Utilization Of Cloud Resources Research Articles

Location-aware job scheduling for IoT systems using cloud and fog

The rapid growth of Internet of Things (IoT) applications generates vast volumes of high-speed data streams from numerous devices. Cloud computing solutions often handle and manage this data; however, for certain applications, the latency introduced by transmitting data from edge devices to the cloud may be unacceptable. This issue is exacerbated by the bandwidth constraints of public networks, which become significant barriers in large-scale IoT implementations. Consequently, effective resource management, service management, data storage, and power management require more robust infrastructure and complex protocols. An "intelligent gateway" based on fog computing can enhance the efficient utilization of cloud and network resources. Resource planning and management in a fog-cloud environment significantly impact system performance, particularly latency. This problem is known to be NP-hard. This paper addresses the challenge of lifetime optimization for scheduling data-intensive tasks in fog and cloud-based IoT systems. Initially, we propose an Integer Linear Programming (ILP) optimization model to frame the problem. Subsequently, we introduce a heuristic method named Data-Locality Aware Job Scheduling in Fog-Cloud (DLSFC), which is derived from the proposed formulation. The effectiveness of DLSFC is evaluated across various system characteristics, with results demonstrating that DLSFC achieves solutions within 85 % of the ideal outcome provided by the LP solver.

API management and cloud integration model for SMEs

In the rapidly evolving technological landscape, Small and Medium-sized Enterprises (SMEs) face significant challenges in maintaining efficient and scalable IT systems. This review outlines a strategic model for API management and cloud integration designed to enhance system performance and optimize cloud-native features. The proposed model focuses on the redesign and optimization of Application Programming Interfaces (APIs) to leverage cloud capabilities effectively, addressing common issues faced by SMEs in system integration and performance. The model begins with a comprehensive assessment of existing API architectures to identify inefficiencies and performance bottlenecks. It emphasizes the need for a cloud-native approach, which includes the adoption of microservices architecture and serverless computing. By redesigning APIs to align with cloud-native principles, the model aims to achieve greater scalability, flexibility, and responsiveness in handling varying workloads. Key components of the model include API versioning strategies, improved security protocols, and enhanced data management practices. The integration of cloud-native features such as containerization, orchestration, and automated scaling ensures that APIs are optimized for performance and reliability. This approach not only streamlines system operations but also reduces the overhead associated with traditional server-based architectures. Furthermore, the model incorporates best practices for continuous integration and continuous deployment (CI/CD), facilitating seamless updates and maintenance of APIs. It also includes monitoring and analytics tools to provide real-time insights into API performance and user interactions, enabling proactive management and optimization. By implementing this strategic model, SMEs can achieve significant improvements in system performance, reduce latency, and enhance overall cloud integration. The model supports the efficient utilization of cloud resources, leading to cost savings and improved operational efficiency. This approach positions SMEs to better compete in the digital economy by leveraging advanced technologies and optimizing their IT infrastructure.

Optimizing Cloud Resource Utilization with ANN-Based VM Placement and Prediction

Optimizing Cloud Resource Utilization with ANN-Based VM Placement and Prediction

Nonlinear Analysis of EDF Scheduling Framework for Resource Distribution in Cloud Computing

In modern times, the field of information technology is significantly shifting towards cloud computing. This innovative technology involves the permanent storage of resources on servers, which clients access via the internet. Typically, a cloud comprises a collection of resources known as virtual machines, capable of managing both computational tasks and storage needs. As the demand for cloud services continues to rise, the scheduling of these resources presents a considerable challenge. Scheduling refers to the workflow management necessary to complete tasks within the system. An efficient scheduler adapts its scheduling policy dynamically in response to changing conditions and task requirements. Cloud computing represents a recently developed technology where resources, whether physical or virtual machines, are permanently stored on servers and accessed by users through the internet. The cloud offers various service models, including Platform as a Service (PaaS), Software as a Service (SaaS), and Infrastructure as a Service (IaaS). In this context, IaaS refers to a set of resources, including virtual machines, that provide computational and storage services. Given the increasing adoption of cloud technology, resource scheduling has become a critical task. Hence, optimizing the scheduling of requests to ensure the best utilization of cloud resources is essential. This paper introduces a Nonlinear Analysis of EDF Scheduling Framework for resource allocation that considers deadlines and processing times when assigning resources to specific jobs. The performance metrics, including Average Turnaround Time, Average Waiting Time, and Average Deadline Violation, show a significant improvement when compared to traditional scheduling models such as First-Come, First-Served (FCFS), the Shortest Job First (SJF), and Simple Earliest Deadline First (EDF) models.

