To adapt to volatile edge environments this paper envisages distributed systems built from small units of coordi-nation called microcompositions. Microcompositions decompose a single goal into subgoals and declaratively express compo-sition and coordination constraints. These microcompositions are managed by agents that self-organise themselves into a distributed management overlay structure - a Goal Realisation Tree (GRT) - based on goal realisation (means-ends) links. Both microcompositions and GRTs serve as runtime models, allowing agents to dynamically restructure them in response to changes in goals, service provision and context. Results from our evaluation demonstrate the effectiveness and scalability of our approach.

This paper envisages an agent-based approach for creating resilient, self-managing microservice systems. The approach is based on a categorisation of the adaptation space for microservices across two dimensions – application/infrastructure and service-type/composition. The monitoring and adaptation actions in the quadrants defined by these dimensions are distinct, requiring controllers/agents with different management capabilities. Based on this division of responsibility, an agent-oriented architectural approach is proposed – the Agent Mesh. A service mesh provides the observability and control of a network overlay that connects agents and services, while agents with the different capabilities defined above close the control loops at the application and infrastructure levels. A core feature of the approach is that both domain (managed) services and managing agents are integrated as microservices within the same mesh, and communicate with each other via sidecar proxies, thereby allowing adaptation of both the domain services and self-adaptation of the agents.

Resilience and antifragility are highly desirable properties for systems operating in dynamic, contested environments. Recent work has proposed an approach for achieving antifragility through three new self-* properties, one of which is adversarial self-exploration. In that approach, a single agent is responsible for defending a (distributed) managed system against an adversary. However, a major limitation is that the agent requires full observability of the managed system and its environment, which is not practical in many scenarios - including when a system operates in contested environments. We address this limitation by extending the approach to multi-agent self-exploration, where agents with partial observability and control of the managed system coordinate with other agents to compute the most resilient responses in the presence of adversaries. We demonstrate the feasibility and scalability of the proposed approach through extensive experimentation.

Abstract The electronic calendar is a valuable resource nowadays for managing our daily life appointments or schedules, also known as events, ranging from professional to highly personal. Researchers have studied various types of calendar events to predict smartphone user behavior for incoming mobile communications. However, these studies typically do not take into account behavioral variations between individuals. In the real world, smartphone users can differ widely from each other in how they respond to incoming communications during their scheduled events. Moreover, an individual user may respond the incoming communications differently in different contexts subject to what type of event is scheduled in her personal calendar. Thus, a static calendar-based behavioral model for individual smartphone users does not necessarily reflect their behavior to the incoming communications. In this paper, we present a machine learning based context-aware model that is personalized and dynamically identifies individual’s dominant behavior for their scheduled events using logged time-series smartphone data, and shortly name as ‘CalBehav’. The experimental results based on real datasets from calendar and phone logs, show that this data-driven personalized model is more effective for intelligently managing the incoming mobile communications compared to existing calendar-based approaches.

In today’s dynamic ICT environments, the ability to control users’ access to information resources and services has become ever important. On the one hand, it should provide flexibility to adapt to the users’ changing needs, while on the other hand, it should not be compromised. The user is often faced with different contexts and environments that may change the user’s information needs. To allow for this, it is essential to incorporate the dynamically changing context information into the access control policies to reflect different contexts and environments through the use of a new context-aware access control (CAAC) approach with both dynamic associations of user-role and role-permission capabilities. Our proposed CAAC framework differs from the existing access control frameworks in that it supports context-sensitive access control to information resources and dynamically re-evaluates the access control decisions when there are dynamic changes to the context. It uses the dynamic context information to specify the user-role and role-permission assignment policies. We first present a formal policy model for our framework, specifying CAAC policies. Using this model, we then introduce a policy ontology for modeling CAAC policies and a policy enforcement architecture which supports access to resources according to the dynamically changing context information. In addition, we demonstrate the feasibility of our framework by considering (i) the completeness, correctness and consistency of the ontology concepts through application to healthcare scenarios and (ii) the performance and usability testing of the framework when using desktop and mobile-based prototypes.

