Nowadays, cloud providers offer a broad catalog of services for migrating and distributing applications in the cloud. However, the existence of a wide spectrum of cloud services has become a challenge for deciding where to host applications, as these vary in performance and cost. This work addresses such a challenge, and provides a utility-based decision support model and method that evaluates and ranks during design time potential application distributions spanned among heterogeneous cloud services. The utility model is evaluated using the MediaWiki (Wikipedia) application, and shows an improved efficiency for selecting cloud services in comparison to other decision making approaches.

Utility, defined as the perceived satisfaction with a service, provides the ideal means for decision making on the level of individual entities and collectives participating in a large-scale dynamic system.Previous works have already introduced the concept into the area of collective adaptive systems, and have discussed what is the necessary infrastructure to support the realization of the involved theoretical concepts into actual decision making.In this work we focus on two aspects.First, we provide a concrete utility model for a case study that is part of a larger research project.Second, we incorporate this model into our implementation of the proposed architecture.More importantly, we design and execute an experiment that aims to empirically evaluate the use of utility for decision making by comparing it against simpler decision making mechanisms.

The increase of available Cloud services and providers has contributed to accelerate the development and has broaden the possibilities for building and provisioning Cloud applications in heterogeneous Cloud environments. The necessity for satisfying business and operational requirements in an agile and rapid manner has created the need for adapting traditional methods and tooling support for building and provisioning Cloud applications. Focusing on the application's performance and its evolution, we observe a lack of support for specifying, capturing, analyzing, and reasoning on the impact of using different Cloud services and configurations. This paper bridges such a gap by proposing the conceptual and tooling support to enhance Cloud application topology models to capture and analyze the evolution of the application's performance. The tooling support is built upon an existing modeling environment, which is subsequently evaluated using the MediaWiki (Wikipedia) application and its realistic workload.

The emergence and consolidation of container-based virtualization techniques has simplified and accelerated the development, provisioning, and deployment of applications for the Cloud. When considering the case of composite service-based applications that rely on service middleware solutions for their operation, container-based virtualization offers the opportunity for rapid and efficient building and deployment of lightweight, optimally configured middleware instances. As such, it provides an ideal tool for the purposes of cloudifying existing middleware solutions and offering them as part of larger PaaS offerings. As part of this effort, our investigation focuses on leveraging and evaluating a container-based virtualization environment towards enabling the assembly, provisioning, and execution of dynamically tailored instances to satisfy service middleware communication requirements of specific applications. For these purposes we scope the discussion on one particular type of messaging middleware for composite service applications, the Enterprise Service Bus (ESB) technology.

The success of the Cloud computing paradigm, together with the increase of Cloud providers and optimized Infrastructure-as-a-Service (IaaS) offerings have contributed to a raise in the number of research and industry communities that are strong supporters of migrating and running their applications in the Cloud. Focusing on eScience simulation-based applications, scientific workflows have been widely adopted in the last years, and the scientific workflow management systems have become strong candidates for being migrated to the Cloud. In this research work we aim at empirically evaluating multiple Cloud providers and their corresponding optimized and nonoptimized IaaS offerings with respect to their offered performance, and its impact on the incurred monetary costs when migrating and executing a workflow-based simulation environment. The experiments show significant performance improvements and reduced monetary costs when executing the simulation environment in off-premise Clouds.

The on-demand provisioning of services, a cloud-based extension for traditional service-oriented architectures, improves the handling of services in usage scenarios where they are only used rarely and irregularly. However, the standard process of service provisioning and de-provisioning shows still some shortcomings when applying it in real world. In this paper, we introduce a middleware-centric optimization approach that can be integrated in the existing on-demand provisioning middleware in a loosely coupled manner, changing the standard provisioning and de-provisioning behavior in order to improve it with respect to cost and time. We define and implement a set of optimization strategies, evaluate them based on a real world use case from the eScience domain and provide qualitative as well as quantitative decision support for effectively selecting and parametrizing a suitable strategy. Altogether, our work improves the applicability of the existing on-demand provisioning approach and system in real world, including guidance for selecting the suitable optimization strategy for specific use cases.

