# Intelligent Resource Orchestration for 5G Edge Infrastructures
## 3 Related Work  
The literature has extensively explored the application of artificial intelligence (AI) techniques, including evolutionary algorithms and machine learning (ML) algorithms, to address diverse prediction and optimization challenges in both cloud and edge environments. A recent study [27] offered a comprehensive review of machine-learning-based solutions for resource management in cloud computing. This review encompassed areas such as workload estimation, task scheduling, virtual machine (VM) consolidation, resource optimization, and energy efficiency techniques. Additionally, a recent book [28] compiled various research works that considered optimal resource allocation, energy efficiency, and predictive models in cloud computing. These works leveraged a range of ML techniques, including deep learning and neural networks. For edge computing platforms, surveys such as [29] have analyzed different machine and deep learning techniques for resource allocation in multi-access edge computing. Similarly, ref. [30] provided a review of task allocation and optimization techniques in edge computing, covering centralized, decentralized, hybrid, and machine learning algorithms. If we focus on workload prediction, we see this is an essential technique in cloud computing environments, as it enables providers to effectively manage and allocate cloud resources, save on infrastructure costs, implement auto-scaling policies, and ensure compliance with service-level agreements (SLAs) with users. Workload prediction can be performed at application or infrastructure level. Application-level techniques [31; 32] involve predicting a metric related to the application demand (e.g., requests per second or task arrival rate) to anticipate the optimal amount of resources needed to meet that demand. On the other hand, infrastructure-level techniques [33; 34] are based on predicting one or more resource usage metrics (such as CPU, memory, disk, or network) and making advanced decisions about the optimal amount and size of virtual or physical resources to provision to avoid overload or over-sizing situations. Another interesting piece of research in this area is [35], which presented a taxonomy of the various real-world metrics used to evaluate the performance of cloud, fog, and edge computing environments. The most common workload prediction techniques used in cloud computing are based on time-series prediction methods [19; 24], which involve collecting historical data of the target metric (e.g., the historical CPU usage of a VM or group of VMs) and forecasting future values of that metric for a certain time horizon. There are many different methods for modeling and predicting the time-series used in cloud computing, many of them based on classical techniques such as linear regression [36], Bayesian models [37], or ARIMA statistical methods [38]. The main advantage of these models is their flexibility and simplicity in representing different types of time-series, making them quick and easy to use. However, they have a significant limitation in their linear behavior, making them inadequate in some practical situations. More recently, different methods have been proposed for time-series prediction based on machine learning and deep learning models [34; 39; 40] using artificial neural networks that have inherent non-linear modeling capabilities. One of the most common ML models applied to time-series is the long short-term memory (LSTM) neural network [23; 41; 42], which overcomes the problem of vanishing gradients associated with other neural networks. However, these methods also have several drawbacks. The first is that the training and prediction times of the neural network can be quite high (several minutes, or even hours), making them unfeasible in certain situations.