# Agnostic Energy Consumption Models for Heterogeneous GPUs in Cloud Computing
## 1 Introduction  
Cloud-computing usage has grown significantly, and its popularity has rapidly increased in recent years. In 2021, around 50% of the total workload was executed outside the organization's servers [1]. The size of the global market for public cloud computing has increased significantly in recent years; the market size was approximately 58.6 billion US dollars in 2009, and it is expected to grow to 362.3 billion US dollars in 2022 [2]. Due to the rapid and continuous growth of cloud computing, cloud service providers (CSPs) need to expand and update their cloud infrastructures to cover the cloud users' demands. However, several challenges arise due to the expansion of the cloud infrastructures to fit their users' requests. To expand the cloud infrastructure to cover the rapid increase of cloud user requests, CSPs need to address the issue of hardware resource heterogeneity. Different types of processors, storage, and other resources are used in cloud-computing infrastructures. Moreover, cloud-computing infrastructures have widely adopted accelerator units, such as Graphics Processing Units (GPUs), to boost their computing power capabilities inorder to cover the rapid demand for intensive computing and data applications. Several prominent cloud-computing providers including Amazon [3] have enabled the usage of different types of GPUs for their customers. Because of the programmability of GPUs, GPU usage has shifted from its standard purpose, showing images and video games on computers' screens, to computational usage, and this shows a substantial leap in the execution of high-performance intensive parallelism. Thus, this new trend in GPU usage has been utilized in a wide range of fields and applications, producing a significant performance increase. Moreover, the nature of the GPU architecture facilitates the spread of the usage of Deep Learning (DL) applications, such as image classification [4]. Training DL applications is a time-consuming task with CPUs since these applications contain massive amounts of data that need to be trained. Therefore, GPUs dramatically reduce the DL training phase. Maintaining the services' performance for end-users' demands can lead to performing some operations in the cloud infrastructure, such as live migration. These operations produce unexpected operational costs. Energy consumption is considered one of the main costs incurred by CSPs [5]. For instance, 42% of the total budget for Amazon EC2 was spent on energy consumption, divided between 19% for direct power consumption and 23% for cooling operations [6]. According to [7], in 2030, data centers will consume between 3% and 13% of global electricity compared to 1% in 2010. The estimated amount of energy consumed by these data centers will be approximately 8000 TWh in the worst-case scenario. Moreover, this increase in energy consumption harms the environment. ICT sectors produce 2% of the total CO2 emissions globally, while data centers produce 0.3% [8]. Therefore, finding solutions to reduce the energy consumed by cloud-computing infrastructures is essential. The accurate and direct measuring of power and energy consumption is not always achievable. Therefore, energy models are an alternative way to predict energy with a decent level of accuracy by using several modeling techniques, such as Machine Learning (ML) techniques. Power and energy modeling can be beneficial in several ways, such as optimizing the power consumption of applications without harmfully affecting performance. Energy modeling can also save time and financial expenses [9]. Since there is a strong relationship between power consumption and energy consumption, factors influencing power consumption will be reused for energy models with other factors. Different modeling techniques are adopted to model GPU power consumption, most of which are performed in non-cloud environments. Studies presented in [10; 11] have presented analytical models using low-level micro-architectural components. This approach can produce acceptable estimation results.