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

机译:云应用程序模型驱动设计的多模型优化框架

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The rise and adoption of the Cloud computing paradigm had a strong impact on the ICT world in the last few years; this technology has now reached maturity and Cloud providers offer a variety of solutions and services to their customers. However, beside the advantages, Cloud computing introduced new issues and challenges. In particular, the heterogeneity of the Cloud services offered and their relative pricing models makes the identification of a deployment solution that minimizes costs and guarantees QoS very complex. Performance assessment of Cloud based application needs for new models and tools to take into consideration the dynamism and multi-tenancy intrinsic of the Cloud environment. The aim of this work is to provide a novel mixed integer linear program (MILP) approach to find a minimum cost feasible cloud configuration for a given cloud based application. The feasibility of the solution is considered with respect to some non-functional requirements that are analyzed through multiple performance models with different levels of accuracy. The initial solution is further improved by a local search based procedure. The quality of the initial feasible solution is compared against first principle heuristics currently adopted by practitioners and Cloud providers.
机译:在过去的几年中,云计算范式的兴起和采用对ICT世界产生了巨大影响。这项技术现已成熟,并且云提供商为他们的客户提供了各种解决方案和服务。但是,除了优势之外,云计算还带来了新的问题和挑战。尤其是,所提供的云服务的异构性及其相对的定价模型使确定最小化成本并确保QoS的部署解决方案变得非常复杂。基于云的应用程序的性能评估需要新模型和新工具,以考虑到云环境固有的动态性和多租户。这项工作的目的是提供一种新颖的混合整数线性程序(MILP)方法,以找到针对给定基于云的应用程序的最低成本的可行云配置。对于某些非功能性需求,考虑了该解决方案的可行性,这些非功能性需求通过具有不同准确度的多个性能模型进行了分析。通过基于本地搜索的过程进一步改善了初始解决方案。将初始可行解决方案的质量与从业人员和云提供商当前采用的第一原理试探法进行比较。

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