Summary With the single‐instance multitenancy (SIMT) model for composite Software‐as‐a‐Service (SaaS) applications, a single composite application instance can host multiple tenants, yielding the benefits of better service and resource utilization and reduced operational cost for the SaaS provider. An SIMT application needs to share services and their aggregation (the application) among its tenants while supporting variations in the functional and performance requirements of the tenants. The SaaS provider requires a middleware environment that can deploy, enact, and manage a designed SIMT application, to achieve the varied requirements of the different tenants in a controlled manner. This paper presents the SDSN@RT (software‐defined service networks at runtime) middleware environment that can meet the aforementioned requirements. SDSN@RT represents an SIMT composite cloud application as a multitenant service network, where the same service network simultaneously hosts a set of virtual service networks, one for each tenant. A service network connects a set of services and coordinates the interactions between them. A virtual service network realizes the requirements for a specific tenant and can be deployed, configured, and logically isolated in the service network at runtime. SDSN@RT also supports the monitoring and runtime changes of the deployed multitenant service networks. We show the feasibility of SDSN@RT with a prototype implementation and demonstrate its capabilities to host SIMT applications and support their changes with a case study. The performance study of the prototype implementation shows that the runtime capabilities of our middleware incur little overhead.

Mobile phones can record individual’s daily behavioral data as a time-series. In this paper, we present an effective time-series segmentation technique that extracts optimal time segments of individual’s similar behavioral characteristics utilizing their mobile phone data. One of the determinants of an individual’s behavior is the various activities undertaken at various times-of-the-day and days-of-the-week. In many cases, such behavior will follow temporal patterns. Currently, researchers use either equal or unequal interval-based segmentation of time for mining mobile phone users’ behavior. Most of them take into account static temporal coverage of 24-h-a-day and few of them take into account the number of incidences in time-series data. However, such segmentations do not necessarily map to the patterns of individual user activity and subsequent behavior because of not taking into account the diverse behaviors of individuals over time-of-the-week. Therefore, we propose a behavior-oriented time segmentation (BOTS) technique that takes into account not only the temporal coverage of the week but also the number of incidences of diverse behaviors dynamically for producing similar behavioral time segments over the week utilizing time-series data. Experiments on the real mobile phone datasets show that our proposed segmentation technique better captures the user’s dominant behavior at various times-of-the-day and days-of-the-week enabling the generation of high confidence temporal rules in order to mine individual mobile phone users’ behavior.

Mobile phone log data (e.g., phone call log) is not static as it is progressively added to day-by-day according to individual's diverse behaviors with mobile phones. Since human behavior changes over time, the most recent pattern is more interesting and significant than older ones for predicting individual's behavior. The goal of this poster paper is to identify the recent behavioral data length dynamically from the entire phone log for recency-based behavior modeling. To the best of our knowledge, this is the first dynamic recent log-based study that takes into account individual's recent behavioral patterns for modeling their phone call behaviors.

A composite cloud application can achieve economies of scale by sharing partner services between tenants at runtime, following the Single-Instance Multi-Tenancy (SIMT) model. However, supporting runtime sharing with tenant-specific functional and performance variations in an SIMT application is challenging. We address this challenge by proposing the Software-Defined Service Networking (SDSN) approach. SDSN realizes an SIMT composite cloud application with a managed service network and a set of managed virtual service networks (VSNs) that share the service network. The service network is formed by connecting a set of partner services according to their capabilities and interoperability. To achieve the functional and performance requirements of a particular tenant, a VSN composes a subset of the services under a specific configuration design over the service network, and regulates the interactions between the services concerned according to a specific regulation design. We describe how a service network and its VSNs are designed and enacted to achieve the SIMT objectives. We show the feasibility of SDSN, demonstrate the utilization benefits it achieves, and quantify the runtime overhead it incurs.

