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Journal articles on the topic 'Cloud Computing; Resource Allocation; Resource Utilization'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Abirami, S.P.1 and Shalini Ramanathan2. "Linear Scheduling Strategy for Resource Allocation in Cloud Environment." International Journal on Cloud Computing: Services and Architecture(IJCCSA) 2, February (2018): 01–09. https://doi.org/10.5281/zenodo.1444279.

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Cloud computing technology virtualizes and offers many services across the network. It mainly aims at scalability, availability, throughput, and resource utilization. Emerging techniques focus on scalability and availability. However, cloud computing must be advanced to focus on resource utilization and resource management. The cloud environment, embedded with the nimbus and cumulus services will contribute more in making the responsibility of resource utilization in Cloud Computing. Considering the processing time, resource utilization based on CPU usage, memory usage and throughput, the clou

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Rachna, Ms, Ms Namrata, and Ms Diksha. "Resource Allocation in Cloud." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (2022): 1395–99. http://dx.doi.org/10.22214/ijraset.2022.40517.

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Abstract: A cloud environment is the popular shareable computing environments where large number of clients/users are connected to the common cloud computing environment to access the resources and the services. The presented work is focused on the concept of effective resource allocation, de-allocation and reallocation in a cloud environment. To present the concept, we have taken a cloud environment with multiple clouds along with multiple virtual machines. All the machines are homogenous. These all clouds are assigned by a specific priority. Now as the user request arrive, it performs the re

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George Fernandez, I., and J. Arokia Renjith. "Resource allocation, scheduling and auto-scaling algorithms for enhancing the performance of cloud using Grey Wolf Optimization and Fuzzy rules." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 7449–67. http://dx.doi.org/10.3233/jifs-200787.

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Cloud computing technology is playing a major role in the industry and real-life, for providing fast services such as data sharing and allocating the cloud resources that are paid and truly required. In this scenario, the cloud users are scheduled according to the rule-based systems for attempting to automate the matching between computing requirements and resources. Even though, the majority auto-scaling algorithms only helped as indicators for simple resource utilization and also not considered both cloud user needs and budget concerns. For this purpose, we propose a new model which is the c

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Sheeba, Adlin, Brijendra Gupta, Malathi L, and Saravanan D. "SWARM INTELLIGENCE OPTIMIZATION FOR RESOURCE ALLOCATION IN CLOUD COMPUTING ENVIRONMENTS." ICTACT Journal on Soft Computing 13, no. 4 (2023): 3048–54. http://dx.doi.org/10.21917/ijsc.2023.0429.

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Cloud computing has emerged as a powerful paradigm for resource allocation due to its scalability and flexibility. Efficient resource allocation is critical for optimizing the performance and utilization of cloud resources. In this context, swarm intelligence optimization algorithms, such as Salp Swarm Optimization (SSO), have shown promising results in solving complex optimization problems. This paper presents a novel approach that utilizes SSO for resource allocation in cloud computing environments. The proposed approach aims to maximize resource utilization, minimize response time, and impr

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Wang, Baoming, Yuhang He, Zuwei Shui, Qi Xin, and Han Lei. "Predictive optimization of DDoS attack mitigation in distributed systems using machine learning." Applied and Computational Engineering 64, no. 1 (2024): 89–94. http://dx.doi.org/10.54254/2755-2721/64/20241350.

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In recent years, cloud computing has been widely used. This paper proposes an innovative approach to solve complex problems in cloud computing resource scheduling and management using machine learning optimization techniques. Through in-depth study of challenges such as low resource utilization and unbalanced load in the cloud environment, this study proposes a comprehensive solution, including optimization methods such as deep learning and genetic algorithm, to improve system performance and efficiency, and thus bring new breakthroughs and progress in the field of cloud computing resource man

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Gaurav, Raj1 Ankit Nischal2. "Efficient Resource Allocation in Resource provisioning policies over Resource Cloud Communication Paradigm." International Journal on Cloud Computing: Services and Architecture(IJCCSA) 2, June (2018): 01–08. https://doi.org/10.5281/zenodo.1438579.

