Cloud Resources Research Articles

Revolutionizing Cloud Resource Allocation: Harnessing Layer-Optimized Long Short-Term Memory for Energy-Efficient Predictive Resource Management

INTRODUCTION: This is the introductory text. Accurate data center resource projection will be challenging due to the dynamic and constantly changing workloads of multi-tenant co-hosted applications. Resource Management in the Cloud (RMC) becomes a significant research component. In the cloud's easy service option, users can choose to pay a fixed sum or based on the amount of time. OBJECTIVES: The main goal of this study is systematic method for estimating future cloud resource requirements based on historical consumption. Resource distribution to users, who require a variety of resources, is one of cloud computing main objective in this study. METHODS: This article suggests a Layer optimized based Long Short-Term Memory (LOLSTM) to estimate the resource requirements for upcoming time slots. This model also detects SLA violations when the QoS value exceeds the dynamic threshold value, and it then proposes the proper countermeasures based on the risk involved with the violation. RESULTS: Results indicate that in terms of training and validation the accuracy is 97.6%, 95.9% respectively, RMSE and MAD shows error rate 0.127 and 0.107, The proposed method has a minimal training and validation loss at epoch 100 are 0.6092 and 0.5828, respectively. So, the suggested technique performed better than the current techniques. CONCLUSION: In this work, the resource requirements for future time slots are predicted using LOLSTM technique. It regularizes the weights of the network and avoids overfitting. In addition, the proposed work also takes necessary actions if the SLA violation is recognized by the model. Overall, the proposed work in this study shows better performance compared to the existing methods.

Optimization of the Use of Cloud Computing Resources Using Exploratory Data Analysis and Machine Learning

Abstract Rapid growth in the popularity of cloud computing has been largely caused by increasing demand for scalable IT solutions, which could provide a cost-effective way to manage the software development process and meet business objectives. Optimization of cloud resource usage remains a key issue given its potential to significantly increase efficiency and flexibility, minimize costs, ensure security, and maintain high availability of services. This paper presents a novel concept of a Cloud Computing Resource Prediction and Optimization System, which is based on exploratory data analysis that acknowledges, among others, the information value of outliers and dynamic feature selection. The optimization of cloud resource usage relies on long-term forecasting, which is considered a dynamic and proactive optimization category. The analysis presented here focuses on the applicability of classical statistical models, XGBoost, neural networks and Transformer. Experimental results reveal that machine learning methods are highly effective in long-term forecasting. Particularly promising results – in the context of potential prediction-based dynamic resource reservations – have been yielded by prediction methods based on the BiGRU neural network and the Temporal Fusion Transformer.

Towards Quantum Computing-Inspired Evolutionary Algorithm for Optimized Cloud Resource Management

Towards Quantum Computing-Inspired Evolutionary Algorithm for Optimized Cloud Resource Management

Optimizing cloud resource utilization in the digital economy: An integrated Pythagorean fuzzy-based decision-making approach

The digital economy has experienced an unheard-of surge in the twenty-first century, revolutionizing how businesses run, societies function, and people interact. The utilization of cutting-edge technologies to optimize resources and streamline operations has become crucial with the changing digital landscape. Cloud computing has become a potential enabler in this transformative era, providing a flexible and scalable solution that meets the needs of the rapidly growing digital ecosystem. This study aims a novel PF-DNMA-SWARA method that amalgamates Stepwise Weight Assessment Ratio Analysis (SWARA) and double normalization-based multiple aggregation (DNMA) constructed on Pythagorean Fuzzy Sets (PFSs) to identify potential barriers to efficient cloud resource utilization for large-scale implementation in the digital economy. The results of this study uncovered a compelling narrative that emphasized the developed approach’s unique advantages in terms of its capacity to function naturally within the context of PFSs. The method offered a more complex and finely tuned evaluation framework by utilizing the inherent flexibility and interpretability of PFSs. This factor is of utmost importance, especially in complex situations where there are many uncertainties and imprecisions, making it impossible for conventional methods to accurately capture the nuances of real-world resource utilization dynamics.

