Orchestrator conversation : distributed management of cloud applications

Managing cloud applications is complex, and the current state of the art is not addressing this issue. The ever-growing software ecosystem continues to increase the knowledge required to manage cloud applications at a time when there is already an IT skills shortage. Solving this issue requires capturing IT operation knowledge in software so that this knowledge can be reused by system administrators who do not have it. The presented research tackles this issue by introducing a new and fundamentally different way to approach cloud application management: a hierarchical collection of independent software agents, collectively managing the cloud application. Each agent encapsulates knowledge of how to manage specific parts of the cloud application, is driven by sending and receiving cloud models, and collaborates with other agents by communicating using conversations. The entirety of communication and collaboration in this collection is called the orchestrator conversation. A thorough evaluation shows the orchestrator conversation makes it possible to encapsulate IT operations knowledge that current solutions cannot, reduces the complexity of managing a cloud application, and happens inherently concurrent. The evaluation also shows that the conversation figures out how to deploy a single big data cluster in less than 100 milliseconds, which scales linearly to less than 10 seconds for 100 clusters, resulting in a minimal overhead compared with the deployment time of at least 20 minutes with the state of the art

Experiences of models@run-time with EMF and CDO

Model-driven engineering promotes models and model transformations as the primary assets in software development. The models@run-time approach provides an abstract representation of a system at run-time, whereby changes in the model and the system are constantly reflected on each other. In this paper, we report on more than three years of experience with realising models@run-time in scalable cloud scenarios using a technology stack consisting of the Eclipse Modelling Framework (EMF) and Connected Data Objects (CDO). We establish requirements for the three roles domain-specific language (DSL) designer, developer, and operator, and compare them against the capabilities of EMF/CDO. It turns out that this technology stack is well-suited for DSL designers, but less recommendable for developers and even less suited for operators. For these roles, we experienced a steep learning curve and several lacking features that hinder the implementation of models@run-time in scalable cloud scenarios. Performance experiences show limitations for write heavy scenarios with an increasing amount of total elements. While we do not discourage the use of EMF/CDO for such scenarios, we recommend that its adoption for similar use cases is carefully evaluated until this technology stack has realised our wish list of advanced features