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Optimal resource utilization for executing tasks within the cloud is one of the biggest challenges. In executing the task over a cloud, the resource provisioner is responsible for providing the resources to create virtual machines. To utilize the resources optimally, the resource provisioner has to take care of the process of allocating resources to Virtual Machine Manager (VMM). In this paper, an efficient way to utilize the resources, within the cloud, to create virtual machines has been proposed considering optimum cost based on performance factor. This performance factor depends upon the o

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Souravlas, Stavros, and Stefanos Katsavounis. "Scheduling Fair Resource Allocation Policies for Cloud Computing through Flow Control." Electronics 8, no. 11 (2019): 1348. http://dx.doi.org/10.3390/electronics8111348.

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In this short paper, we discuss the problem of resource allocation for cloud computing. The cloud provides a variety of resources for users based on their requirements. Thus, one of the main issues in cloud computing is to design an efficient resource allocation scheme. Each job generated by a user in the cloud has some resource requirements. In this work, we propose a resource allocation method which aims at maximizing the resource utilization and distributing the system’s resources in a fast and fair way, by controlling the flow according to the resources available and by analyzing the domin

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Zhou, Qiao. "Research on Optimization Algorithm of Cloud Computing Resource Allocation for Internet of Things Engineering Based on Improved Ant Colony Algorithm." Mathematical Problems in Engineering 2022 (April 26, 2022): 1–6. http://dx.doi.org/10.1155/2022/5632117.

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Considering the inability to use genetic algorithms, increased total execution time of tasks, and low user satisfaction and resource utilization based on existing algorithms, an improved ant colony algorithm optimization method for cloud computing resource allocation based on the mobile Internet of Things project is designed in order to better complete the allocation of cloud computing resources. In the mobile Internet of Things engineering environment, the tasks are classified by the characteristics of cloud computing resource allocation, and then, the justice distributive principle of the Be

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Zhou, Qiao. "Research on Optimization Algorithm of Cloud Computing Resource Allocation for Internet of Things Engineering Based on Improved Ant Colony Algorithm." Mathematical Problems in Engineering 2022 (April 26, 2022): 1–6. http://dx.doi.org/10.1155/2022/5632117.

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Considering the inability to use genetic algorithms, increased total execution time of tasks, and low user satisfaction and resource utilization based on existing algorithms, an improved ant colony algorithm optimization method for cloud computing resource allocation based on the mobile Internet of Things project is designed in order to better complete the allocation of cloud computing resources. In the mobile Internet of Things engineering environment, the tasks are classified by the characteristics of cloud computing resource allocation, and then, the justice distributive principle of the Be

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Zhou, Qiao. "Research on Optimization Algorithm of Cloud Computing Resource Allocation for Internet of Things Engineering Based on Improved Ant Colony Algorithm." Mathematical Problems in Engineering 2022 (April 26, 2022): 1–6. http://dx.doi.org/10.1155/2022/5632117.

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Considering the inability to use genetic algorithms, increased total execution time of tasks, and low user satisfaction and resource utilization based on existing algorithms, an improved ant colony algorithm optimization method for cloud computing resource allocation based on the mobile Internet of Things project is designed in order to better complete the allocation of cloud computing resources. In the mobile Internet of Things engineering environment, the tasks are classified by the characteristics of cloud computing resource allocation, and then, the justice distributive principle of the Be

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Sutar, Sandeep Gajanan, and Kumarswamy S. "Efficient Scheduling of Jobs and Allocation of Resources in Cloud Computing." International Journal of Software Innovation 10, no. 1 (2022): 1–13. http://dx.doi.org/10.4018/ijsi.307013.

