Cloud Computing Environment Research Articles

Farmland fertility algorithm based resource scheduling for makespan optimization in cloud computing environment

Resource scheduling (RS) for makespan optimization in a cloud computing (CC) environment is an important aspect of handling effective resources in the cloud. Makespan optimization defines the minimization of time required to complete a collection of tasks in a computational environment. In the context of CC, makespan optimization aims to reduce the overall time required to execute tasks while effectively allocating and managing resources. RS in CC is a difficult task because of the number and variation of resources accessible and the volatility of usage-patterns of the resource assuming that the resource setting is on the service providers. Therefore, this article presents a Farmland Fertility Algorithm based Resource Scheduling for Makespan Optimization (FFARS-MSO) in Cloud Computing Environment. The presented FFARS-MSO technique is mainly based on FFA, which is stimulated by the farmland fertility in nature where the farmers split the various regions of the farm based on soil quality, and thereby every region's soil quality is distinct from others. In addition, the presented FFARS-MSO technique is utilized for load balancing and uniform distribution of resources depending upon the demand. The simulation outcomes ensure that the FFARS-MSO approach has reached effectual resource allocation over other optimization algorithms.

A Comparative Study of Deep Neural Networks and Support Vector Machines for Unsupervised Anomaly Detection in Cloud Computing Environments

Abstract: Cloud computing is now ubiquitous and provides convenient access to computing resources on demand. Cloud environments are complex and prone to faults, which can have a negative impact on service quality. Cloud providers must be able to detect issues in a proactive manner using unsupervised anomaly detection. This does not require labeled data. This paper presents a comparison of deep neural networks and support vector machine (SVMs), both used for unsupervised anomaly identification in cloud environments. On benchmark datasets provided by cloud providers, we evaluate the performance Autoencoders with LSTM models, One Class SVMs, and Isolation Forests. Our results show that shallow Autoencoders do not capture workload patterns well, but LSTMs or Convolutional Autoencoders can. SVMs are as good or better than Autoencoders. One-Class SVMs show robust performance in all workloads. Isolation Forests perform poorly on cloud data that is seasonal. One-Class SVMs are the most accurate and low latency option for anomaly detection. Our findings offer cloud providers guidance on how to select suitable unsupervised models based upon their performance, interpretability, and computational overhead. The results and comparative methodology will be used to inform future research into adapting unsupervised-learning for cloud anomaly detection.

Advances in Malware Analysis and Detection in Cloud Computing Environments: A Review

Advances in Malware Analysis and Detection in Cloud Computing Environments: A Review

Gaussian Flower Pollination Optimization Based Trusted Service Selection for Cloud Computing Environment

Gaussian Flower Pollination Optimization Based Trusted Service Selection for Cloud Computing Environment

Optimal Usage of Resources through Quality Aware Scheduling in Containers based Cloud Computing Environment

For cloud computing, the Quality Aware Scheduling of Containers (QASC) model has been proposed for delay-sensitive tasks. Plan your cloud tasks. Typically, time constraints are met while resources are used effectively. It's a really difficult undertaking. In order to distribute containers more effectively, QASC takes into account a number of performance factors. Containers and their make-span logs, as well as input quality metrics like I/O-intensive workload, startup time, hot standby failure rate, and inter-container dependencies, are collected by the QASC model. A metric coefficient that indicates each container's overall rating is calculated by normalizing and averaging these values to determine it. In order to determine how well scheduling performed, the model also includes a quality coefficient that calculates this metric-coefficient threshold. It's also critical for QASC to be able to determine the remaining energy in each container, which represents its request capacity. In order to optimize cloud resources, energy use is also taken into account by the model. From the cloud-sim simulation, an experimental dataset including 50 containers and 1,200 internet protocol-capable users was employed. For the make-span ratio, round-trip time, and energy consumption analysis, this produced 20,000 data points. The RLSched, DSTS, and ADATSA models were contrasted with the QASC model. The outcomes showed that QASC performed better than these models in a number of crucial areas. Tasks may be managed better with the higher average make-span ratio and lower volatility. Its superior job scheduling and resource use were further demonstrated by its shorter round-trip durations and lower energy usage across loads. The QASC model is an extremely complex scheduling method for container-based systems and a significant advancement in cloud computing research. Its approaches and methods enable for more intelligent energy use as well as high-quality services while also improving system performance, particularly for tasks that are delay-sensitive.