Mobile or cellular phones can record various types of context data related to a user's phone call activities. In this paper, we present an approach to discovering individualized behavior rules for mobile users from their phone call records, based on the temporal context in which a user accepts, rejects or misses a call. One of the determinants of an individual's phone behavior is the various activities undertaken at various times of a day and days of the week. In many cases, such behavior will follow temporal patterns. Currently, researchers modeling user behavior using temporal context statically segment time into arbitrary categories (e.g., morning, evening) or periods (e.g., 1 hour). However, such time categorization does not necessarily map to the patterns of individual user activity and subsequent behavior. Therefore, we propose a behavior-oriented time segmentation (BOTS) technique that dynamically identifies diverse time segments for an individual user's behaviors based on the phone call records. Experiments on real datasets show that our proposed technique better captures the user's dominant call response behavior at various times of the day and week, thereby enabling more appropriate rules to be created for the purpose of automated handling of incoming calls, in an intelligent call interruption management system.

The electronic calendar usually serves as a personal organizer and is a valuable resource for managing daily activities or schedules of the users. Naturally, a calendar provides various contextual information about individual's scheduled events/appointments, e.g., meeting. A number of researchers have utilized such information to predict human behavior for mobile communication, by assuming a predefined event-behavior mapping which is static and non-personalized. However, in the real world, people differ from each other in how they respond to incoming calls during their scheduled events, even a particular individual may respond differently subject to what type of event is scheduled in the calendar. Thus a static behavioral model does not necessarily map to calendar schedules and corresponding phone call response behavior of individuals. Therefore, we propose an evidencebased behavioral model (EBM) that dynamically identifies the actual call response behavior of individuals for various calendar events based on their mobile phone log that records the data related to a user's phone call activities. Experiments on real datasets show that our proposed technique better captures the user's call response behavior for various calendar events, thereby enabling more appropriate rules to be created for the purpose of automated handling of incoming calls in an intelligent call interruption management system.

With the convergence of pervasive mobile computing and social networking, interest has grown significantly in software systems and applications that are aware of users' social context to make pervasive applications more intelligent and accessible. Thus, socially-aware systems have further advanced context-aware systems taking account of human social context such as social relationships to enable the attainment of users' tasks in different domains. However, social context-awareness introduces a variety of software engineering challenges. In this paper, we address these challenges by proposing a software engineering process that provides a methodological framework for developing various types of socially-aware applications from requirements elicitation through to concrete implementation. We provide context models and software infrastructure to assist developers in rapid prototyping. We also present two case studies to demonstrate the feasibility and applicability of our software engineering process by presenting how this process can be used to develop two different types of socially-aware applications utilizing our model and infrastructure. Finally, we evaluate our software engineering approach with respect to a set of software quality metrics.

A customizable web service is a service that enables service consumers to dynamically determine variants of the service they receive. Provisioning customizable services helps to efficiently address functional variability in customer requirements. However, this is challenging due to: i) the complexity in deriving the right subset of service capabilities for a service variant and ii) the existence of a large number of variants and their dependencies. We propose a feature-based framework to tackle this challenge. In our framework, a feature model is used to capture functional variability in customer requirements at a high-level of abstraction and to provide customers with a much simpler way to customize an atomic service. A service engineering process is designed to facilitate the systematic identification and implementation of variability during service development, and to maintain the mapping between variabilities at the feature modeling level and the service implementation level. We define a generative middleware that supports service deployment and exploits the mapping to enable runtime service customization. A large scale case study based on the Amazon web services is used for evaluation. In addition to addressing the challenge in provisioning customizable services, our experiments show that the generative middleware helps to reduce runtime resource consumption.

