An Enhanced Virtualization of Resources for High Performance Applications in Cloud Computing Using Deep Regression Model

This paper proposes an Enhanced Virtualization of Resources (EVR) system for high performance applications in Cloud Computing. It uses a Deep Regression Model (DRM) to predict the resource requirements of an application to be deployed on the Cloud. The model takes into account various parameters like number of users, bandwidth requirements, processing time, number of I/O requests and server capability to make accurate predictions. The model is further optimized with a Genetic Algorithm, which uses mutation, crossover and selection operations to ensure the model produces a high-accuracy output. The resulting model is then used by the EVR system to decide which server nodes should be allocated to the application for best performance. The performance of the EVR system is evaluated using metrics such as Cloud query time, Cloud response time, and Cloud resource allocation accuracy. Results demonstrate that the proposed system can provide up to 73.3% more efficiency than existing approaches in Cloud virtualization.

I. Introduction

Data-driven computing, especially cloud computing, has become mainstream in the modern day. Cloud data centers are equipped with vast amounts of hardware and software resources, all of which are connected to a single data-processing network [1]. Virtualization of these resources is often necessary to maximize their use and performance. This is especially for high-performance applications used within cloud computing, such as those used in the health and financial industries [2]. Virtualization's main advantage is that it allows the cloud to break down the computing resources into easily manageable pieces. This enables IT departments within businesses to easily allocate the necessary resources and optimize processes. When this is combined with serverless models and the power of deep learning algorithms, the performance of high-performance applications can be improved significantly [3]. Deep regression models are used to effectively analyze the data and optimize decisions. By using complex algorithms and techniques, these models are able to identify the best virtualization patterns and performance models for applications [4]. This helps in identifying which resources are needed to run a particular application and whether they are being allocated optimally. This also helps in reducing costs and increasing effectiveness. Cloud computing has become increasingly popular in recent years as a way for organizations to utilize the most up-to-date technologies. One of the major challenges of cloud computing, however, is providing resources that can meet the high performance demands of cloud applications [5]. One possible solution to this challenge is to enhance virtualization in the cloud, allowing applications to access resources more effectively. The goal of enhancing virtualization of resources in the cloud is to create a more efficient way to access and manage them [6]. This can be achieved through improved resource allocation strategies and transitioning away from traditional physical resource management models [7]. A major factor in this transition is the use of deep learning regression models to auto-tune resource allocation to optimize performance. By utilizing deep learning, cloud providers can quickly identify how best to allocate resources by utilizing predictive analysis [8]. The construction diagram has shown in the following fig. 1 Fig 1:

Construction diagram