New Methods for Optimal Power Allocation and Joint Resource Scheduling in 5G Network which Use Mobile Edge Computing

Mobile Edge Computing (MEC) is considered one of the enabling and promising technologies in 5G networks, especially with the massive data movement of various devices and the increased demand for computing. Here, computational offloading of tasks to edge clouds provides an effective, flexible, low-latency solution for mobile users in the network. However, the limited computing resources in edge clouds and the dynamic demands of mobile users make it difficult to schedule computing requests to appropriate edge clouds, and make the offloading process energetically expensive for devices. Therefore, it is very important to design an energy-efficient offloading strategy. To this end, we first formulate the transmission power allocation (PA) problem for mobile phone users to minimize power consumption. Using a quasi-convex technique, we address the (PA) problem using a (Hybrid GAPSO) algorithm resulting from combining the Genetic Algorithm (GA) with the Particle Swarm Optimization (PSO) algorithm. Next, we model the joint request offloading and resource scheduling (JRORS) problem as a mixed- nonlinear program to minimize the response delay of requests. The (JRORS) problem can be divided into two problems, namely the request offloading (RO) problem and the computing resource scheduling (RS) problem. Therefore, we analyze the JRORS problem as a dual decision problem and propose a multi-objective particle swarm optimization algorithm referred as (MO-PSO). The simulation results show that (HGAPSO) can save transmission power consumption and has good convergence property, and (MO-PSO) outperforms existing methods in terms of response rate and can maintain good performance in a dynamic network.

Robust and effective ab initio molecular dynamics simulations on the GPU cloud infrastructure using the Schrödinger Materials Science Suite

Ab initio Born-Oppenheimer molecular dynamics (AIMD) is a valuable method for simulating physico-chemical processes of complex systems, including reactive systems, and for training machine learning models and force fields. Speed and stability issues on traditional hardware preclude routine AIMD simulations for larger systems and longer timescales. We postulate that any practically useful AIMD simulation must generate a trajectory of a minimum 1000 MD steps a day on a moderate cloud resource. In this work, we implement a computing workflow that enables routine calculations at this throughput and demonstrate results for several non-trivial atomistic dynamical systems. In particular, we have employed the GPU implementation of the Quantum ESPRESSO code which we will show increases AIMD productivity compared to the CPU version. In order to take advantage of transient servers (which are more cost and energy effective compared to the stable servers), we have implemented automatic restart/continuation of the AIMD runs within the Schrödinger Materials Science Suite. Finally, to reduce simulation size and thus reduce compute time when modeling surfaces, we have implemented a wall potential constraint. Our benchmarks using several reactive systems (lithium anode surface/solvent interface, hydrogen diffusion in an iron grain boundary) show a significant speed up when running on a GPU-enabled transient server using our updated implementation.

AI-powered sensor fault detection for cost-effective smart greenhouses

Sensor networks in greenhouses play a pivotal role in controlling the stability of environmental and chemical factors. The Internet of Things (IoT) has been widely adopted for monitoring various sensor networks in greenhouses. In the present study, an IoT platform for remote monitoring of greenhouse environment is designed. The platform consists of a sensor node, a sink node, and an edge server. The sensor node measures indoor and outdoor humidity and temperature, interior CO(g), and interior luminosity and transmits data to the sink node, where it is timestamped. The sink node is connected to an edge server through the Mosquitto MQTT broker and the data is subsequently transferred to a MongoDB Cloud infrastructure, where the data of each variable is stored in proper formats. In the second part of the study, four 1D convolutional neural networks (CNNs) were developed for data prediction of each sensor to provide fault-tolerance in the system. The first three models, for predicting inner humidity and temperature, outdoor temperature, and outdoor humidity, directly predict the actual data of the faulty sensor based on regression analysis. The last model is designed for predicting CO and luminosity and performs data classification for faulty sensors. The models provide a high level of precision in their predictions. The RMSE for interior temperature and humidity and exterior temperature and humidity are 0.86 ℃, 3.47%, 0.682 ℃, and 2.74%, respectively. Additionally, the accuracy for luminosity and CO classification are 89.70% and 83.43%. Comparison of 1D CNN, decision tree, and linear regression model revealed that the machine learning models considerably outperform linear regression model, and they can better capture the nonlinear correlations among the variables. Furthermore, the predictive outcomes of the models were consistent across different weather conditions. The proposed methodology can be used to induce tolerance against faulty reads at sensor level in greenhouse sensor networks, independent of time and the data gathered by the faulty sensors. The study indicates that exploiting cloud resources for promoting the use of complex AI models on IoT platforms can provide a suitable solution for real-time monitoring and fault control of greenhouse environmental factors.

