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Evaluating High Availability-Aware Deployments Using Stochastic Petri Net Model and Cloud Scoring Selection Tool

机译:使用随机Petri网络模型和云评分选择工具评估高可用性感知部署

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摘要

Different challenges are facing the adoption of cloud-based applications, including high availability (HA), energy, and other performance demands. Therefore, an integrated solution that addresses these issues is critical for cloud services. Cloud providers promise the HA of their infrastructure while cloud tenants are encouraged to deploy their applications across multiple availability zones. Moreover, the environmental and cost impacts of running applications in the cloud are integral parts of incorporated responsibility where the cloud providers and tenants intend to reduce. Hence, an analytical stochastic model is needed for the tenants and providers to quantify the expected availability offered by an application deployment. If multiple deployment options can satisfy the HA requirement, the question remains, how can we choose the deployment that satisfies the other providers and tenants requirements? Therefore, this paper proposes a cloud scoring system and integrates it with a Stochastic Petri Net model. While the Petri Net model evaluates the availability of cloud applications deployments, the scoring system selects the optimal HA-aware deployment in terms of energy, operational expenditure, and other norms. We illustrate our approach with a use case that shows how we can use the various deployment options to satisfy both the cloud tenant and provider needs.
机译:不同的挑战正面临着采用基于云的应用,包括高可用性(HA),能量和其他性能需求。因此,解决这些问题的集成解决方案对于云服务至关重要。云提供商承诺在其基础架构的HA承诺,而鼓告云租户会跨多个可用区域部署其应用程序。此外,云中运行应用的环境和成本影响是纳入责任的组成部分,云提供商和租户打算减少。因此,租户和提供商需要分析随机模型,以量化应用程序部署所提供的预期可用性。如果多个部署选项可以满足HA要求,问题仍然存在,我们如何选择满足其他提供商和租户要求的部署?因此,本文提出了云评分系统,并将其与随机培养净净模型集成。虽然Petri网络模型评估了云应用部署的可用性,但评分系统选择了能源,运营支出和其他规范方面的最佳HA感知部署。我们用一个用例说明了我们的方法,该方法显示了如何使用各种部署选项来满足云租户和提供商需求的方式。

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