With the advances of Internet technologies and an explosive growth in the popularity of social media, an increasingly large part of human life is getting digitized and becoming available on the web. This phenomenon brings opportunities and motivates us to infer users’ situations by exploiting their interaction events in various social media such as online social networks, blogs and email. One of the key requirements of inferring situations from interaction events is to consider both the semantic and temporal aspects of events in the situation inference process. In this paper, we address this issue and propose a novel approach to exploiting users’ interaction events in social media to infer their situations. We present an ontology-based interaction event model that captures the properties of users’ interaction activities in social media. We further provide a rule-based situation specification technique that integrates the interaction event ontology (for semantically matching interaction events) with temporal event relationships (for correlating historical interaction events). We also provide a platform to realize the situation reasoning/inference process, which combines semantic matching and complex event processing. We conduct a performance evaluation of the platform to quantify its efficacy. The feasibility and applicability of our approach is demonstrated by developing a socially aware phone call application as a case study.

In modern communication environments, the ability to provide access control to information resources and software services in a context-aware manner is crucial. By leveraging the dynamically changing context information, we can achieve context-specific control over access to such resources and services, better satisfying the security and privacy requirements of the stakeholders. Existing access control approaches are highly domain-specific and they control access to services depending on the specific types of context information (e.g. location and time). One of the key limitations of the existing approaches is the lack of systematic capture and use of context information in making context-aware access control decisions. Therefore, new access control approaches are required for such dynamic and context-aware environments. Existing approaches define context as the state/situation of the entities. To achieve context-aware access control, in this paper we not only consider the states of the entities but also consider the states of the relationships between entities. We introduce a generic framework, OntCAAC (Ontology-based Context-Aware Access Control), that adopts semantic technologies in modelling dynamic contexts and corresponding access control policies. It includes a context model specific to access control, capturing the relevant context information. The context model also incorporates the ability to infer high-level implicit context information according to operator-defined rules. Using the context model, the policy model of the OntCAAC framework provides support for specifying and enforcing context-aware access control policies. We have developed a prototype implementation of the framework and have demonstrated its use in making context-aware access control decisions through two case studies from different domains. Experimental results show the feasibility of our approach and quantify the performance overhead of providing context-aware access control for software services.

We present a social context as a service (SCaaS) platform for managing adaptations in collaborative pervasive applications that support interactions among a dynamic group of actors such as users, stakeholders, infrastructure services, businesses and so on. Such interactions are based on predefined agreements and constraints that characterize the relationships between the actors and are modeled with the notion of social context. In complex and changing environments, such interaction relationships, and thus social contexts, are also subject to change. In existing approaches, the relationships among actors are not modeled explicitly, and instead are often hard-coded into the application. Furthermore, these approaches do not provide adequate adaptation support for such relationships as the changes occur in user requirements and environments. In our approach, inter-actor relationships in an application are modeled explicitly using social contexts, and their execution environment is generated and adaptations are managed by the SCaaS platform. The key features of our approach include externalization of the interaction relationships from the applications, representation and modeling of such relationships from the domain and actor perspectives, their implementation using a service oriented paradigm, and support for their runtime adaptation. We quantify the platform's adaptation overhead and demonstrate its feasibility and applicability by developing a telematics application that supports cooperative convoy.

The efficient management of shared resources in virtualized environments has become an important issue with the advent of cloud computing. This is a challenging management task because the resources of a single physical server may have to be shared between multiple virtual machines (VMs) running applications with different performance objectives, under unpredictable and erratic workloads. A number of existing works have developed performance differentiation and resource management techniques for shared resource environments by using linear feedback control approaches. However, the dominant nonlinearities of performance differentiation schemes and virtualized environments mean that linear control techniques do not provide effective control under a wide range of operating conditions. Instead of using linear control techniques, this paper presents a new nonlinear control approach that enables achieving differentiated performance requirements effectively in virtualized environments through the automated provisioning of resources. By using a nonlinear block control structure called the Hammerstein and Wiener model, a nonlinear feedback control system is integrated to the physical server (hypervisor) to efficiently achieve the performance differentiation objectives. The novelty of this approach is the inclusion of a compensation framework, which reduces the impact of nonlinearities on the management system. The experiments conducted in a virtual machine environment have shown significant improvements in performance differentiation and system stability of the proposed nonlinear control approach compared to a linear control system. In addition, the simulation results demonstrate the scalability of this nonlinear approach, providing stable performance differentiation between 10 applications/VMs.