A Proposed Framework of Vulnerability Assessment and Penetration Testing (VAPT) in Cloud Computing Environments from Penetration Tester Perspective

Penetration testing is a process that focuses on finding security vulnerabilities in a target environment that could let an attacker penetrate the network or computer system or steal information. Due to the COVID-19 endemic, most employees still implement working from home or a hybrid approach, even though the number of new cases of COVID-19 is decreasing. However, working from home depends mainly on cloud computing applications that help employees efficiently accomplish their daily work. This situation also increased the number of data generated from various sources, so they may be exposed to different security risks. This research will propose a framework to conduct vulnerability assessment and penetration testing (VAPT) in cloud service models such as SaaS, PaaS, and IaaS from the perspective of penetration testers. This proposed framework is developed through the integration and mapping of existing frameworks and guidelines to conduct VAPT on testing components such as web applications, APIs, network testing, etc. In this proposed framework, the method of conducting VAPT for each cloud service model will be discussed in detail, from the planning and reconnaissance stage until the report is delivered to the cloud subscriber or cloud provider. An advantage of this proposed framework for the penetration tester is that there is still a lack of methods or guidelines for conducting VAPT that cover all the cloud service models in one comprehensive document

E-learning in the Cloud Computing Environment: Features, Architecture, Challenges and Solutions

E-learning in the Cloud Computing Environment: Features, Architecture, Challenges and Solutions

CloudSec: A Lightweight and Agile Approach to Secure Medical Image Transmission in the Cloud Computing Environment

CloudSec: A Lightweight and Agile Approach to Secure Medical Image Transmission in the Cloud Computing Environment

Construction And Performance Evaluation of Big Data Prediction Model Based on Fuzzy Clustering Algorithm in Cloud Computing Environment

In the evolving landscape of biomedical biometrics, where multimodal approaches are increasingly crucial for reliable user authentication, this research presents a comprehensive study. The primary focus is on the construction and performance evaluation of a robust big data prediction model within a cloud computing environment. The advent of big data and cloud computing has revolutionized the field of biomedical biometrics, offering immense potential for advanced data analysis and prediction. This research presents the development and evaluation of a robust prediction model for multimodal biometric data in biomedical applications. The proposed model incorporation of Reliable Discrete Variable Topology (RDVT) into the prediction model. RDVT introduces a topological data structure that enhances data reliability and ensures the integrity of multimodal biometric information. The construction and training of the prediction model are meticulously detailed, encompassing data preprocessing, feature extraction, clustering, classification, and model evaluation. Additionally, the integration of a fuzzy clustering algorithm enhances the model's ability to handle uncertainty and imprecision in biometric data. The advancement of multimodal biometrics in the biomedical field by introducing the Reliable Discrete Variable Topology (RDVT) and a big data prediction model based on a fuzzy clustering algorithm in a cloud computing environment. The model's performance is rigorously assessed through extensive experimentation, including accuracy, precision, recall, and F1-score measurements.

Retracted: Analysis of the Impact of Big Data on E-Commerce in Cloud Computing Environment

Retracted: Analysis of the Impact of Big Data on E-Commerce in Cloud Computing Environment

Survey on Virtualization Technique and Its Role in Cloud Computing Environment

Survey on Virtualization Technique and Its Role in Cloud Computing Environment

DPSO: A Hybrid Approach for Load Balancing using Dragonfly and PSO Algorithm in Cloud Computing Environment

Load balancing is one of the promising challenges in cloud computing system. For solving the issues, many heuristic, meta heuristic, evolutionary and hybrid algorithms have been proposed by the researchers. Still, it is under way of research for finding optimal solution in dynamic change in behaviour of task as well as computing environments. Attempts have been made to develop a hybrid framework to balance the load in cloud environment by obtain the best fitness value. To achieve an optimal resource for load balancing, the proposed framework integrates Dragonfly (DF) and Particle Swarm Optimization (PSO) algorithm. The performance of the proposed method is compared with PSO and Dragonfly algorithm. The performance is evaluated in different measures such as best fitness value, response time by varying the user base and response time. The user bases are varied from 50, 100, 500, and 1000. Similarly, the population size has been varied to observe the performance of the algorithm. It is observed that the proposed method outperforms the other approached for load balancing. The statistical analysis and standard testing also validate the relative superiority of PSO a considerable Dragonfly Algorithm. The hybrid approach provides better response time.

Maximizing the Efficiency and Performance: The Role of Load Balancer in Modern Cloud Computing Environment

Load balancing is a process of distributing the client requests from client application to the backend servers in an efficient way throughout the internet.There are several algorithms available to distribute this request. Cloud computing generally based on “pay per use” model that provides services like Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS). we have different types of cloud types like Public, Private, Hybrid and Community clouds. While making the efficiency and performance maximum we will face some challenges and we need to make key strategies, The evolving role it plays in the pursuit of superior performance and scalability. Optimizing resource utilization and maintaining consistent performance are crucial for businesses and organizations in the dynamic cloud computing landscape. In this we have discussed about Golden Eagle optimization Algorithm (GEO) and Mouse And Cat optimization(CMO) algorithm .The proposed Mouse-Cat-Golden Eagle (MCGEO) algorithm employs a unique combination of three animal- inspired optimization techniques to dynamically allocate tasks across servers in a distributed computing environment.

