a core feature of modern mobile and ubiquitous computing systems. Although it has not reached its full potential yet, one can already observe significant activity in the area of software engineering for supporting the development of context-aware applications. An example of such an activity is the MUSIC project, which proposes a middleware featuring a generic and reusable context management system. This paper describes the pluggable architecture of this system, and explains how it advances the state of the art through its support for context heterogeneity and better resource utilization. The former is achieved with the use of a novel architecture, which enables the separation of low-level, platform-specific context plug-ins from higher-level application-specific ones. The improved resource utilization is achieved through intelligent activation and deactivation of context plug-ins based on the needs of the active applications. The proposed approach has been experimentally evaluated and the results indicate that it significantly improves the resource utilization in context-aware applications, especially when deployed on lightweight mobile devices.

A resource oriented integration architecture for the Internet of Things

The vision of the Internet of Things (IoT) foresees a future Internet incorporating smart physical objects that offer hosted functionality as IoT services. These services when integrated with the traditional enterprise level services form the creation of ambient intelligence for a wide range of applications. To facilitate seamless access and service life cycle management of large, distributed and heterogeneous IoT resources, service oriented computing and resource oriented approaches have been widely used as promising technologies. However, a reference architecture integrating IoT services into either of these two technologies is still an open research challenge. In this article, we adopt the resource oriented approach to provide an end-to-end integration architecture of front-end IoT devices with the back-end business process applications. The proposed architecture promises a programmer friendly access to IoT services, an event management mechanism to propagate context information of IoT devices, a service replacement facility upon service failure, and a decentralized execution of the IoT aware business processes.

Interoperability and object identity

Data model transparency can be achieved by providing a canonical language format for the definition and seamless manipulation of multiple autonomous information bases. In this paper we assume a canonical data and computational model combining the function and object-oriented paradigms. We investigate the concept of identity as a property of an object and the various ways this property is supported in existing databases, in relation to the object-oriented canonical data model. The canonical data model is the tool for combining and integrating preexisting syntactical homogeneous, but semantical heterogeneous data types into generalized unifying data types. We identify requirements for object identity in federated systems, and discuss problems of object identity and semantical object replication arising from this new abstraction level. We argue that a strong notion of identity at the federated level can only be acheived by weakening strict autonomy requirements of the component information bases. Finally we discuss various solutions to this problem that differ in their requirements with repect to giving up autonomy.