OCL-MEC: An online CPU-core prediction based on load balancing framework for offloading resource management in mobile edge computing environment

Clients can increase or decrease the number of resources they use dynamically over time due to the elasticity of cloud resources. As a result, variations in resource demands and predefined VM sizes result in a lack of resource utiliation, load imbalances, and excessive power consumption. A framework of efficient resource management is proposed to address these issues, balancing the load accordingly and anticipating the resource utilization of the servers. By optimizing resource utilization and minimizing the number of active servers, this technique facilitates power savings. Under/overloaded servers reduce energy consumption, execution delay, and performance degradation through a resource prediction system that is deployed at the CPU. Moreover, OCL-MEC load-balancing and resource allocation algorithms are proposed to reduce data center network traffic and power consumption. Experiments on real-world workload datasets, namely Bitsbrain VM traces, are conducted to evaluate the proposed framework. Different performance metrics demonstrate the superiority of the proposed framework over state-of-the-art approaches. Power savings of up to 98 % can be achieved by the OCL-MEC framework using a decision tree load balancing model based on HMM prediction systems.

Scalable energy optimization of resources for mobile cloud computing using sensor enabled cluster based system

Scalable energy optimization of resources for mobile cloud computing using sensor enabled cluster based system

Forensic Approaches for End-to-End Encryption Cloud Storage Services: MEGA as a Case Study

The advancement of cloud-based data storage technology allows users to conveniently access and manage files using endpoint devices without being constrained by their environment. While cloud storage services have improved the efficiency of performing our daily tasks, they have also become a medium for criminals to distribute illegal materials. Services that support end-to-end encryption (E2EE), cannot decrypt data even when it's stored on their servers, attracting users who require high security. There are some existing studies related to cloud-based services using E2EE, but they only deal with local artifacts, which makes it difficult to analyze when local devices cannot be found or when there are changes to local artifacts. This study identifies the mechanisms by which MEGA, a cloud-based file hosting service, operates to obtain user authentication, explore metadata, and collect files while applying end-to-end encryption. Furthermore, we propose a forensic investigation methodology to explore various metadata and selectively acquire cloud resources relevant to an incident through an understanding of E2EE algorithms. Also, we apply MEGA to the existing framework to suggest improving the framework that encompasses E2EE cloud-based services. The findings of this study serve as a valuable reference for dealing with cloud-based services with E2EE from the perspectives of computer security and digital forensics.

Empowering Sustainable Industrial and Service Systems through AI-Enhanced Cloud Resource Optimization

This study focuses on examining the shift of an application system from a traditional monolithic architecture to a cloud-native microservice architecture (MSA), with a specific emphasis on the impact of this transition on resource efficiency and cost reduction. In order to evaluate whether artificial intelligence (AI) and application performance management (APM) tools can surpass traditional resource management methods in enhancing cost efficiency and operational performance, these advanced technologies are integrated. The research employs the refactor/rearchitect methodology to transition the system to a cloud-native framework, aiming to validate the enhanced capabilities of AI tools in optimizing cloud resources. The main objective of the study is to demonstrate how AI-driven strategies can facilitate more sustainable and economically efficient cloud computing environments, particularly in terms of managing and scaling resources. Moreover, the study aligns with model-based approaches that are prevalent in sustainable systems engineering by structuring cloud transformation through simulation-supported frameworks. It focuses on the synergy between endogenous AI integration within cloud management processes and the overarching goals of Industry 5.0, which emphasize sustainability and efficiency that not only benefit technological advancements but also enhance stakeholder engagement in a human-centric operational environment. This integration exemplifies how AI and cloud technology can contribute to more resilient and adaptive industrial and service systems, furthering the objectives of AI and sustainability initiatives.

Improved optimization algorithm for resource management in cloud applications with performance monitor of VM provisioning, placement and recycling

The machine learning technique has been used to increase cloud management’s intelligence. Effective resource provisioning also preserves the environment. Manual cloud management has some difficult problems, such as complexity in cloud systems and scale issues. Hence, this paper introduces a new task for managing the resources in the cloud using deep learning. The aim is to predict the overall workload and server status prediction to the cloud resource management. Initially, performance monitoring is performed to keep aware of the performance of the application and guarantee the cloud application’s performance. In the suggested work, the required data is collected for the resource utilization on multiple Virtual Machine (VM) metrics. The VM provisioning is performed next to rectify the issues of resource provisioning. After that, the workload and server status prediction is conducted, where the Weighted Recurrent Neural Network (W-RNN) is adopted. After attaining the predicted workload, the VM placement module is carried out. Here, the virtual resource’s quantity is attained. Moreover, the multi-objective functions like resource utilization; cost, energy, time, and Quality of Service (QoS) are derived in this phase with the help of the Improved Rain Optimization Algorithm (IROA). Subsequently, the VM recycling is performed in the suggested work. Here, a resource collector is given for the virtual resources recycling task. It scans the applications of the cloud in the data centre and processes the VM recycling for every application. While considering the statistical analysis of the IROA-W-RNN-based resource management system achieved a mean of 56.27% than JAYA-W-RNN, 21.09% than SCO-W-RNN, 60.2% than MFOA-W-RNN, and 16.74% than DA-W-RNN for configuration 4. Finally, the numerical analysis is conducted to validate the presented resource management task with the aid of various conventional tasks.