A Novel Method of Secured Data Distribution Using Sharding Zkp and Zero Trust Architecture in Blockchain Multi Cloud Environment

In the era of cloud computing, guaranteeing the safety and effectiveness of data management is of utmost importance. This investigation presents a novel approach that amalgamates the sharding concept, encryption, zero-knowledge proofs (zkp), and blockchain technology for secure data retrieval and data access control to improve data security, efficiency in cloud storage and migration. Further, we utilize user-specific digital wallets for secure encryption keys in order to encrypt the file before storing into the cloud. As Large files (greater than 50 MB) or Big data files (greater than 1 TB) require greater computational complexity, we leverage the sharding concept to enhance both space and time complexity in cloud storage. Hence, the large files are divided into shards and stored in different database servers. We also employ a blockchain smart contract to enhance secure retrieval of the file and also a secure access method, which ensures the privacy of the user. The zk-snark protocol is utilized to ensure the safe transfer of data between different cloud services. By utilizing this approach, data privacy is preserved, as only the proof of the data’s authenticity is shared with the verifier at the destination cloud, rather than the actual data themselves. The suggested method tackles important concerns related to data protection, privacy, and efficient resource utilization in cloud computing settings by ensuring it meets all the cloud policies required to store data. Since the environment maintains the privacy of the user data and the raw data of the user is not stored anywhere, the entire environment is set up as a Zero trust model.

SCROOGEVM: Boosting Cloud Resource Utilization With Dynamic Oversubscription

SCROOGEVM: Boosting Cloud Resource Utilization With Dynamic Oversubscription

Advancing anomaly detection in cloud environments with cutting‐edge generative AI for expert systems

AbstractAs artificial intelligence (AI) continues to advance, Generative AI emerges as a transformative force, capable of generating novel content and revolutionizing anomaly detection methodologies. This paper presents CloudGEN, a pioneering approach to anomaly detection in cloud environments by leveraging the potential of Generative Adversarial Networks (GANs) and Convolutional Neural Network (CNN). Our research focuses on developing a state‐of‐the‐art Generative AI‐based anomaly detection system, integrating GANs, deep learning techniques, and adversarial training. We explore unsupervised generative modelling, multi‐modal architectures, and transfer learning to enhance expert systems' anomaly detection systems. We illustrate our approach by dissecting anomalies regarding job performance, network behaviour, and resource utilization in cloud computing environments. The experimental results underscore a notable surge in anomaly detection accuracy with significant development of approximately 11%.

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.

Comprehensive review of load balancing in cloud computing system

Load balancing plays a critical role in optimizing resource utilization and enhancing performance in cloud computing systems. As cloud environments grow in scale and complexity, efficient load balancing mechanisms become increasingly vital. This paper presents a comprehensive review of load balancing techniques in cloud computing systems, with a focus on their applicability, advantages, and limitations. The review encompasses both static and dynamic load balancing approaches, evaluating their effectiveness in addressing the challenges posed by cloud infrastructure, such as heterogeneity, scalability, and variability in workload demands. Furthermore, the review examines load balancing algorithms considering factors such as resource utilization, response time, fault tolerance, and energy efficiency. Additionally, the impact of load balancing on cloud performance metrics, including throughput, latency, and scalability, is analyzed. This review aims to provide insights into the state-of-the-art load balancing strategies and serve as a valuable resource for researchers, practitioners, and system designers involved in the development and optimization of cloud computing systems.