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Due to the drastic utilization of clouds, a Proper and proficient allocation of resources in dynamically working environment of cloud systems turns into the challenging task. Different promising mechanisms have been created to work on the effectiveness of process of resource allocation. Yet at the same time there is some incompetency as far as resource allocation and job scheduling, when the systems become highly loaded. Hence, an effective algorithm for scheduling of jobs is needed to work on the proficiency of resource allocation activities. In this paper a advanced technique for scheduling

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Hitesh, Jodhavat, Khandelwal Nirmesh, and Mishra Gaurav. "AI-Optimized Resource Allocation in Cloud Computing: Performance Engineering Through Predictive Load Balancing." Global Journal of Engineering and Technology [GJET] 4, no. 2 (2025): 25–27. https://doi.org/10.5281/zenodo.14964741.

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<em>Cloud computing has revolutionized modern computing by providing scalable and on-demand computing resources. However, efficient resource allocation remains a critical challenge, directly affecting system performance, cost, and energy consumption. This research explores the role of AI-driven predictive load balancing in optimizing resource allocation within the scope of performance engineering and cloud engineering. By leveraging machine learning-based forecasting models, cloud infrastructure can predict workload fluctuations and dynamically allocate resources, improving efficiency, reliabi

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Torana Kamble, Et al. "Predictive Resource Allocation Strategies for Cloud Computing Environments Using Machine Learning." Journal of Electrical Systems 19, no. 2 (2024): 68–77. http://dx.doi.org/10.52783/jes.692.

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Cloud computing revolutionizes fast-changing technology. Companies' computational resource use is changing. Businesses can quickly adapt to changing market conditions and operational needs with cloud-based solutions' adaptability, scalability, and cost-efficiency. IT operations and service delivery have changed due to widespread computational resource access. Cloud computing efficiently allocates resources in cloud environments, making it crucial to this transformation. Resource allocation impacts efficiency, cost, performance, and SLAs. Users and providers can allocate cloud resources based o

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Ou, Yining. "Dynamic Allocation Mechanism of Cloud Computing Resources Driven by Neural Network." Frontiers in Computing and Intelligent Systems 6, no. 1 (2023): 11–14. http://dx.doi.org/10.54097/fcis.v6i1.03.

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With the popularization of cloud computing technology, the dynamic allocation mechanism of cloud computing resources has become an important research field to improve resource utilization and meet the needs of diversified workloads. The purpose of this study is to explore the dynamic allocation mechanism of cloud computing resources driven by neural network and introduce the powerful ability of deep learning into cloud computing environment. We put forward a comprehensive framework, which combines data collection, analysis, decision-making and implementation to realize intelligent resource all

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J, Anurag. "Review of AI-driven Cloud Optimization." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34000.

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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 effi

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Alsaffar, Aymen Abdullah, Hung Phuoc Pham, Choong-Seon Hong, Eui-Nam Huh, and Mohammad Aazam. "An Architecture of IoT Service Delegation and Resource Allocation Based on Collaboration between Fog and Cloud Computing." Mobile Information Systems 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/6123234.

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Despite the wide utilization of cloud computing (e.g., services, applications, and resources), some of the services, applications, and smart devices are not able to fully benefit from this attractive cloud computing paradigm due to the following issues: (1) smart devices might be lacking in their capacity (e.g., processing, memory, storage, battery, and resource allocation), (2) they might be lacking in their network resources, and (3) the high network latency to centralized server in cloud might not be efficient for delay-sensitive application, services, and resource allocations requests. Fog

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Chung, Wu-Chun, Tsung-Lin Wu, Yi-Hsuan Lee, Kuo-Chan Huang, Hung-Chang Hsiao, and Kuan-Chou Lai. "Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds." Applied Sciences 11, no. 1 (2020): 149. http://dx.doi.org/10.3390/app11010149.

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Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in co

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Sreenivasulu Gogula, Et al. "A Study Resource Optimization Techniques Based Job Scheduling in Cloud Computing." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1714–22. http://dx.doi.org/10.17762/ijritcc.v11i10.8746.

