Disaster recovery planning and securing business processes against outtakes have been essential parts of running a company for decades. As IT systems have become more important, and especially since more and more revenue is generated over the Internet, securing the IT systems that support the business processes against outages is essential. For cost reasons, we proposed Cloud Computing for disaster recovery in prior work. In order to update and deploy a standby system, however, a deployment method that allows a provider independent, automated deployment of distributed systems is necessary. In this work we present such a deployment method. We show that by using Cloud Standby Deployment, the recovery time can be reduced significantly, and the distributed system can be deployed provider independently.

The usage of large amounts of data has an immense potential for global economic growth and the competitiveness of countries with high technological standards. Vast amounts of data from different sources are collected and analyzed in order to seek economic profit and competitive advantages for companies and society in general. To gain profit from such data, it needs to be analyzed, processed, and interpreted. Thus, knowledge can be created and such generation of knowledge within the analysis and interpretation process constitutes the difference between "Big" and "Smart" Data. In this paper we present a taxonomy to develop standards in the field of Smart Data. It consists of 8 challenges that need to be addressed by standards and 13 fields of standardization.

Infrastructure as a Service (IaaS) is commonly used to deploy distributed systems like Web applications. For every component of a distributed system, IaaS consumers need to select, configure, and deploy a virtual machine (VM), the image to run on the VM, and software to install on the image. These tasks involve multiple decisions, require complex and error-prone manual effort, and may occur unexpectedly. To deal with these challenges, we present a feature-based configuration and vendor-independent deployment approach. An IaaS deployment model allows to describe and automatically perform the distributed system deployment vendor-independently. We use service feature modeling to support the decisions required for configuration. These modeling approaches are combined in a feature-based configuration and deployment process. It can be performed automatically and can be triggered in reaction to unexpected events. We present a proof-of-concept implementation that we use to perform a use case about deploying the Web application of the Barcoo service, showing the approach's applicability.

As the sector of SaaS is being evolved, the need of portability and effective orchestration of Cloud-enabled applications over virtualised infrastructures is more apparent. In this paper we present a proof of concept (PoC) project together with industry partners which deals with the management of portable Cloud applications. Experimentation with a multi-tier Cloud application use case gives insights into the capabilities of the TOSCA specification (version 1). To realize support for a Cloud environment based on OpenStack and Opscode Chef, an environment that allows the execution and management of TOSCA service topologies has been developed. For the evaluation of the implemented system, we orchestrate the un-/deployment of the multi-tier Cloud application use case and in the end we discuss the results and experiences of this exercise.

Virtualization is the foundation of modern, cloud-based applications. The existence of virtual machines (VM) that host the components of such applications enables their portability and scalability. VMs are used in cloud infrastructures, and with dynamic operational requirements there is a need to move VMs within and across different clouds. The successful migration of VMs from one cloud to another should not always be considered as given. The goal of this paper is to devise and implement methods and conduct functional tests towards evaluating interoperability in cloud environments. We suggest a methodology for assessing the interoperability across different systems and we conduct a survey with a series of hypervisors and operating systems.

As part of the Cloud Computing stack, Infrastructure-as-a-Service (IaaS) offerings become more and more widespread. They allow users to deploy and run virtual machines in remote data centers (the Cloud), paying by use. However, performance specifications for virtual machines provided by providers are not coherent and sometimes not even sufficient to predict the actual performance of a deployment. To measure hardware performance, hardware benchmarks are available. For measuring the performance of virtual machines in IaaS offerings, these benchmarks are not sufficient, as they don't take into account the IaaS provisioning model where the host hardware is unknown and can change. We have designed a new performance measuring method for Infrastructure-as-a-Service offerings. The method takes into account the type of service running in a virtual machine. By using the method, the actual performance of the virtual machines running a specific IaaS service is measured. This measurement can be used to better compare prices between different providers, but also to evaluate the performance actually available on a certain IaaS platform. We have evaluated the method on several Cloud infrastructure offerings of the Amazon EC2 platform, Flexi scale platform and Rack space platform to validate its utility. We show that already on EC2, the performance indicators given by providers, namely Amazon's Elastic Compute Unit, are not sufficient to determine the actual performance of a virtual machine.

Building on compute and storage virtualization, Cloud Computing provides scalable, network-centric, abstracted IT infrastructure, platforms, and applications as on-demand services that are billed by consumption. Cloud Service Engineering is the application of a systematic approach to leverage Cloud Computing in the context of the Internet in its combined role as a platform for technical, economic, organizational and social networks. This tutorial introduces concepts and technology of Cloud Computing and Cloud Service Engineering, providing an overview of state-of-the-art in research and practice.