# Agnostic Energy Consumption Models for Heterogeneous GPUs in Cloud Computing
## 1 Introduction
Yet, such kinds of models need a deep knowledge of the GPU micro-architecture [12], and it is hard to acquire information on heterogeneous GPU micro-architecture components when running several applications, since the power consumption of each micro-architectural component needs to be measured and the access rate when running every application needs to be calculated [13]. Other studies, such as [14; 15], have used low-level micro-architectural GPU power modeling and are aimed to be used as GPU power simulators. Simulation tools are useful for cost reduction. However, these simulations produce significant overhead and require a high level of knowledge for the configuration setup. Additionally, simulation tools are unsuitable for several generations of GPU architectures such as the Kepler architecture [12]. Therefore, we consider using the Machine Learning (ML) approach to deal with these difficulties, and it can produce remarkably accurate results as well. Even though there are already several research studies considering ML for the development of GPU power consumption models like [16; 17], they use hardware performance counters as input factors in non-cloud environments. Moreover, modeling energy consumption is not an effortless task. Serval studies can be performed to model energy consumption since different factors should be handled. Some recent studies have developed energy models. Hardware heterogeneity increases the difficulty of developing an energy consumption model. Although some studies deal with heterogeneous GPUs in terms of energy modeling, they handle these GPUs individually or handle homogeneous GPUs performed in non-cloud environments, such as [9; 18]. A few studies have been performed in cloud-computing environments. Therefore, this work aims to investigate other factors affecting GPU power consumption and consider them in the development of GPU power models for heterogenous GPU architectures combined with performance counters in cloud computing. Moreover, this work aims to develop a unified GPU energy model to handle different GPU architectures in cloud computing. The contributions of this work are as follows:  
* Novel GPU power consumption models considering hybrid inputs for heterogeneous GPU architectures in cloud computing. This model aims to predict the power consumed by GPU applications running on a heterogeneous cloud-computing infrastructure. * A novel agnostic GPU energy consumption model. This model aims to automatically estimate the energy consumed by GPU applications executing on a heterogeneous cloud-computing infrastructure and abstract the heterogeneity of GPU architectures. The remaining sections in this work are structured as follows: Section 2 presents the related work in GPU power-modeling and GPU energy-modeling techniques in cloud-computing and non-cloud-computing environments. Section 3 discusses the power and energy models in detail. This section begins with the details of developing the GPU power model. The GPU energy consumption model development details are then introduced. Section 4 shows the results of this work. Section 5 concludes this work and discusses future work directions.