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Cloud computing has revolutionized the way businesses and individuals utilize computing resources. It offers on-demand access to a vast pool of virtualized resources, such as processing power, storage, and networking, through the Internet. One of the key challenges in cloud computing is efficiently scheduling jobs to maximize resource utilization and minimize costs. Job scheduling in cloud computing involves allocating tasks or jobs to available resources in an optimal manner. The objective is to minimize job completion time, maximize resource utilization, and meet various performance metrics

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Mohammad Elham Ebadi, Wang Yu, Khoshal Rahman Rahmani, and Musawer Hakimi. "Resource Allocation in The Cloud Environment with Supervised Machine learning for Effective Data Transmission." Journal of Computer Science and Technology Studies 6, no. 3 (2024): 22–34. http://dx.doi.org/10.32996/jcsts.2024.6.3.3.

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Resource allocation in the cloud environment for 5G applications can be explained by referring to the strategic distribution and necessary assignment of computing resources such as virtual machines, storage, and network bandwidth that meet the dynamic demands of applications and services. The framework proposed is on resource allocation in the cloud environment by BRoML for 5G applications. In the proposed BRoML model, the Backtracking Regularized model is incorporated for the effective calculation of the resources in the cloud environment. The optimization is performed for the effective compu

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Tahir Alyas. "A Quantum Optimization Model for Dynamic Resource Allocation in Cloud Computing." Lahore Garrison University Research Journal of Computer Science and Information Technology 1, no. 1 (2017): 44–53. http://dx.doi.org/10.54692/lgurjcsit.2017.01015.

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Quantum Computing and Cloud Computing technologieshave potential capability to change the dynamic of futurecomputing. Similarly, both Complexities, time and Space are the basicconstraints which can determine the efficient cloud service performance.Quantum optimization for the cloud resources in dynamic environmentprovides a way to deal with the present classical cloud computationmodel’s challenges. By combining the fields of quantum computing andcloud computing, will result in evolutionary technology. Virtual resourceallocation is a major challenge facing cloud computing with dynamiccharacteri

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Karamthulla, Musarath Jahan, Jesu Narkarunai, Arasu Malaiyappan, and Ravish Tillu. "Optimizing Resource Allocation in Cloud Infrastructure through AI Automation: A Comparative Study." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2, no. 2 (2023): 315–26. http://dx.doi.org/10.60087/jklst.vol2.n2.p326.

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Optimizing resource allocation in cloud infrastructure is paramount for ensuring efficient utilization of computing resources and minimizing operational costs. With the proliferation of diverse workloads and dynamic user demands, manual resource management becomes increasingly challenging. In this context, artificial intelligence (AI) automation emerges as a promising approach to enhance resource allocation efficiency. This paper presents a comparative study of various AI techniques applied to optimize resource allocation in cloud environments. We explore the efficacy of machine learning, evol

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Xu, Xin, and Huiqun Yu. "A Game Theory Approach to Fair and Efficient Resource Allocation in Cloud Computing." Mathematical Problems in Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/915878.

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On-demand resource management is a key characteristic of cloud computing. Cloud providers should support the computational resource sharing in a fair way to ensure that no user gets much better resources than others. Another goal is to improve the resource utilization by minimizing the resource fragmentation when mapping virtual machines to physical servers. The focus of this paper is the proposal of a game theoretic resources allocation algorithm that considers the fairness among users and the resources utilization for both. The experiments with an FUGA implementation on an 8-node server clus

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THUMMULURU, KAVITHA, and THATIMAKULA SUDHA Dr. "EFFICIENT RESOURCE UTILIZATION APPROACH IN CLOUD COMPUTING USING OPTIMIZED DIRECT RESOURCE PROVISIONING." Seybold Report Journal 18, no. 08 (2023): 1372–88. https://doi.org/10.5281/zenodo.8310627.

