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The increasing popularity of virtualized services and cloud computing, offering elastic and scalable computing resources challenges software engineering methods by asking a number of new questions: How can we integrate deployment-specific information in the overall design of software applications? How can we express and compare deployment decisions in the design phase, so that performance diagnosis can happen early in the software development cycle? How do we design scalable applications?This thesis proposes a methodology for the modeling and analysis of object-oriented distributed applications that are able to adapt to changes in client traffic, so that they can keep desired levels of performance. As a starting point for developing our methodology, we take Core ABS, an abstract, behavioral specification language that targets the executable modeling of concurrent, distributed and object-oriented systems. We extend Core ABS with deployment-specific information such as deployment architectures, flexible application-specific schedulers, deadlines at the application level, resource management primitives, and user-defined resource costs. The extensions are developed following conventions of formal methods, combining rigorous definitions and formal semantics with a user-friendly Java-like syntax and tool support. By exploiting these language extensions, our methodology makes it possible to compare at the modeling level how a software behaves under different deployment choices. Such comparisons allow a better understanding of the trade-offs from different deployment choices, consequently better design decisions for distributed applications can be made early in the software development life cycle.By integrating resource management and deployment decisions in the design phase, our methodology has the potential to improve the software engineering process for virtualized and scalable software applications running in the cloud, both with respect to quality and with respect to development and deployment cost.

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Abstract. Today's software systems are becoming increasingly configurable and designed for deployment on a plethora of architectures, ranging from sequential machines via multicore and distributed architectures to the cloud. Examples of such systems are found in, e.g., software product lines, service-oriented computing, information systems, embedded systems, operating systems, and telephony. To model and analyze systems without a fixed architecture, the models need to naturally capture and range over relevant deployment scenarios. For this purpose, it is interesting to lift aspects of low-level deployment concerns to the abstraction level of the modeling language. In this paper, the objectoriented modeling language Creol is extended with a notion of dynamic deployment components with parametric processing resources, such that processor resources may be explicitly reallocated. The approach is compositional in the sense that functional models and reallocation strategies are both expressed in Creol, and functional models can be run alone or in combination with different reallocation strategies. The formal semantics of deployment components is given in rewriting logic, extending the semantics of Creol, and executes on Maude, which allows simulations and test suites to be applied to models which vary in their available resources as well as in their resource reallocation strategies.

Abstract. An application's quality of service (QoS) depends on resource availability; e.g., response time is worse on a slow machine. On the cloud, a virtualized application leases resources which are made available on demand. When its work load increases, the application must decide whether to reduce QoS or increase cost. Virtualized applications need to manage their acquisition of resources. In this paper resource provisioning is integrated in high-level models of virtualized applications. We develop a Real-Time ABS model of a cloud provider which leases virtual machines to an application on demand. A case study of the Montage system then demonstrates how to use such a model to compare resource management strategies for virtualized software during software design. RealTime ABS is a timed abstract behavioral specification language targeting distributed object-oriented systems, in which dynamic deployment scenarios can be expressed in executable models.

Abstract. Software today is often developed for deployment on varying architectures. In order to model and analyze the consequences of such deployment choices at an early stage in software development, it seems desirable to capture aspects of low-level deployment concerns in high-level models. In this paper, we propose an integration of a generic cost model for resource consumption with deployment components in Timed ABS, an abstract behavioral specification language for executable object-oriented models. The actual cost model may be user-defined and specified by means of annotations in the executable Timed ABS model, and can be used to capture specific resource requirements such as processing capacity or memory usage. Architectural variations are specified by resource-restricted deployment scenarios with different capacities. For this purpose, the models have deployment components which are parametric in their assigned resources. The approach is demonstrated on an example of multimedia processing servers with a user-defined cost model for memory usage. We use our simulation tool to analyze deadline misses for given usage and deployment scenarios.

Abstract. Modern software systems are increasingly being developed for deployment on a range of architectures. For this purpose, it is interesting to capture aspects of low-level deployment concerns in high-level modeling languages. In this paper, an executable object-oriented modeling language is extended with resource-restricted deployment components. To analyze model behavior a formal methodology is proposed to assess resource consumption, which balances the scalability of the method and the reliability of the obtained results. The approach applies to a general notion of resource, including traditional cost measures (e.g., time, memory) as well as concurrency-related measures (e.g., requests to a server, spawned tasks). The main idea of our approach is to combine reliable (but expensive) worst-case cost analysis of statically predictable parts of the model with fast (but inherently incomplete) simulations of the concurrent aspects in order to avoid the state-space explosion. The approach is illustrated by the analysis of memory consumption.