# Popularity-Aware Service Provisioning Framework in Cloud Environment
## 1 Introduction  
In the flourishing cloud industry [1], there are two kinds of players: cloud operators (i.e., cloud providers) and service providers (i.e., cloud consumers). The cloud operators have three primary deployment models: Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS) [2; 3]. Specifically, IaaS provides on-demand infrastructure resources to consumers via the cloud, such as computing, storage, networking, and virtualization. Consumers do not have to manage, maintain, or update their own data center infrastructure, but they are responsible for the operating system, middleware, virtual machines, and any apps or data. IaaS gives consumers flexibility and scalability to purchase only the components they need and scale them up or down as needed. There is low overhead and no maintenance costs, making IaaS a very affordable option. However, the main drawbacks to IaaS are the possibilities of security and service reliability issues due to multi-tenant systems with multiple clients, which can be avoided by choosing a reliable and trustworthy provider. Meanwhile, PaaS provides and manages all the hardware and software resources to develop applications through the cloud. Consumers can use PaaS to develop, run, and manage applications without having to build and maintain the infrastructure or platform on their own. PaaS enhances developer productivity but comes with limited customizations and low flexibility due to the dependency on cloud providers. Finally, SaaS provides an entire application stack that consumers can access and use. SaaS products are completely managed by the cloud provider and come ready to use, including all updates and overall maintenance. Consumers can easily access, set up, and start using SaaS products over any internet connection on any device. However, the main drawbacks to SaaS are very limited customization and vendor lock-in issues due to the tight dependency on the cloud providers. In practice, the service providers frequently use IaaS to achieve a high level of control and flexible management [4; 5]. Thus, we focus on the IaaS paradigm from the perspective of service providers. For IaaS, there are two kinds of pricing policy: pay-as-you-go and pay-as-per-use [6]. In the pricing policy of pay-as-you-go, the service provider pays a certain amount of cost to borrow a fixed amount of resources (e.g., CPU, memory, storage, etc.) for a specified period of time (e.g., 1 h) [6]. The pricing policy of pay-as-per-use, on the other hand, charges based on the usage. That is, when a service provider chooses the pricing policy of pay-as-per-use, he/she only pays for what he/she uses. However, the price of pay-as-per-use is generally more expensive than that of pay-as-you-go. Since one of the objectives in this paper is to minimize the cost of the service provider, we consider the pricing policy of pay-as-you-go. However, in pay-as-you-go, if the requests for a specific service boost up and the borrowed resources for a specific service are not sufficient to support the boosted requests, the response time of that service cannot meet its requirement. This phenomenon can be avoided by borrowing sufficient amounts of resources. However, too excessive resource borrowing causes a significant increased service deployment cost (i.e., operating expenditure (OPEX)) of the service provider. This indicates that the service provider should borrow an appropriate amount of resources based on the estimated service popularity variation (i.e., variation of service requests). To address this issue of the service providers, this paper proposes a popularity-aware service provisioning framework (PASPF). In the PASPF, the service provider assesses the popularity variation of the service by requesting the analytics to a network data analytics function (NWDAF) in 5G. The NWDAF provides the analytics that estimate the congestion level and list of top services contributing most of the traffic change for the next time period.