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<strong>Abstract</strong> In the cloud, users can access large pools of distributed computing resources and storage resources on-demand and anywhere there is an internet connection. In order to manage these many assets efficiently, scheduling methods need to be time-saving. In addition to optimizing efficiency and fairness, the perfect scheduler will improve quality of service (QoS). Large-scale distributed systems require scheduling methods. Now the cloud customers are charged based upon the amount of resources they are consumed or held in reserve. Usage of data in cloud provisioning for diff

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K. Sonkar, S., and M. U.Kharat. "A Novel Energy Efficient Resource Management System in Cloud Computing Environment." International Journal of Engineering & Technology 7, no. 4.19 (2018): 1030. http://dx.doi.org/10.14419/ijet.v7i4.19.28281.

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Primary target of cloud provider is to provide the maximum resource utilization and increase the revenue by reducing energy consumption and operative cost. In the service providers point of view, resource allocation, resource sharing, migration of resources on demand, memory management, storage management, load balancing, energy efficient resource usage, computational complexity handling in virtualization are some of the major tasks that has to be dealt with. The major issue focused in this paper is to reduce the energy consumption problem and management of computation capacity utilization. Fo

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Cui, Yanan. "Development and Utilization of College Chinese Curriculum Resources Based on Cloud Computing Resource Scheduling Algorithm." Mathematical Problems in Engineering 2022 (March 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/3648678.

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In the current situation of university education in China, there are some problems that need to be solved urgently in college Chinese teaching in China, which seriously restrict the realization and implementation of college Chinese teaching objectives. The emergence and development of cloud computing technology can integrate different information media, and through the integration of traditional text, pictures, videos, and other resource media, it can be presented in a clearer and more vivid way. In this paper, a cloud computing resource scheduling model is established for the realization of t

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Vipin Kumar Jaiswal. "Leveraging Fog Computing for Virtual Machine Placement in the Cloud Computing Environment." Journal of Electrical Systems 20, no. 3 (2024): 6342–53. http://dx.doi.org/10.52783/jes.6793.

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In the era of cloud computing, the allocation of virtual machines to physical machines within a data center is a critical issue that involves the decision-making process of determining the optimal strategy. It involves optimizing resource usage of CPU and RAM, reducing latency, reducing energy consumption, and ensuring the completion of tasks. Fog computing addresses these challenges by extending processing and storage resources to the edge of the network. Similar to the cloud, it may employ virtual machines for efficient resource utilization. In this paper, the capability of fog computing is

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Kenga, Derdus, Vincent Omwenga, and Patrick Ogao. "Virtual Machine Customization Using Resource Using Prediction for Efficient Utilization of Resources in IaaS Public Clouds." Journal of Information Technology and Computer Science 6, no. 2 (2021): 170–82. http://dx.doi.org/10.25126/jitecs.202162196.

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The main cause of energy wastage in cloud data centres is the low level of server utilization. Low server utilization is a consequence of allocating more resources than required for running applications. For instance, in Infrastructure as a Service (IaaS) public clouds, cloud service providers (CSPs) deliver computing resources in the form of virtual machines (VMs) templates, which the cloud users have to choose from. More often, inexperienced cloud users tend to choose bigger VMs than their application requirements. To address the problem of inefficient resources utilization, the existing app

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Bettahalli Kengegowda, Dhanalakshmi, Srikantaiah Kamidoddi Chowdaiah, Gururaj Harinahalli Lokesh, and Francesco Flammini. "Classification and Merging Techniques to Reduce Brokerage Using Multi-Objective Optimization." Algorithms 15, no. 2 (2022): 70. http://dx.doi.org/10.3390/a15020070.

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Cloud computing is concerned with effective resource utilization and cost optimization. In the existing system, the cost of resources is much higher. To overcome this problem, a new model called Classification and Merging Techniques for Reducing Brokerage Cost (CMRBC) is designed for effective resource utilization and cost optimization in the cloud. CMRBC has two benefits. Firstly, this is a cost-effective solution to service providers and customers. Secondly, for every job, virtual machine (VM) creations are avoided to reduce brokerage. The allocation, creation or selection of resources of VM

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Dr. Atul Nandwal, Mr. Ritesh Jain, Dr. Preeti Nandwal. "Dynamic Load Balancing for Improved Resource Allocation in Cloud Environments." Tuijin Jishu/Journal of Propulsion Technology 44, no. 3 (2023): 969–76. http://dx.doi.org/10.52783/tjjpt.v44.i3.408.