Review of AI-driven Cloud Optimization

Cloud automation is the key to realization a fully-optimized performance of modern cloud platforms while cloud resources utilization. Resource allocation efficiency is valuable. We are however faced with increasing pressure for computational resources. The Long Short-Term Memory (LSTM) algorithms have found a great use case in the dynamic resource allocation problem when the problem is solved by the proactive provisioning of resources based on historical usage patterns taking advantage of recurrent neural networks. Furthermore, the concern over quality-of-service delivery (QoS) and energy efficiency is now almost as challenging for cloud providers when implementing the utilization of cloud resources, especially in a dynamic setting. Deep Reinforcement Learning (DRL) allows to pursue that end by developing agents, which might guide the work of optimizing resource allocation and reducing energy expenses at the same time. It improves the result and adaptability of the applications running on clouds. Artificial intelligence, in its diverse form, for the instance machine learning and optimization algorithms, brings in a great influence in the areas of cloud operations, resource management, and security. Furthermore, the 3rd generation of FPGAs (Reconfigurable Digital Computing-In-Memory or ReDCIM processors) and the bitwise parallelism via-memory core multipliers also improve the efficiency of computing in cloud environments. Adopting these inputting methodologies is a consequence of cloud systems achieving top performance, high scalability, and low costing. Keywords - Cloud Automation, Long Short-Term Memory (LSTM) algorithms, Deep Reinforcement Learning (DRL), Reconfigurable Digital Computing-In-Memory (ReDCIM) processors, Resource sAllocation, Energy Efficiency.

Key Challenges of Cloud Computing Resource Allocation in Small and Medium Enterprises

Although cloud computing offers many benefits, such as flexibility, scalability, and profitability, some small and medium enterprises (SMEs) are still unable to fully utilize cloud resources, such as memory, computing power, storage, and network bandwidth. This reduces their productivity and increases their expenses. Therefore, the central objective of this paper was to examine the key challenges related to the allocation of cloud computing resources in small and medium enterprises. The method used for this study is based upon qualitative research using 12 interviews with 12 owners, managers, and experts in cloud computing in four countries: the United States of America, the United Kingdom, India, and Pakistan. Our results, based on our empirical data, show 11 key barriers to resource allocation in cloud computing that are classified based on the technology, organization, and environment (TOE) framework. Theoretically, this research contributes to the body of knowledge concerning cloud computing technology and offers valuable understanding of the cloud computing resource allocation approaches employed by small and medium enterprises (SMEs). In practice, this research is useful to aid SMEs in implementing successful and sustainable strategies for allocating cloud computing resources.

Blockchain based Quantum Resistant Signature Algorithm for Data Integrity Verification in Cloud and Internet of Everything


 INTRODUCTION: The processing and storage capacities of the Internet of Everything (IoE) platform are restricted, but the cloud can readily provide efficient computing resources and scalable storage. The Internet of Everything (IoE) has expanded its capabilities recently by employing cloud resources in multiple ways. Cloud service providers (CSP) offer storage resources where extra data can be stored. These methods can be used to store user data over the CSP while maintaining data integrity and security. The secure storage of data is jeopardized by concerns like malicious system damage, even though the CSP's storage devices are highly centralized. Substantial security advancements have been made recently as a result of using blockchain technology to protect data transported to networks. In addition, the system's inclusive efficacy is enhanced, which lowers costs in comparison to earlier systems.
 OBJECTIVES: The main objective of the study is to a blockchain-based data integrity verification scheme is presented to provide greater scalability and utilization of cloud resources while preventing data from entering the cloud from being corrupted.
 METHODS: In this paper, we propose a novel method of implementing blockchain in order to enhance the security of data stores in cloud.
 RESULTS: The simulations indicate that the proposed approach is more effective in terms of data security and data integrity. Furthermore, the comparative investigation demonstrated that the purported methodology is far more effective and competent than prevailing methodologies.
 CONCLUSIONS: The model evaluations demonstrated that the proposed approach is quite effective in data security.

Performance enhancement of cloud security with migration algorithm for choosing virtual machines in cloud computing

The management of the hardware as well as software resources entrusted to third-party service suppliers have shown to be extraordinarily remarkable and accessible with Cloud Computing (CC), a potential computing technology for the future. Yet, a major undertaking is characterized by security as well as the use of Virtual Machines (VMs) to perform a request. The main issue with using technology is security worries, which raises the need for a strong security system to safeguard data on the cloud. Thus, to address this problem, a method called Weighted Mean-based Radial Basis Function Network with Advanced Encryption Standard (WMRBFN-AES) is developed to improve cloud security along with the time of execution. Random keys are produced utilizing the Pseudo Random Number Generator (PRNG) technique, and the best keys are created employing the WMRBFN technique. In the WMRBFN technique, randomly generated keys are used as input, and the hidden layers yield an estimate associated with the secret key of the input. The output layer generates the most precise secret keys to encrypt the data. Encryption is carried out using the AES algorithm. The files about the encrypted data are kept in a cloud storage system. Reduced execution times for stakeholder demands (tasks) as well as optimal cloud resource utilization result from the selection of VMs, which significantly improves reliability. The Migration Algorithm (MA) is used in the cloud framework to optimize the selection of VMs to improve resource utilization and performance. The experimental findings of the suggested framework and the state-of-the-art frameworks are compared, and it was found that the new model performed better in terms of cost, resource utilization, service latency, throughput, and execution time.