Subject Review: Enhancing Cyber Security through Machine Learning Model in Cloud Computing Environment

Cloud computing (CC) has become more and more popular in environments that use distributed computing. Cloud-based processing and data storage is becoming a global trend. Perhaps might put up the effect that Software as a Service (SaaS) has on numerous commercial applications and daily lives as disruptive technology. Cloud computing (CC) is an extension of Internet-based computing, where devices can access shared software, resources, and data as required. Machine learning (ML) has become a popular tool in the areaof cybersecurity(CS)through the cloud computing environment in recent years. Examples of its applications include biometric-based user authentication and the detection of malware or intrusions. However, attacks on machine learning (ML) algorithms may occur in both the testing and training phases, which typically results in significant performance reductions and safety breaches. This article provides a comprehensive study on the use of machine learning (ML) algorithmsto enhance cybersecurity (CS), as well as a comparison of most of the machine learning (ML) techniques mentioned in the work. The article presents future work of previous works that help researchers find ways to improve cybersecurity.

Mathematical Models for Predicting Network Traffic in Cloud Computing Environments

Effective network traffic prediction is crucial for optimizing resource allocation and ensuring efficient performance in cloud computing environments. In this research, mathematical models are developed to use historical data, factors, and other data to predict future network traffic patterns more effectively. In this context, we then compare an array of time series models such as ARIMA, LSTM, as well as the Prophet model so that we can determine the cloud environments most appropriate for each. These models include time and day of the week as well as the general activities of the users in the network in order to mimic the real flow of network traffics. The experimental results concern the efficiency of the proposed models as opposed to existing approaches and give a lot of information to network administrators and cloud service providers. The outcomes make a significant contribution as to the formulation of intelligent approaches in resource management and improve the dependability and performance of cloud computing environments.

Low-carbon Data Center Operation Strategy in Cloud Computing Environments

Low-carbon Data Center Operation Strategy in Cloud Computing Environments

A Review on Technologies of Secure Outsourced Association Rule Mining in Cloud Computing Environment

A Review on Technologies of Secure Outsourced Association Rule Mining in Cloud Computing Environment

Research on the Optimization of Enterprise Resource Economic Benefits and Management Costs in Cloud Computing Environment

Abstract Based on cloud computing technology, this paper constructs a benefit-optimal enterprise resource scheduling model and an enterprise cost management prediction model to maximize the economic benefits of enterprise resources and optimize the management costs, respectively. Combining the improved greedy algorithm and the benefit function, a cloud resource scheduling algorithm based on benefit optimization is proposed, defining the benefit function about time and cost, and taking the highest user satisfaction as the optimal resource scheduling objective. A mathematical model for management cost prediction is constructed by using the fireworks algorithm to optimize the number of trees and the number of leaf nodes in the random forest, and the Informatica software is used to manage and integrate the enterprise source data. The performance of the company’s operating conditions after optimizing resource management and cost savings is explored as a case study of Company S. In 2023, the total asset turnover ratio of Company S increased to 21.45 from 0.48 in 2022, and the overall asset operating efficiency improved. Working capital rose to 262,852,200 yuan. The current ratio, quick ratio, and cash ratio improved, while the gearing ratio, equity ratio, and equity multiplier showed a downward trend. The net operating margin achieved the maximum profitability in the last three years, net total assets margin, and net equity margin, with an increase of 25.42%, 20.65%, and 31.49%, respectively.

Load Balancing Using Improved Weighted Round Robin Algorithm in Cloud Computing Environment

Load Balancing Using Improved Weighted Round Robin Algorithm in Cloud Computing Environment

Innovation of Student Quality Evaluation Model in Vocational Education under Cloud Computing Environment

Abstract This paper takes cloud computing technology as the core, constructs a student quality evaluation model, collects and analyzes students’ learning data through cloud computing technology, proposes the direction of optimization and reform of the educational quality evaluation system, and establishes a comprehensive quality evaluation management system for students. Combining AHP and entropy weight methods, the subjective and objective weights of each index are calculated, and the comprehensive weights are determined through game theory. Based on the evaluation method of students’ comprehensive quality, the cloud model is established, the normal cloud digital features are calculated, and the mapping of qualitative concepts to quantitative representations is realized through the forward cloud generator, which completes the construction of the evaluation index system of students’ comprehensive quality. The evaluation cloud model is obtained by using the digital features calculation, and the cloud is located between levels 8 and 9 because E x = 8.5265, which has a closer fit with level 8, indicating that the comprehensive quality of the student belongs to the good upper level. In the comparison of the innovation of the student quality evaluation model, the comprehensive theoretical quality of the students in the experimental group is 8.215 points, and the P value is 0, significantly higher than that of the control group students, and the model innovation effect is remarkable.