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Cloud computing has emerged as a revolutionary paradigm in information technology, offering scalable and on-demand access to computing resources. Efficient resource allocation is a crucial aspect in ensuring optimal performance and cost-effectiveness of cloud environments. Load balancing algorithms play a vital role in distributing workloads across available resources, preventing resource overutilization and underutilization. This research focuses on optimizing resource allocation in cloud computing through the application of advanced load balancing algorithms. The primary objective is to enha

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single, Neeraj. "Efficient Task Allocation Based on Green Computing in Private Cloud." CGC International Journal of Contemporary Technology and Research 2, no. 1 (2019): 68–76. http://dx.doi.org/10.46860/cgcijctr.2019.12.20.68.

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Cloud computing is a rapidly emerging new paradigm for delivering computing as a service. There are many research issues in cloud computing. Resource allocation is one of the challenging tasks in cloud environment. The main aim of resource allocation to reduce the infrastructure cost associated with companies. The resources offered in the cloud are probably heterogeneous and extremely dynamic. Due to this dynamic access, load balancing of jobs required. Cloud computing resource allocation should be elastic and intelligent, based on application demand and user requirements [1].Green cloud compu

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Hussain, Altaf, Muhammad Aleem, Atiq Ur Rehman, and Umer Arshad. "DE-RALBA: dynamic enhanced resource aware load balancing algorithm for cloud computing." PeerJ Computer Science 11 (March 18, 2025): e2739. https://doi.org/10.7717/peerj-cs.2739.

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Cloud computing provides an opportunity to gain access to the large-scale and high-speed resources without establishing your own computing infrastructure for executing the high-performance computing (HPC) applications. Cloud has the computing resources (i.e., computation power, storage, operating system, network, and database etc.) as a public utility and provides services to the end users on a pay-as-you-go model. From past several years, the efficient utilization of resources on a compute cloud has become a prime interest for the scientific community. One of the key reasons behind inefficien

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Moh'd, Z. Freaj, and Sleit Azzam. "Hybrid Approach for Resource Provisioning in Cloud Computing." Journal of Information Sciences and Computing Technologies 6, no. 1 (2016): 546–61. https://doi.org/10.5281/zenodo.3968238.

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Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Elasticity of resources is considered as a key characteristic of cloud computing using this key characteristic; internet services are allocated the onlyneeded resources. This allocation of resources however should not be at the expense of the services’ performance. Allocation of resources without degrading performance is called resource prov

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Agbaje, M. O., O. B. Ohwo, T. G. Ayanwola, and Ogunyolu Olufunmilola. "A Survey of Game-Theoretic Approach for Resource Management in Cloud Computing." Journal of Computer Networks and Communications 2022 (April 11, 2022): 1–13. http://dx.doi.org/10.1155/2022/9323818.

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Cloud computing is a groundbreaking technique that provides a whole lot of facilities such as storage, memory, and CPU as well as facilities such as servers and web service. It allows businesses and individuals to subcontract their computing needs as well as trust a network provider with its data warehousing and processing. The fact remains that cloud computing is a resource-finite domain where cloud users contend for available resources to carry out desired tasks. Resource management (RM) is a process that deals with the procurement and release of resources. The management of cloud resources

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Chahal, Harvinder, Anshu Bhasin, and Parag Ravikant Kaveri. "QoS Based Efficient Resource Allocation and Scheduling in Cloud Computing." International Journal of Technology and Human Interaction 15, no. 4 (2019): 13–29. http://dx.doi.org/10.4018/ijthi.2019100102.