The wide adoption of the cloud computing paradigm by many different domains has increased both the number and type of available offerings as a service, e.g. Database-as-a-service (DBaaS) or Platform-as-a-Service (PaaS), and with them the possibilities to deploy and operate an application partially or completely in the cloud. The need for providing design support to application developers in this environment is the focus of this work. Toward this goal, in the following we first scope the discussion on the persistence layer of applications and we investigate the effect of different deployment scenarios on the performance of the application over time. Based on the results of this analyses we then propose an application (re-)distribution design support process, which we evaluate empirically by means of a well-known application. The results of this evaluation both highlight the strengths of our proposal, and at the same time, provide a clear path for the following steps in our work.

The Cloud computing paradigm emerged by establishing new resources provisioning and consumption models. Together with the improvement of resource management techniques, these models have contributed to an increase in the number of application developers that are strong supporters of partially or completely migrating their application to a highly scalable and pay-per-use infrastructure. In this paper we derive a set of functional and non-functional requirements and propose a process-based approach to support the optimal distribution of an application in the Cloud in order to handle fluctuating over time workloads. Using the TPC-H workload as the basis, and by means of empirical workload analysis and characterization, we evaluate the application persistence layer's performance under different deployment scenarios using generated workloads with particular behavior characteristics.

Large-scale systems comprising of multiple heterogeneous entities are directly influenced by the interactions of their participating entities.Such entities, both physical and virtual, attempt to satisfy their objectives by dynamically collaborating with each other, and thus forming collective adaptive systems.These systems are subject to the dynamicity of the entities' objectives, and to changes to the environment.In this work we focus on the latter, i.e. on providing the means for entities in such systems to model, monitor and evaluate their perceived utility by participating in the system.This allows for them to make informed decisions about their interactions with other entities in the system.For this purpose we propose a utility-based approach for decision making, as well as an architecture that allows for the support of this approach.

Service orientation has significantly facilitated the development of complex distributed systems spanning multiple organizations.However, different application areas approach such systems in domain-specific ways, focusing only on particular aspects relevant for their application types.As a result, we observe a very fragmented landscape of service-oriented systems, which does not enable collaboration across organizations.To address this concern, in this work we introduce the notion of Collaborative, Dynamic and Complex (CDC) systems and position them with respect to existing technologies.In addition, we present how CDC systems are modeled and the steps to provision and execute them.Furthermore, we contribute an architecture and prototypical implementation, which we evaluate by means of a case study in a Cloud-enabled context-aware pervasive application.

In the last years Cloud computing has become popular among IT organizations aiming to reduce their operational costs. Applications can be designed to run in the Cloud, or can be partially or completely migrated to it. Migrating the data layer of an application to the Cloud, however, implies that existing applications might need to be adapted in order to access their migrated to the Cloud databases. In this work we examine how we can use an existing ESB to enable transparent access to the relational data store running either in the Cloud or on-premise. The goal of our approach is to minimize the effort required to adapt the application. In particular, we discuss the requirements and prototype realization of a Cloud aware data access layer for transparent data access, using an existing open source and multi-tenant aware ESB as the basis. We then evaluate the performance of our proposed solution by considering different Cloud providers and using example data from an existing benchmark as application workload.

Nowadays different Cloud services enable enterprises to migrate applications to the Cloud. An application can be partially migrated by replacing some of its components with Cloud services, or by migrating one or multiple of its layers to the Cloud. As a result, accessing application data stored off-premise requires mechanisms to mitigate the negative impact on Quality of Service (QoS), e.g. due to network latency. In this work, we propose and realize an approach for transparently accessing data migrated to the Cloud using a multi-tenant open source Enterprise Service Bus (ESB) as the basis. Furthermore, we enhance the ESB with QoS awareness by integrating it with an open source caching solution. For evaluation purposes we generate a representative application workload using data from the TPC-H benchmark. Based on this workload, we then evaluate the optimal caching strategy among multiple eviction algorithms when accessing relational databases located at different Cloud providers.