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques

Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model plays a vital role in the prediction of server load and workload provisioning. This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm (“DPSO-GA”) for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction challenge by combining the benefits of these two methods in fine-tuning the Hyperparameters. In the second phase, CNN-LSTM is utilized. Before using the CNN-LSTM approach to forecast the consumption of resources, a hybrid approach, PSO-GA, is used for training it. In the proposed framework, a one-dimensional CNN and LSTM are used to forecast the cloud resource utilization at various subsequent time steps. The LSTM module simulates temporal information that predicts the upcoming VM workload, while a CNN module extracts complicated distinguishing features gathered from VM workload statistics. The proposed model simultaneously integrates the resource utilization in a multi-resource utilization, which helps overcome the load balancing and over-provisioning issues. Comprehensive simulations are carried out utilizing the Google cluster traces benchmarks dataset to verify the efficiency of the proposed DPSO-GA technique in enhancing the distribution of resources and load balancing for the cloud. The proposed model achieves outstanding results in terms of better precision, accuracy and load allocation.

Optimizing cloud resource allocation: A long short-term memory and DForest-based load balancing approach

Load balancing is an element that must exist for a cloud server to function properly. Without it, there would be substantial lag and the server won’t be able to operate as intended. In a Cloud computing (CC) establishing, load balancing is the process of dividing workloads and computer resources. The distribution of assets from a data centre involves many different factors, including load balancing of workloads in cloud environment. To make best use each virtual machine’s (VM) capabilities, load balancing needs to be done in a way that ensures that all VMs have balanced loads. Both overloading and underloading are examples of load unbalance, and both of these types of load unbalance could be fixed by using techniques created especially for load balancing. The research that has been done on the subject have not attempted to take into account the factors that affect the problem of load unbalancing. Results indicate that the LSTM and DForest-based load balancing approach significantly improves cloud resource utilization, reduces response times, and minimizes the occurrence of overloading or underloading scenarios. To effectively design those programmes, it is essential to first understand the advantages and disadvantages of current methodologies before developing efficient AI-based load balancing programmes. Compared to existing method the proposed method is high accuracy 0.98, KNN accuracy is 0.97, SVM accuracy is 0.99, and DForest accuracy is 0.987.

Energy efficient virtual machines placement in cloud datacenters using genetic algorithm and adaptive thresholds.

Cloud computing platform provides on-demand IT services to users and advanced the technology. The purpose of virtualization is to improve the utilization of resources and reduce power consumption. Energy consumption is a major issue faced by data centers management. Virtual machine placement is an effective technique used for this purpose. Different algorithms have been proposed for virtual machine placement in cloud environments. These algorithms have considered different parameters. It is obvious that improving one parameter affects other parameters. There is still a need to reduce energy consumption in cloud data centers. Data centers need solutions that reduce energy consumption without affecting other parameters. There is a need to device solutions to effectively utilize cloud resources and reduce energy consumption. In this article, we present an algorithm for Virtual Machines (VMs) placement in cloud computing. The algorithm uses adaptive thresholding to identify over utilized and underutilized hosts to reduce energy consumption and Service Level Agreement (SLA) violations. The algorithm is validated with simulations and comparative results are presented.