Resource allocation strategy of space cloud network based on resource clustering

SummarySpace information network (SIN) is difficult to fully utilize the limited on‐board resource due to its dynamic and heterogeneous nature, while the traditional resource management methods cannot adapt to the increasingly diverse task requirements. Space cloud network architecture is an effective technology to reduce the pressure on satellite resources. To effectively manage the space cloud network resources, we design a resources allocation strategy based on resource clustering. Firstly, we propose the space cloud network architecture. Then, we propose a genetic algorithm to cluster the space cloud resources. Finally, we propose a dynamic resource allocation method based on reinforcement learning for the dynamic characteristics of space cloud resources. The method improves the reinforcement learning algorithm through dynamic objective optimization to complete the optimization of multiple objectives in the process of space cloud resources allocation. The simulation results show that the algorithm proposed in this paper reduces the task execution delay by an average of 10.5% compared with the original DQN algorithm and increases the execution success rate by 2.17%.

Efficiently localizing system anomalies for cloud infrastructures: a novel Dynamic Graph Transformer based Parallel Framework

Cloud environment is a virtual, online, and distributed computing environment that provides users with large-scale services. And cloud monitoring plays an integral role in protecting infrastructures in the cloud environment. Cloud monitoring systems need to closely monitor various KPIs of cloud resources, to accurately detect anomalies. However, due to the complexity and highly dynamic nature of the cloud environment, anomaly detection for these KPIs with various patterns and data quality is a huge challenge, especially those massive unlabeled data. Besides, it’s also difficult to improve the accuracy of the existing anomaly detection methods. To solve these problems, we propose a novel Dynamic Graph Transformer based Parallel Framework (DGT-PF) for efficiently detect system anomalies in cloud infrastructures, which utilizes Transformer with anomaly attention mechanism and Graph Neural Network (GNN) to learn the spatio-temporal features of KPIs to improve the accuracy and timeliness of model anomaly detection. Specifically, we propose an effective dynamic relationship embedding strategy to dynamically learn spatio-temporal features and adaptively generate adjacency matrices, and soft cluster each GNN layer through Diffpooling module. In addition, we also use nonlinear neural network model and AR-MLP model in parallel to obtain better detection accuracy and improve detection performance. The experiment shows that the DGT-PF framework have achieved the highest F1-Score on 5 public datasets, with an average improvement of 21.6% compared to 11 anomaly detection models.

Analysing and managing risk from third-party OAuth application access

Modern cloud application architectures allow users to dynamically grant third-party apps access to their cloud resources. When looking at a large, anonymised dataset of over 600,000 users and their approvals of over 43,000 applications, we found that organisations often are unaware of the magnitude of the access problems: on average, an organisation will grant 440 unique third-party apps access to Google data and resources; one organisation’s users approved 12,330 unique applications; out of all the approved applications, over 44 per cent have been granted access to either sensitive data or all data on the user’s Google Drive. The use of OAuth 2.0 has resulted in users creating numerous, unknown access paths from external third-party apps to organisations’ data and resources, in a way that is often hidden and unmanaged by organisations’ IT or security departments. In addition, the credentials granted to access users’ cloud data and resources often can be refreshed indefinitely. Security operations face challenges such as minimal to no tracking or management of these paths or credentials, along with immature or incomplete technical controls. This paper discusses an approach to understanding, assessing, analysing and managing third-party OAuth application risk, using anonymised real-world data as relevant examples, to provide prescriptive guidance on reducing risk associated with third-party applications.

Quality of Service Based on Scheduling in Cloud/Fog Environment

In the area of fog and cloud computing, efficient work scheduling is critical for optimising resource allocation and raising Quality of Service (QOS) levels. Research in this area has resulted in a variety of ways for dealing with this problem. One line of research focuses on lowering data latency and improving QOS in fog-cloud systems by precise job scheduling. This requires intelligently dividing jobs across fog and cloud resources to reduce data processing and delivery delays, ultimately improving the overall user experience. Furthermore, the investigation encompasses novel methodologies such as hybrid evolutionary algorithms and heuristic strategies. These approaches seek to create a compromise between competing objectives, such as cost and energy efficiency, while also achieving QOS criteria. Researchers aim to develop scheduling systems that optimise resource utilisation and reduce operating costs without sacrificing service quality by applying evolutionary concepts or combining heuristic methods. Furthermore, ongoing discussions focus on improving resource scheduling approaches to achieve optimal QOS results. This entails continual evaluation and adaption of scheduling algorithms to changing environmental conditions, workload fluctuations, and user requests. Furthermore, developing methodologies such as the CODA approach provide intriguing avenues for efficient job scheduling in fog computing settings, with the potential to revolutionise future resource allocation and QOS enhancement efforts.

