Athanasios Tsitsipas

Cloud orchestration features: are tools fit for purpose?

Even though the cloud era has begun almost one decade ago, many problems of the first hour are still around. Vendor lock-in and poor tool support hinder users from taking full advantage of main cloud features: dynamic and scale. This has given rise to tools that target the seamless management and orchestration of cloud applications. All these tools promise similar capabilities and are barely distinguishable what makes it hard to select the right tool. In this paper, we objectively investigate required and desired features of such tools and give a definition of them. We then select three open-source tools (Brooklyn, Cloudify, Stratos) and compare them according to the features they support using our experience gained from deploying and operating a standard three-tier application. This exercise leads to a fine-grained feature list that enables the comparison of such tools based on objective criteria as well as a rating of three popular cloud orchestration tools. In addition, it leads to the insight that the tools are on the right track, but that further development and particularly research is necessary to satisfy all demands.

What Can OpenStack Adopt from a Ganeti-Based Open-Source IaaS?

Cloud computing is becoming a widely adopted paradigm for the provision of different types of ICT services. Building on a set of combined technologies, it enables service provision following on-demand usage patterns. Different vendors aim at addressing the needs of private and public large and small organizations. To this end, several solutions have been developed tackling varying service, platform and infrastructure needs. In this paper, we present the outcomes of our experimentation with respect to two open-source infrastructure-level solutions, namely OpenStack and Synnefo which have attracted the attention both of the research community and of the industrial one. The goal of our qualitative and quantitative survey and experimentation is to identify shortcomings and areas for improvement in the widely adopted OpenStack project.

Experiences of models@run-time with EMF and CDO

Model-drivenengineering promotes models and modeltrans- formations as the primary assets in software development. The models@run-time approach provides an abstract rep- resentation 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 EM- F/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 experi- enced 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 sim- ilar use cases is carefully evaluated until this technology stack has realised our wish list of advanced features.

CactoSim simulation framework final prototype: accompanying document for project deliverable D6.4

SimPlugin # simulationProcess : SimProcess # remainingSimulationDuration : double # nextStopPoint : double +resumeSimulation(time : double):void +run():void +finishExecution():void +waitForSimProcessResume():void #stopFromSim(time: double double, mainEvent: SimEvent) :void Figure 75: Simulation Plugin class diagram The abstract methods to be implemented at the Simulation engine are grouped in four categories depending on their main aim: to control the simulation flow, to control the optimisation steps, reactive actions from simulation the simulation engine, and to manage the optimisation of resources.

Towards Usage-Based Dynamic Overbooking in IaaS Clouds

IaaS Cloud systems enable the Cloud provider to overbook his data centre by selling more virtual resources than physical resources available. This approach works if on average the resource utilisation of a virtual machine is lower than the virtual machine boundaries. If this assumption is violated only locally, Cloud users will experience performance degradation and poor quality of service. This paper proposes the introduction of dynamic overbooking in the sense that the overbooking factors are not equal for all physical resources, but vary dynamically depending on the resource demands of the virtual resources they host. It allows new pricing models that are dependent on the overbooking a Cloud customer is willing to accept. Additionally, we discuss prerequisites for supporting its realisation in an OpenStack private Cloud, including a monitoring system, dedicated metrics to be monitored, as well as performance models that predict the performance degradation depending on the overbooking.