A Resource Design Framework to Realize IntentBased Cloud Management More businesses and organizations are choosing clouds to accommodate their workload, e.g., annalistic departments of enterprises use publicprivate cloud to deploy the analysis function of data, and telecommunication operators deploy Virtual Network Functions (NFVs) such as virtual Evolved Packet Core (vEPC) on clouds to provide telecommunication services. When utilizing the cloud, the cloud user is concerned about the functionality, reliability, performance, etc. of the cloudbased function, which we call the intent or the service requirements. However, to meet the intent, the user needs to tell the cloud resource orchestratorcontroller what kinds of Virtual Machines (VMs) are needed and the amount of computing resources necessary to allocate to each VM, which we call resource requirements. In our preliminary work 1, we addressed this problem and proposed an IntentBased Cloud Management (IBCM) framework to translate the cloud useru0027s service requirements into resource requirements. In this work, we have focused on realizing a Resource Design Framework (RDF) for IBCM that decides the needed computing resource amount to fulfill the cloud useru0027 intent service requirements. The main contributions of this work are as follows: (1) We have systematically analyzed factors that need to be taken into consideration when designing the resources. (2) On the basis of the analysis, we propose the architecture of RDF that is applicable for various cloudbased applications and scenarios. To the best of knowledge, this is the first work to address this issue. (3) We have validated the proposed RDF in an experimental cloudbased machine learning scenario. The experiment results show our RDF is able to infer the performance and operation state with over 90% precision, thus enabling resources to be precisely designed in accordance with performance requirements and operation policy. (4) In addition to precise resource design, we also proposed a service level agreement (SLA) violation prevention mechanism for RDF and verified its effectiveness.

An Intelligent Resource Selection Framework for Cloud Computing Environments Cloud computing provides computing resources as utility services. It offers clients with several usage scenarios. These scenarios fulfill the services requested by the clients with a lower cost as compared with deploying them locally. This paper introduces an intelligent resource selection framework that provides the client with the best set of resources in terms of computing cost and the fulfillment of clientu0027s requests. It is assumed that the requested resources can be provided to the client from multiple data centers. Additionally, cloud resources are allocated in units determined according to clientu0027s request. The client submits his resource requirements in terms of units of computing power, storage, networking, software xe2x80xa6 etc. Then, the proposed framework finds the best set of resources that satisfies the clientu0027s request. Moreover, it takes into consideration the interdependencies among resources that lead to mutually allocating certain resources together. Additionally, it considers the overhead incurred in acquiring and using the requested resources. This problem is formulated as a constrained optimization problem and has been tackled in this paper using genetic algorithms. Simulation studies show that the proposed framework provides a nearoptimal solution for the studied cases.