Choice of architecture of video surveillance systems of State Border Guard of Ukraine based on cloud and fog technologies

Modern video surveillance systems have evolved from closed-circuit television systems to the level of complex systems that operate as part of complex integrated systems and solve the tasks of not only recording events in the sectors of view of video surveillance cameras but also analysing the received video information. The increase in the amount of information circulating in modern systems requires the creation of new concepts. In recent years, the attention of scientists has been drawn to distributed information processing technologies. The concept of cloud computing is developing rapidly, and the basic ideas that were laid down in its construction can no longer solve the problems faced by the technology. This has led to the emergence of so-called post-cloud architectures that extend and complement the capabilities of cloud computing. These architectures include Mist, Edge, Fog, etc. The purpose of the article is to study video surveillance systems based on the concept of cloud and fog computing and criteria for assessing their effectiveness. The research was conducted using the methods of analysis and generalisation, modelling, and experimentation. This approach made it possible to conduct a comparative study of video surveillance systems built on two architectures. The results obtained indicate that fog computing technology has the advantage of reducing latency, minimising the need for repeated requests to the cloud by performing calculations at the cloud edge. In a model based on the cloud computing concept, services that use cloud resources lead to an increase in network load. At the same time, fog computing technology allows you to relieve the network load by performing part of the computation by fog nodes. The results of the experimental study show the advantages of fog computing for networks that are sensitive to delays. However, if we consider a video surveillance system with the main task of recording events in the sectors of view of CCTV cameras and the ability to view video information in real time, it is obvious that fog nodes will not provide long-term storage of video information, and delays will not be critical. In the case of a video surveillance system with video analytics functions, fog nodes will be able to perform part of the video analytics algorithms, thus unloading the cloud. Therefore, an urgent task is to study the effectiveness of building video surveillance systems with video analytics functions based on fog architecture.

Document Management System Generative AI and AWS Comprehend

Abstract: Document Management Systems that have Generative AI and cloud features integrated in them can help users to save time. The generative ai features can boost creativity, quality and cloud resources can greatly help expand the scope of the system. A document management system built entirely on the cloud offers mobility and freedom of expression and analysis. With analysis features, users can derive important insights from texts in place and cloud storage offers the mobility to access these features from anywhere. From this, comes the possibility of an application that allows users to choose their features depending upon their needs. It is a deeper stride into the pay-as-you-go-model where the users pay only for what they use. The application provides a model to what a truly dynamic feature rich application might look like

Enterprise cloud resource optimization and management based on cloud operations

The so-called automated operation and maintenance refers to a large number of repetitive tasks in daily IT operations (from simple daily checks, configuration changes and software installation to organizational scheduling of the entire change process) from manual execution in the past to standardized, streamlined and automated operations. This article delves into the realm of enterprise cloud resource optimization and management, leveraging automated operations (autoOps) as a fundamental strategy. As industries like banking witness exponential growth and innovation in IT systems, the complexity of managing resources escalates. Automated operations have emerged as a critical component, transitioning from manual interventions to encompass standardization, workflow optimization, and architectural enhancements. Through real-world deployments and theoretical frameworks, it elucidates effective strategies for optimizing and governing enterprise cloud resources, thereby enhancing efficiency, security, and resilience in IT operations.

Enterprise cloud resource optimization and management based on cloud operations

The so-called automated operation and maintenance refers to a large number of repetitive tasks in daily IT operations (from simple daily checks, configuration changes and software installation to organizational scheduling of the entire change process) from manual execution in the past to standardized, streamlined and automated operations. This article delves into the realm of enterprise cloud resource optimization and management, leveraging automated operations (autoOps) as a fundamental strategy. As industries like banking witness exponential growth and innovation in IT systems, the complexity of managing resources escalates. Automated operations have emerged as a critical component, transitioning from manual interventions to encompass standardization, workflow optimization, and architectural enhancements. Through real-world deployments and theoretical frameworks, it elucidates effective strategies for optimizing and governing enterprise cloud resources, thereby enhancing efficiency, security, and resilience in IT operations.