Many approaches have been proposed to develop self-adaptive software systems based on control engineering methods in recent years. However, these research works only evaluate the self-adaptive capabilities of the proposed control solution, but no evaluation is performed to quantify the costs of implementing such a control solution. This paper provides results of an empirical study, conducted to quantify the implementation, testing and knowledge requirement costs of building a self-adaptive software system using control engineering methods. Our objective is to investigate, whether these costs can be significantly reduced if a library of prepackaged control components is available to software engineers. The findings of the study indicate that the aforementioned costs can be significantly reduced when supporting libraries are available. We also list the lessons learned from this study and recommendations, which may be useful in designing experiments to evaluate engineering costs of self-adaptive methods in the future.

Context-awareness and adaptability are highly desirable features for services that are operating in dynamic environments. Recently, a number of approaches have been introduced to support the development of such services. But, validating the varying requirements of these services is still a major challenge. In this paper, we introduce a novel scenario-based approach to address this challenge. First, our approach captures a service's requirements as two sets of scenarios: functional and adaptation. The functional scenarios represent the service's core functionality, while the adaptation scenarios capture the service's runtime adaptation in response to context changes. The service properties that need to hold at runtime are also represented graphically in a form similar to the scenarios. Second, a technique is introduced to enumerate and generate the specifications of a service's variants from its scenarios. The generated variants are then validated against the service properties to ensure their validity. This technique also checks the consistency of the service's adaptation requirements (scenarios). Case studies have shown that with our approach, a small number of service scenarios specified by the software engineer is able to cover a large number of service variants, which are generated and validated automatically.

Verifying adaptive behavior is a critical challenge in the development of context-aware systems due to their complexity and uncertainty. This paper presents our novel model-based approach that provides a modeling framework to (1) specify the structural and behavioral aspects of a context-aware system, (2) define invariants of the system that need to be satisfied regardless of the adaptations, and (3) support formal verification of the system model against the invariants. Underlying this framework is our ROAD4Context model that supports the separation of adaptation concerns in context-aware systems. We show how the behavioral model of ROAD4Context can be translated into Petri nets, and how it can be verified against the system invariants. We demonstrate our approach through the modeling and verification of an adaptive cruise control system.

Availability of several web services having a similar functionality has led to using quality of service (QoS) attributes to support services selection and management. To improve these operations and be performed proactively, time series ARIMA models have been used to forecast the future QoS values. However, the problem is that in this extremely dynamic context the observed QoS measures are characterized by a high volatility and time-varying variation to the extent that existing ARIMA models cannot guarantee accurate QoS forecasting where these models are based on a homogeneity (constant variation over time) assumption, which can introduce critical problems such as proactively selecting a wrong service and triggering unrequired adaptations and thus leading to follow-up failures and increased costs. To address this limitation, we propose a forecasting approach that integrates ARIMA and GARCH models to be able to capture the QoS attributes' volatility and provide accurate forecasts. Using QoS datasets of real-world web services we evaluate the accuracy and performance aspects of the proposed approach. Results show that the proposed approach outperforms the popular existing ARIMA models and improves the forecasting accuracy of QoS measures and violations by on average 28.7% and 15.3% respectively.

Due to differences in consumer requirements, a Web service usually has multiple service variants for use in different business contexts. In such situations, delivering customizable services helps increase efficiency not only in service description and publication but also in service consumption. However, existing approaches for providing customizable services enforce the tight coupling between providers and consumers. Nor do they take into account recursive nature of service customization. Consequently, the approaches hamper the widespread use of customizable services in SOA. In this paper, we propose a language, namely Web Service Variability Description Language (WSVL), which formalizes the customization interface between providers and consumers using the XML technology to address these problems. We also describe a reference architecture for service deployment and a service engineering technique which together support the provisioning of WSVL-based customizable services. A proof-of-concept prototype system is introduced to demonstrate the feasibility of our approach.