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The Cloud environment is a large pool of virtually available resources that perform thousands of computational operations in real time for resource provisioning. Allocation and scheduling are two major pillars of said provisioning with quality of service (QoS). This involves complex modules such as: identification of task requirement, availability of resource, allocation decision, and scheduling operation. In the present scenario, it is intricate to manage cloud resources, as Service provider aims to provide resources to users on productive cost and time. In proposed research article, an optim

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Shamshirband, Shahab, Javad Hassannataj Joloudari, Sahar Khanjani Shirkharkolaie, et al. "Game theory and evolutionary optimization approaches applied to resource allocation problems in computing environments: A survey." Mathematical Biosciences and Engineering 18, no. 6 (2021): 9190–232. http://dx.doi.org/10.3934/mbe.2021453.

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<abstract> <p>Today's intelligent computing environments, including the Internet of Things (IoT), Cloud Computing (CC), Fog Computing (FC), and Edge Computing (EC), allow many organizations worldwide to optimize their resource allocation regarding the quality of service and energy consumption. Due to the acute conditions of utilizing resources by users and the real-time nature of the data, a comprehensive and integrated computing environment has not yet provided a robust and reliable capability for proper resource allocation. Although traditional resource allocation approaches in a

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Arwa Mohamed, Mosab Hamdan, Ahmed Abdelazizb, and Sharief F. Babiker. "Dynamic Resource Allocation in Cloud Computing Based on Software-Defined Networking Framework." Open Journal of Science and Technology 3, no. 3 (2020): 304–13. http://dx.doi.org/10.31580/ojst.v3i3.1668.

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cloud computing has become more powerful with the inclusion of software-defined networking (SDN) in its environment. In Cloud Data Centers (CDCs), an important research issue is how to forecast and allocate resources efficiently whilst achieving Quality of Service (QoS) of users request with minimal overall power consumption; taking into account the frequent changes in resource requirements. In this paper, we propose a Supervisor Controller-based Software-Defined Cloud Data Center (SC-boSD-CDC) framework for dynamic resource allocation and prediction of cloud computing-based SDN. In this propo

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Kaur, Amardeep, and Amandeep Verma. "Adaptive Access Control Mechanism (AACM) for Enterprise Cloud Computing." Journal of Electrical and Computer Engineering 2023 (July 13, 2023): 1–30. http://dx.doi.org/10.1155/2023/3922393.

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Enterprise cloud computing provides various services to enterprises, but access to these services is controlled by a firewall. The firewall determines the actions and operations a legitimate user can perform on the available resources. Access control policies allow or restrict access to resources, and they also keep a record of attempted access. In the role-based access control model, access to resources is based on a user’s role in the enterprise. As resources are limited, the policy manager has to create policies that optimize resource availability to different roles to improve overall resou

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Suresh, Salini, and L. Manjunatha Rao. "CCCORE: Cloud Container for Collaborative Research." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 3 (2018): 1659. http://dx.doi.org/10.11591/ijece.v8i3.pp1659-1670.

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Cloud-based research collaboration platforms render scalable, secure and inventive environments that enabled academic and scientific researchers to share research data, applications and provide access to high- performance computing resources. Dynamic allocation of resources according to the unpredictable needs of applications used by researchers is a key challenge in collaborative research environments. We propose the design of Cloud Container based Collaborative Research (CCCORE) framework to address dynamic resource provisioning according to the variable workload of compute and data-intensiv

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Nair, Rohith Sunilkumar. "Dynamic Resource Allocation in Cloud Environments." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 417–22. http://dx.doi.org/10.22214/ijraset.2023.53668.

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Abstract: The provisioning and use of computing resources has been completely transformed by cloud computing, which provides previously unheard-of levels of scalability, flexibility, and cost-effectiveness. In order to maximize resource utilizatio and guarantee peak performance in cloud settings, dynamic resource allocation is essential. In order to provide a thorough grasp of the most recent developments in this subject, this survey paper offers an in-depth review of dynamic resource allocation methods in cloud computing. The survey includes a wide range of dynamic resource allocation techniq

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Aboubakar, Moussa, Yasmine Titouche, Mickael Fernandes, Ado Adamou Abba Ari, and Md Siddiqur Rahman. "CNN-LSTM is all you Need for Efficient Resource Allocation in Cloud Computing." International Journal of Engineering Research in Africa 71 (September 18, 2024): 141–62. http://dx.doi.org/10.4028/p-o4crn9.

