Metaverse is a vast term that can contain every digital thing in the future. Therefore, life domains, such as learning and education, should have their systems redirected to adopt this topic to keep their availability and longevity. Many papers have discussed the metaverse, the applications to run on, and the historical progress to have the metaverse the way it is today. However, the framework of the metaverse itself is still unclear, and its components cannot be exactly specified. Although E-Learning systems are a need that has developed over the years along with technology, the structures of the available E-Learning systems based on the metaverse are either not well described or are adopted, in their best case, as just a 3D environment. In this paper, we examine some previous works to find out the special technologies that should be provided by the metaverse framework, then we discuss the framework of the metaverse if applied as an E-Learning environment framework. This will make it easy to develop future metaverse-based applications, as the proposed framework will make the virtual learning environments work smoothly on the metaverse. In addition, E-Learning will be a more interactive and pleasant process.

Cloud computing enables clients to acquire cloud resources dynamically and on demand for their cloud applications and services. For cloud providers, especially, Software as a Service (SaaS) providers, the prediction of future cloud resource requirements, such as CPU usage for their cloud applications, to implement client requests is a complex task because it depends on incoming workloads. Due to workload fluctuations, it is difficult for SaaS cloud providers to predict or forecast future demand for resource usage in the next time interval and, accordingly, to allocate the required resources. Furthermore, cloud computing systems consist of many virtual machines (VMs), which increases the complexity of the prediction problem due to the correlations that exist between the large workload data in these VMs. Therefore, accurate resource usage forecasting remains a challenge, and relatively few studies have explored the prediction of CPU usage for VMs in cloud data centers. This paper proposes an autonomic and intelligent workload forecasting method for cloud resource provisioning based on the concept of autonomic computing and a deep learning approach. In particular, to predict future demand for CPU usage and determine how to respond to workload fluctuations in the next interval, we propose an efficient deep learning model based on a diffusion convolutional recurrent neural network (DCRNN). Existing deep learning models that are widely applied cannot handle accurate real-time forecasting due to the presence of inconsistent and nonlinear workloads in cloud computing systems. The goal of the proposed deep learning model is to improve forecasting accuracy and minimize the error between the predicted and the actual workloads. The effectiveness of the proposed DCRNN-based deep learning model was evaluated using experiments on a real-world dataset of PlanetLab's CPU usage traces. The results indicate that the proposed approach outperformed other existing deep learning models, achieving a mean absolute percentage error of 0.18 and root-mean-square error of 2.40.

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Cloud computing has a great ability to store and manage remote access to services in a term of software as a service (SaaS). Recently, many organizations have moved to use outsourcing over the cloud to reduce the local resource burden. The stored services over the cloud are too scalable and complex, so an optimization method is more desirable to select appropriate services that satisfy the clients' request. To do so, the quality of service (QoS) parameters that associated with each service are the best resources for choosing and optimizing the appropriate services over the cloud. Therefore, the cloud service composition aims to select and integrate services over the cloud to satisfy the clients' request. In this work, a hybrid algorithm is introduced, which combines ant colony optimization (ACO) and genetic algorithm (GA) to efficiently compose the services over the cloud. The GA is used to tune the ACO's parameters automatically and the ACO adapts its performance based on the parameters tuning. The main contribution of this work is to help the ACO algorithm to avoid stagnation problem and enhance the performance of the ACO where this performance is affected by the value of the ACO's parameters. The experimental results on 15 different real datasets have shown the effectiveness of the proposed algorithm to search comparable solutions compared to five competitors.INDEX TERMS Cloud Services Composition (CSC); Ant Colony Optimization (ACO), Genetic Algorithm (GA).

The provision of service enabled connectivity is the significant art of clouds. The long-held dream of computing as a utility has become reality in the era of cloud computing. Cloud users are now able to run and access their applications from anywhere in the world on demand. The proposed research considers dynamic capacity planning for cloud systems. The aim is to dynamically adapt computing capacity such that Service Level Agreements (SLAs) are continuously met while minimizing the total costs incurred in running the cloud services. However, research in this regard is still at its infancy. Scalability, resource heterogeneity, workload dynamicity, resource sharing and virtualization are the main challenges that need to be overcome to have effective and trustworthy schemes for capacity management, that play a vital role in cloud computing. The work in this approach is based on developing a capacity planning scheme to ensure that high-level performance targets (SLAs) are continuously met. The scheme applies threshold-based techniques to ensure meeting the SLAs while minimizing the total incurred costs. The approach is designed to work on cloud environments and hence must address these environments specific challenges.