Intent-driven cloud resource design framework to meet cloud performance requirements and its application to a cloud-sensor system

Abstract

In cloud service delivery, the cloud user is concerned about “what” function and performance the cloud service could provide, while the cloud provider is concerned about “how” to design proper underlying cloud resources to meet the cloud user’s requirements. We take the cloud user’s requirement as intent and aim to translate the intent autonomously into cloud resource decisions. In recent years, intent-driven management has been a widely spread management concept which aims to close the gap between the operator’s high-level requirements and the underlying infrastructure configuration complexity. Intent-driven management has drawn attention from telecommunication industries, standards organizations, the open source software community and academic research. There are various application of intent-driven management which are being studied and implemented, including intent-driven Software Defined Network (SDN), intent-driven wireless network configuration, etc. However, application of intent-driven management into the cloud domain, especially the translation of cloud performance-related intent into the amount of cloud resource, has not been addressed by existing studies. In this work, we have proposed an Intent-based Cloud Service Management (ICSM) framework, and focused on realizing the RDF (Resource Design Function) to translate cloud performance-related intent into concrete cloud computation resource amount settings that are able to meet the intended performance requirement. Furthermore, we have also proposed an intent breach prevention mechanism, P-mode, which is essential for commercial cloud service delivery. We have validated the proposals in a sensor-cloud system, designed to meet the user’s intent to process a large quantity of images collected by the sensors in a restricted time interval. The validation results show that the framework achieved 88.93 ~ 90.63% precision for performance inference, and exceeds the conventional resource approach in the aspects of human cost, time cost and design results. Furthermore, the intent breach prevention mechanism P-mode significantly reduced the breach risk, at the same time keeping a high level of precision.