Predicting future values of Quality of Service (QoS) attributes can assist in the control of software intensive systems by preventing QoS violations before they happen. Currently, many approaches prefer Autoregressive Integrated Moving Average (ARIMA) models for this task, and assume the QoS attributes' behavior can be linearly modeled. However, the analysis of real QoS datasets shows that they are characterized by a highly dynamic and mostly nonlinear behavior to the extent that existing ARIMA models cannot guarantee accurate QoS forecasting, which can introduce crucial problems such as proactively triggering unrequired adaptations and thus leading to follow-up failures and increased costs. To address this limitation, we propose an automated forecasting approach that integrates linear and nonlinear time series models and automatically, without human intervention, selects and constructs the best suitable forecasting model to fit the QoS attributes' dynamic behavior. Using real-world QoS datasets of 800 web services we evaluate the applicability, accuracy, and performance aspects of the proposed approach, and results show that the approach outperforms the popular existing ARIMA models and improves the forecasting accuracy by on average 35.4%.

Currently software systems operate in highly dynamic contexts, and consequently they have to adapt their behavior in response to changes in their contexts or/and requirements. Existing approaches trigger adaptations after detecting violations in quality of service (QoS) requirements by just comparing observed QoS values to predefined thresholds without any statistical confidence or certainty. These threshold-based adaptation approaches may perform unnecessary adaptations, which can lead to severe shortcomings such as follow-up failures or increased costs. In this paper we introduce a statistical approach based on CUSUM control charts called AuDeQAV - Automated Detection of QoS Attributes Violations. This approach estimates at runtime a current status of the running system, and monitors its QoS attributes and provides early detection of violations in its requirements with a defined level of confidence. This enables timely intervention preventing undesired consequences from the violation or from inappropriate remediation. We validated our approach using a series of experiments and response time datasets from real-world web services.

In Software-as-a-Service (SaaS) delivery model a vendor maintains a single application instance, which is used by multiple tenants. However, due to changing business requirements tenants expect customizations. Providing such customizations is trivial to retain tenants but a challenge to the vendor due to multi-tenancy. In this paper we present an approach to define such customizable business processes in multi-tenant SaaS applications without unnecessary and hard to maintain duplication of process definitions and deployments.

Many software systems need to be developed with adaptation in mind, where at runtime they need to detect changes in their operating environments and adapt themselves to cope with these changes while achieving or preserving the overall system goals. In recent years, there has been much research in the communities of context-aware systems and self-adaptive systems with their own foci. However, addressing context awareness and self-adaptation in a consistent and integrated manner remains a major challenge. In this paper, we introduce a novel approach to modeling and realizing such context-aware adaptive software systems. Our approach explicitly separates but relates the context model and the system model, so that their relationships, changes, and change impacts across the system and its contexts can be clearly captured and managed. In particular, we differentiate management context from operational context as to whether or not context changes cause system adaptation. To enable runtime changes to the system, its contexts, and their relationships, they all have their runtime representation, so that they can be manipulated and managed at runtime. Our component model for realizing the approach directly supports component interface definitions for context and management as well as functionality. Furthermore, we have developed a tool to generate the system implementations from their models and to validate and verify their context-aware adaptive behavior. We demonstrate our approach through the modeling and realization of a context-aware vehicle route planning system.

