# Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS
## 3 IaaS Pricing Historical Data and Analysis  
### Dataset Description  
AWS data from 2016 onwards will be used to study the industry's historical trends and to forecast future pricing. In 2022, Amazon was the major IaaS provider with more than half of the market share [5]. The pricing dataset ([[https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/using-ppslong.html](https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/using-ppslong.html)]([https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/using-ppslong.html](https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/using-ppslong.html)) (accessed on 5 January 2023)), which spans over the last 7 years, is a useful resource for understanding broader market trends in IaaS and provide prediction models for market trends in the future. The Amazon EC2 dataset consists of table rows, each describing an offer for a RI type, the corresponding date, and the offered characteristics, which include a wide selection of instance types optimized to fit different use cases. Instance types are different combinations of CPU, memory, storage, and networking capabilities and are categorized into instance families, described below:* _General purpose_ instances provide an adequate combination of computing, memory, and networking capabilities, and may be utilized for a wide range of tasks. These instances are appropriate for applications such as web servers and code repositories that require these resources in equal portions. * _Memory-optimized_ instances are intended to provide rapid performance for tasks that handle huge quantities of data in memory. * _Compute-optimized_ instances are suited for compute-intensive applications that benefit from powerful CPUs. Media trans-coding, high-performance web servers, high-performance computing (HPC), scientific modeling, dedicated gaming servers, ad server engines, machine-learning inference, and other compute-intensive applications are well suited for instances in this category. * _Storage-optimized_ instances are designed for applications that need high-throughput, sequential read/write access to large datasets on local storage. They are designed to provide applications with tens of thousands of low-latency, random I/O operations per second (IOPS). * _GPU-optimized_ instances are VMs with enhanced graphics acceleration capabilities. * _Field-programmable gate array (FPGA) instances_, offering customizable hardware acceleration with FPGAs. * _ML-application-specific integrated circuit (ASIC) instances_ delivering high-performance ML inference. End-users can use them to run large-scale machine-learning inference applications such as search, recommendation engines, computer vision, speech recognition, natural language processing, and fraud detection. Table 2 summarizes the number of offers per family in the dataset in the 7-year time period. Memory-optimized instances are the most popular family followed by storage-optimized and general purpose. ML/ASIC appears to have the lowest number of offers as they were announced in late 2022 [32]. Since FPGA and ML/ASIC instances had few offers, we decided not to include them in the price-prediction models. Figure 1 provides further insight in the price distribution for each year in the dataset. For each year, the corresponding subplot contains a plot of the instance price per hour in United States dollars (USD) with respect to the offer index. Figure 1 reflects how the number of offered bundles has increased over time. In years prior to 2019, it was significantly lower than in 2022, and one can distinguish some price clustering before 2019. In later years, the range of instance offerings has increased in response to the evolving needs and demands of end-users. The Figure illustrates a trend for continuously expanding the selection of services while raising prices on high-end appliances made available to accommodate new end-user requirements.