SMSE: A serverless platform for multimedia cloud systems

SummaryAlong with the rise of domain‐specific computing (ASICs hardware) and domain‐specific programming languages, we envision that the next step is the emergence of domain‐specific cloud platforms. Considering multimedia streaming as one of the most trendy applications in the IT industry, the goal of this study is to develop serverless multimedia streaming engine (SMSE), the first domain‐specific serverless platform for multimedia streaming. SMSE democratizes multimedia service development via enabling content providers (or even end‐users) to rapidly develop their desired functionalities on their multimedia contents. Upon developing SMSE, the next goal of this study is to deal with its efficiency challenges and develop a function container provisioning method that can efficiently utilize cloud resources and improve the users' quality of service. In particular, we develop a dynamic method that provisions durable or ephemeral containers depending on the spatiotemporal and data‐dependency characteristics of the functions. Evaluating the prototype implementation of SMSE under real‐world settings demonstrates its capability to reduce both the containerization overhead, and the makespan time of serving multimedia processing functions (by up to 30%) in compare to the function provision methods that are being used in the general‐purpose serverless cloud systems.

Bulut Hesaplama İçin Evrimsel Algoritma Kullanarak İş Akışı Planlaması

Cloud computing provides powerful, highly scalable, flexible resources for real world applications. It also reduces the cost and operation expenses. Workflow scheduling is important for getting higher performance, reducing cost and using resources more efficiently in cloud computing. Workflow scheduling in cloud systems assigns tasks to resources available in the system and aims to utilize cloud resources by decreasing makespan of the workflow. In this study, an evolutionary algorithm is proposed to solve workflow scheduling problem. The main objective of this work is to minimize the makespan of the schedule. To achieve this goal, problem specific crossover operator and mutation operators are proposed in the evolutionary algorithm. The crossover operator will combine the problem-specific information stored in both parents to create a new individual. The mutation operators will explore neighbor solutions using some intelligent search mechanisms. This unique design of the operators increases the diversity of the search space and the quality of the solutions. As a result, the workflow schedules obtained from the evolutionary algorithm decreases the makespan of the workflow in the cloud system. The performance of the proposed study is measured using well-known scientific workflows and is compared with the algorithms from the literature. The proposed study outperforms all related algorithms in 67% of the test cases and obtains the same results in the remaining test cases.

Dynamic offloading technique for real-time edge-to-cloud computing in heterogeneous MEC–MCC and IoT devices

In the realm of Mobile Cloud Computing (MCC), where mobile environments converge with cloud capabilities to address challenges related to performance, environmental considerations, and security, the complexity of task offloading intensifies with the growing number of cloud providers and application units. While Mobile Edge Computing (MEC) shows promise in enhancing MCC performance, the central research challenge revolves around efficiently determining when to offload computational tasks to the cloud or handle them locally at the edge. It involves finding the optimal balance between utilizing cloud resources and enhancing edge computing performance. This study aims to streamline the decision-making process, seeking a harmonious balance between optimal cloud resource utilization and improved edge computing performance. To this end, we introduce HECOM (Hybrid Edge-to-Cloud Offloading for Heterogeneous Computing), a strategic model that combines three decision-making algorithms and an efficient hybrid computation offloading method. HECOM swiftly identifies optimal offloading servers for individual application units, addressing the challenge of efficient offloading decision-making. Importantly, HECOM provides optimal solutions without waiting for all training processes to conclude. Evaluation results from experiments and simulations affirm that the proposed model exhibits impressive energy efficiency, reducing consumption by 25%, increasing offloading ratio by 30%, and minimizing latency and time delay by 15%, making it ideal for real-time applications. It significantly enhances network resource allocation and achieves a more balanced overall performance profile.

Optimizing Task Scheduling in Cloud Computing Environments using Hybrid Swarm Optimization

Cloud computing has revolutionized the Information Technology (IT) landscape by offering on-demand access to a shared pool of computing resources over the internet. Effective task scheduling is pivotal in optimizing resource utilization and enhancing the overall performance of cloud systems. Tasks are allocated to virtual machines (VMs) based on a server's workload capacity, aiming to minimize traffic congestion and waiting times. Although Particle Swarm Optimization (PSO) is currently the most effective algorithm for task scheduling in cloud environments, this study introduces a Hybrid Swarm Optimization (HSO) algorithm that combines the strengths of PSO and Salp Swarm Optimization (SSO). The proposed hybrid algorithm addresses the challenges associated with task scheduling in cloud computing. The performance of the HSO algorithm is evaluated using the CloudSim simulator and compared against traditional scheduling algorithms. Simulation results indicate that the hybrid PSO-SSO algorithm outperforms existing methods regarding makespan time, cloud throughput, and task execution efficiency. Consequently, the hybrid approach significantly improves resource utilization and overall system performance in cloud computing environments.