Cloud migration patterns: a multi-cloud service architecture perspective

Many organizations migrate their on-premise software systems to the cloud. However, current coarse-grained cloud migration solutions have made a transparent migration of on-premise applications to the cloud a difficult, sometimes trial-and-error based endeavor. This paper suggests a catalogue of fine-grained service-based cloud architecture migration patterns that target multi-cloud settings and are specified with architectural notations. The proposed migration patterns are based on empirical evi-dence from a number of migration projects, best practices for cloud architectures and a systematic literature review of existing research. The pattern catalogue allows an or-ganization to (1) select appropriate architecture migration patterns based on their ob-jectives, (2) compose them to define a migration plan, and (3) extend them based on the identification of new patterns in new contexts

Flexible coordination techniques for dynamic cloud service collaboration

The provision of individual, but also composed services is central in cloud service provisioning. We describe a framework for the coordination of cloud services, based on a tuple‐space architecture which uses an ontology to describe the services. Current techniques for service collaboration offer limited scope for flexibility. They are based on statically describing and compositing services. With the open nature of the web and cloud services, the need for a more flexible, dynamic approach to service coordination becomes evident. In order to support open communities of service providers, there should be the option for these providers to offer and withdraw their services to/from the community. For this to be realised, there needs to be a degree of self‐organisation. Our techniques for coordination and service matching aim to achieve this through matching goal‐oriented service requests with providers that advertise their offerings dynamically. Scalability of the solution is a particular concern that will be evaluated in detail

Scalable architectures for platform-as-a-service clouds: performance and cost analysis

Scalability is a significant feature of cloud computing, which ad-dresses to increase or decrease the capacities of allocated virtual resources at application, platform, database and infrastructure level on demand. We investigate scalable architecture solutions for cloud PaaS that allow services to utilize the resources dynamically and effectively without directly affecting users. We have implemented scalable architectures with different session state management solutions, deploying an online shopping cart application in a PaaS solution, and measuring the performance and cost under three server-side session state providers: Caching, SQL database and NoSQL database. A commercial solution with its supporting state management components has been used. Particularly when re-architecting software for the cloud, the trade-off between performance, scalability and cost implications needs to be discussed

Pattern-based multi-cloud architecture migration

Many organizations migrate on-premise software applications to the cloud. However, current coarse-grained cloud migration solutions have made such migrations a non transparent task, an endeavor based on trial-anderror. This paper presents Variability-based, Pattern-driven Architecture Migration .V-PAM), a migration method based on (i) a catalogue of fine-grained service-based cloud architecture migration patterns that target multi-cloud, (ii) a situational migration process framework to guide pattern selection and composition, and (iii) a variability model to structure system migration into a coherent framework. The proposed migration patterns are based on empirical evidence from several migration projects, best practice for cloud architectures and a systematic literature review of existing research. Variability-based, Pattern-driven Architecture Migration allows an organization to (i) select appropriate migration patterns, (ii) compose them to define a migration plan, and (iii) extend them based on the identification of new patterns in new contexts. The patterns are at the core of our solution, embedded into a process model, with their selection governed by a variability model