Service oriented architecture plays a prominent role in creating and utilizing business services in enterprise computing environments. The service composers produce value by aggregating lower-level re-usable services, scattered across the internet to create application level services. Web service middleware facilitates in defining service compositions in a comprehensive manner. However, in order to ensure the business viability amidst unpredictably changing business requirements, such compositions may need to adapt during the runtime. Such changes might vary from a minor regulation to a major re-structuring of the IT service composition. However, the complexity of the composition shouldn't increase and the runtime interruptions to the service delivery need to be kept to a minimum. In this paper we introduce ROAD4WS, which is a middleware extension to the popular Apache Axis2 web service engine. The extension brings together the modular adaptive architecture of the Role Oriented Adaptive Design (ROAD) with the web services deployment and consumption capabilities of the Apache Axis2 engine, in order to facilitate deploying adaptive service compositions.

The convergence of Internet and mobile devices has radically changed the way people communicate and interact with each other, and demand for applications that are "social" enough to assist their daily interactions. To support such device mediated interactions, the social relationships between actors need to be systematically modeled and represented. In addition, an application facilitating such interactions should be able to deal with the task conflicts that occur when an actor is involved in multiple interactions simultaneously. To address these issues, in this paper we present an approach to modeling and coordinating social interactions with the notion of social context. It supports social interaction modeling from both the domain- and player-centric perspectives. In particular, the player-centric model provides the basis to coordinate multiple interactions in which an actor is involved. We further introduce a fuzzy logic based reasoning technique to infer the overall importance of each interaction, assisting the actor to resolve conflicts and make decisions. Finally, we validate our approach through a prototype implementation and test cases analysis.

Automotive software has increasingly become context-aware and adaptive to deal with dynamically changing environments. This paper presents our novel service-based approach to support the structural and behavioral adaptation of automotive telematics. We adopt services to (1) provide physical context facts and (2) facilitate context-aware interactions between entities of automotive telematics systems. In this paper, we introduce a layered architecture of our approach and demonstrate how the approach is applied to develop context-aware automotive telematics systems that support V2X interactions. The empirical evaluations show that our service-based approach is scalable to supporting run-time adaptation of automotive telematics.

This paper proposes a feature-oriented approach for Web services customization that can address three key challenges: reducing complexity, automated validation and dynamic deployment. We exploit techniques from Software Product Line (SPL) domain, particularly feature modeling techniques, to design our framework. Feature modeling allows to abstract variability in consumers' requirements to simplify customization processes, while automated analysis on feature models helps to validate customizations. The use of feature models at runtime enables dynamic deployment of a business process as a result of a service customization. We propose the use of feature models as service description artifacts to facilitate service customizations. The framework builds on a Model-Driven Development (MDD) approach to automate large parts of its operation.

Five properties of an architecture for secure access to web services are defined and two existing architectures are evaluated according to these criteria. References to these criteria in the literature and evaluation of the security architectures are tabulated in the conclusion. Policy-sufficiency is defined as the requirement that any meaningful statements can be expressed in policy definitions of the architecture. Protocol neutrality is the requirement that a protocol exchange which is logically equivalent to a valid protocol sequence is also valid. Predicate-boundedness is the constraint that a fixed, finite set of predicates (or language constructs) will be sufficient for security policy definitions, i.e. the language does not need to be incrementally extended indefinitely. Protocol-closure requires that security protocols can be combined together arbitrarily to make new protocols. Finally, processing complexity constrains algorithms for evaluating security rules to be of satisfactory (low) complexity. No existing security architectures receive a tick for all five of these criteria. The RW architecture is more successful in this regard than the simpler XACML architecture.

In ubiquitous computing environments, certain entities (or actors) often need to interact with each other in achieving a joint goal in a dynamically changing context. To perform such interactions in a seamless manner, the actors need to be aware of not only their physical context (e.g. location) but

Building an adaptive software system that can cope with changing requirements and changing environments presents four major challenges. These are (1) to receive, represent and reason about changing requirements and goals; (2) to cope with volatility of the computational context in which it executes; (3) to work with other systems that are heterogeneous and distributed; and (4) to do all of the above without becoming too complex to develop and maintain. These challenges can be addressed by architectures that focus on the adaptivity of relationships rather than entities. Role-Oriented Adaptive Design (ROAD) is presented as such an architecture that addresses the challenges.