Arnor Solberg

Models@ Run.time to Support Dynamic Adaptation

Todays society increasingly depends on software systems deployed in large companies, banks, airports, and so on. These systems must be available 24/7 and continuously adapt to varying environmental conditions and requirements. Such dynamically adaptive systems exhibit degrees of variability that depend on user needs and runtime fluctuations in their contexts. The paper presents an approach for specifying and executing dynamically adaptive software systems that combines model-driven and aspect-oriented techniques to help engineers tame the complexity of such systems while offering a high degree of automation and validation.

Model-driven development using UML 2.0: promises and pitfalls

Experience indicates that effective complexity management mechanisms automate mundane development tasks and provide strong support for separation of concerns. For example, current high-level programming languages and integrated development environments provide abstractions that shield developers from intricate lower-level details and offer automated support for transforming abstract representations of source code into faithful machine-executable forms. The Object Management Group initiated the Unified Modeling Language 2.0 effort to address significant problems in earlier versions. While UML 2.0 improves over earlier versions in some aspects, its size and complexity can present a problem to users, tool developers, and OMG working groups charged with evolving the standard.

Towards Model-Driven Provisioning, Deployment, Monitoring, and Adaptation of Multi-cloud Systems

In the landscape of cloud computing, the competition between providers has led to an ever growing number of cloud solutions offered to consumers. The ability to run and manage multi-cloud systems (i.e., applications on multiple clouds) allows exploiting the peculiarities of each cloud solution and hence optimising the performance, availability, and cost of the applications. However, these cloud solutions are typically heterogeneous and the provided features are often incompatible. This diversity hinders the proper exploitation of the full potential of cloud computing, since it prevents interoperability and promotes vendor lock-in, as well as it increases the complexity of development and administration of multi-cloud systems. This problem needs to be addressed promptly. In this paper, we provide a classification of the state-of-the-art of cloud solutions, and argue for the need for model-driven engineering techniques and methods facilitating the specification of provisioning, deployment, monitoring, and adaptation concerns of multi-cloud systems at design-time and their enactment at run-time.

An Aspect-Oriented and Model-Driven Approach for Managing Dynamic Variability

Constructing and executing distributed systems that can adapt to their operating context in order to sustain provided services and the service qualities are complex tasks. Managing adaptation of multiple, interacting services is particularly difficult since these services tend to be distributed across the system, interdependent and sometimes tangled with other services. Furthermore, the exponential growth of the number of potential system configurations derived from the variabilities of each service need to be handled. Current practices of writing low-level reconfiguration scripts as part of the system code to handle run time adaptation are both error prone and time consuming and make adaptive systems difficult to validate and evolve. In this paper, we propose to combine model driven and aspect oriented techniques to better cope with the complexities of adaptive systems construction and execution, and to handle the problem of exponential growth of the number of possible configurations. Combining these techniques allows us to use high level domain abstractions, simplify the representation of variants and limit the problem pertaining to the combinatorial explosion of possible configurations. In our approach we also use models at runtime to generate the adaptation logic by comparing the current configuration of the system to a composed model representing the configuration we want to reach.

Using product line techniques to build adaptive systems

Adaptive systems are able to adapt their properties and resource requirements at runtime in response to dynamically varying user needs and resource constraints. With the emergence of mobile and service oriented computing, such variation is becoming increasingly common, and the need for adaptivity is increasing accordingly. Software product line engineering has proved itself as an efficient way to deal with varying user needs and resource constraints. In this paper we present an approach to building adaptive systems based on product line oriented techniques such as variability modeling and component based architectures. By representing the product line architecture at runtime, we are able to delegate much of the complexity of adaptation to a reusable adaptation platform. To validate our approach we have built a prototype adaptation platform and developed a few pilot applications exploiting the platform to achieve adaptivity.

A Domain Specific Modeling Language Supporting Specification, Simulation and Execution of Dynamic Adaptive Systems

Constructing and executing distributed systems that can automatically adapt to the dynamic changes of the environment are highly complex tasks. Non-trivial challenges include provisioning of efficient design time and run time representations, system validation to ensure safe adaptation of interdependent components, and scalable solutions to cope with the possible combinatorial explosions of adaptive system artifacts such as configurations, variant dependencies and adaptation rules. These are all challenges where current approaches offer only partial solutions. Furthermore, in current approaches the adaptation logic is typically specified at the code level, tightly coupled with the main system functionality, making it hard to control and maintain. This paper presents a domain specific modeling language (DSML) allowing specification of the adaptation logic at the model level, and separation of the adaptation logic from the main system functionality. It supports model-checking and design-time simulation for early validation of adaptation policies. The model level specifications are used to generate the adaptation logic. The DSML also provides indirection mechanisms to cope with combinatorial explosions of adaptive system artifacts. The proposed approach has been implemented and validated through case studies.

An aspect oriented model driven framework

In model driven development (MDD), specifying transformations between models at various levels of abstraction can be a complex task. Specifying transformations for pervasive system features that are tangled with other system features is particularly difficult because the elements to be transformed are distributed across a model. This paper presents an aspect oriented model driven framework (AOMDF) that facilitates separation of pervasive features and supports their transformation across different levels of abstraction. The framework is illustrated using an example in which a platform independent model of a banking application is transformed to a platform specific model.

Cloud MF: Applying MDE to Tame the Complexity of Managing Multi-cloud Applications

The market of cloud computing encompasses an ever-growing number of cloud providers offering a multitude of infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS) solutions. The heterogeneity of these solutions hinders the proper exploitation of cloud computing since it prevents interoperability and promotes vendor lock-in, which increases the complexity of executing and managing multi-cloud applications (i.e., Applications that can be deployed across multiple cloud infrastructures and platforms). Providers of multi-cloud applications seek to exploit the peculiarities of each cloud solution and to combine the delivery models of IaaS and PaaS in order to optimise performance, availability, and cost. In this paper, we show how the Cloud Modelling Framework leverages upon model-driven engineering to tame this complexity by providing: (i) a tool-supported domain-specific language for specifying the provisioning and deployment of multi-cloud applications, and (ii) a models@run-time environment for enacting the provisioning, deployment, and adaptation of these applications.

Using aspect oriented techniques to support separation of concerns in model driven development

Model driven development (MDD) tackles software complexity through the use of models. However, managing relationships and specifying transformations between models at various levels of abstraction are complex tasks. System models tangled with concerns such as security and middleware make it difficult to develop complex systems and specify model transformations. This paper presents an MDD framework that uses aspect oriented techniques to facilitate separation of concerns. We argue that using the framework will simplify both the model development task and the task of specifying transformations. The conceptual model of the framework is presented and illustrated using distributed transactions at the PIM and PSM levels.

Managing multi-cloud systems with CloudMF

Dynamically adaptive systems (DAS) enable the continuous design and adaptation of complex software systems, but their main focus is limited to the application itself rather than the underlying platform and infrastructure. Cloud computing, in contrast, enables the management of the complete software stack, but it lacks integration with software engineering approaches, techniques, and methods from DAS. Model-based approaches have been successfully adopted for modelling DAS at design-time and facilitate their adaptation at run-time. Therefore, a natural next step is to adopt model-based approaches to enable cloud-based DAS. In this paper, we present the Cloud Modelling Framework (CloudMF), a model-based framework that addresses this issue. It consists of (i) a tool-supported domain-specific modelling language to model the provisioning and deployment of multi-cloud systems, and (ii) a models@run-time environment for enacting the provisioning, deployment and adaptation of these systems.