Data-Driven Cloud Resource Deployment With High Efficiency

As a new computing paradigm,cloud computing suppots pay-as-you-go resource consumption and flexible expansion of cloud services,making the global cloud market expanding rapidly.With the emergence of new technologies such as Internet of Things and ultra-high-definition video,more and more cloud service providers are deploying cloud resources at the edge of the network to improve users' Quality of Experience(QoE)when accessing cloud services.This paper focuses on how to optimize the cloud resource deployment with the idea of big data to realize the "boost speeds,cut prices" of online services.In order to realize the efficient deployment of cloud resources,the cloud service provider needs to measure the large-scale network with high accuracy to provide data support for the cloud resource deployment;deploy the cloud infrastructure effectively to provide infrastructure support for cloud services;and finally deploy cloud services(this paper takes video services in the cloud as the case study)on the given infrastruc-ture to optimize the user's QoE while reducing the cost of video services.However,the existing network awareness scheme is difficult to take into account both measure-ment accuracy,measurement cost and network friendliness.The existing infrastructure deployment scheme is limited by the pre-given deployment candidate set,and it is dif-ficult to meet a variety of actual deployments requirements at the same time;and the existing cloud video optimization strategy is difficult to achieve the joint optimization of video cloud storage,cloud transcoding and cloud distribution.In this paper,we design a data-driven video cloud resource deployment scheme,which mainly includes the efficient network awareness,data-driven cloud infrastructure deployment,and user-centric cloud video service deployment based on widely distributed client.The research work includes:First of all,we proposed a network awareness scheme based on the widely distributed clients in the network.The scheme can achieve high accurate real-time delay between arbitrary two hosts and the statistical delay between two IP host sets without sacrific-ing users' QoE.Moreover,the scheme do not need to deploy a large number of extra measurement nodes in the network,and is network-friendly.Secondly,we proposed a user-centric framework for network infrastructure deploy-ment,which can meet the various requirements of cloud service providers with flexible infrastructure deployment.Moreover,the infrastructure deployment performance and cost are no longer limited to the pre-given candidate set.Based on the continuous relationship between the infrastructure deployment costs and the user experience,the framework can provide an effective decision support for on-demand deployment of cloud infrastructure.Finally,we proposed the joint optimization of video cloud storage,cloud transcoding and cloud distribution.Based on the modeling and analysis of the joint optimization prob-lem,we designed a Pareto-optimal deployment scheme for single-user and a greedy-based service deployment scheme for multi-user.The schemes can reduce the video service deployment cost significantly when ensuring the user satisfaction rate simultaneously.