A Multi-model Optimization Framework for the Model Driven Design of Cloud Applications

Abstract

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Cloud and Multi-cloud Computing: Current Challenges and Future Applications

“…The choice of the application architecture matching and fully exploiting the characteristics of the underlying Cloud environments is also critical [2], [3]. At the infrastructural layer, resource contentions lead to unpredictable performance [4] and additional work for resource management [5], automated VM and service migration [6] is still needed.…”

“…Model transformations help automating the work of going from abstract concepts to implementation. Moreover, models can also be used to reason about the QoS properties of an application [2] and to support design-time exploration in order to identify the Cloud deployment configuration of minimum cost, while satisfying QoS constraints [3]. Finally, models can be kept alive also at runtime to trigger dynamic adaptation [10], [5], providing QoS guarantees even under workload fluctuations, virtualized systems performance degradations, or failures.…”

QoS Assessment and SLA Management

“…A two-step approach has been developed; in the first step an initial valid configuration of the system is derived automatically starting from a partially specified application description given by the QoS engineer. In order to do so, a Mixed Integer Linear Problem (MILP) is built and efficiently solved [6]. This solution is based on approximated performance models, in fact, the QoS associated to a deployment solution is calculated by means of an M/G/1 queuing model with processor sharing policy.…”

A mixed integer linear programming optimization approach for multi-cloud capacity allocation

“…As in [18] and [19], in this work we propose a two-step approach to the problem. In the first step a model-to-model transformation is performed to obtain a Mixed Integer Linear Problem (MILP) from a set of models in the Each configuration is represented by a set of several hourly LQN models for each Cloud provider, as described in Section 2, which are more expressive and accurate, albeit at the expense of a higher computation time.…”

“…In such a situation approaches that keeps alive a single or just a couple of solutions, like the one by Ouzineb et al [55] based on a Tabu Search heuristic, are more convenient. An approach similar to the one presented in this work has been proposed in [19], where an hybrid bi-level Tabu Search was used to optimize the deployment of an application on a single cloud provider.…”