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Many organizations have embraced cloud computing in recent years to provide new services, easily expand their IT resources, and reduce the cost of their IT infrastructure. This has been made possible through the implementation of resource allocation strategies by cloud service providers. One of the major challenges during resource allocation is to minimize power consumption while ensuring the required Service Level Agreement (SLA). To solve this problem, a new approach to efficiently allocate resources in cloud computing while optimizing energy consumption and guaranteeing the required service

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Mohana., R. S. *. Thangaraj. P. Kalaiselvi. S. Krishnakumar. B. "ENHANCED MULTI OBJECTIVE TASK SCHEDULING FOR CLOUD ENVIRONMENT USING TASK GROUPING." Global Journal of Engineering Science and Research Management 3, no. 10 (2016): 77–82. https://doi.org/10.5281/zenodo.162154.

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In recent years, the information communication technology (ICT) appeared new paradigm of utility computing called cloud computing. The consumer cloud is always important of high performance for cloud computing service and satisfy service agree level (SLA). In cloud computing, there is a need of further improvement in task scheduling algorithm to group of tasks, which will reduce the response time and enhance computing resource utilization. This grouping strategy considers the processing capacity, memory size and service type requirement of each task to realize the optimization for cloud comput

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Judi, J. Antony, F. Ezhil Mary Arasi, and Dr S. Govindarajan. "Utility Based Resource Allocation Model for Cloud Services." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 6, no. 3 (2013): 855–60. http://dx.doi.org/10.24297/ijmit.v6i3.723.

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Minimizing Resource allocation problems under the demand and price uncertainty in cloud computing environments is the motivation to explore a resource provisioning strategy for cloud consumers. In this paper a utilization-based optimal cloud (UBOC) algorithm is proposed to minimize the total cost for provisioning resources in a certain time period. To make an optimal decision, the demand uncertainty from cloud consumer side and price uncertainty from cloud providers are taken into account to adjust the tradeoff between on-demand and oversubscribed costs. Using this UBOC user can share cloud re

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Albert, Pravin, and Manikandan Nanjappan. "An Efficient Kernel FCM and Artificial Fish Swarm Optimization-Based Optimal Resource Allocation in Cloud." Journal of Circuits, Systems and Computers 29, no. 16 (2020): 2050253. http://dx.doi.org/10.1142/s0218126620502539.

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Cloud computing model allows service-oriented system that fulfills the needs of the consumers. Capable resource management and task allocation are the important issues in cloud computing. Performance of the task scheduling method directly interrupts the utilization of cloud computing resources and the quality of experience of users. For that reason, reasonable virtual machine (VM) allocation and task scheduling are extremely important. In this paper, an efficient resource allocation model is proposed. Initially, the virtual machines are clustered with the help of Kernel Fuzzy [Formula: see tex

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Sekwatlakwatla, Sello Prince, and Vusumuzi Malele. "Data Analytics Techniques for Addressing Cloud Computing Resources Allocation Challenges: A Bibliometric Analysis Approach." Journal of Information Systems and Informatics 6, no. 1 (2024): 47–56. http://dx.doi.org/10.51519/journalisi.v6i1.640.

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The increase in the use of digital technology led to an increase in online activities. In this regard, many organizations adopted cloud computing systems to manage this online traffic. It is plan of every cloud computing resource provider to manage their system effectively and efficiently. This paper uses bibliometric analysis technique to look at the prevalence of utilization of data analytics techniques in addressing cloud computing resource allocation challenges. In this regard, the following research databases the Association for Computing Machinery, the Institute of Electrical and Electro

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Pillareddy, Vamsheedhar Reddy, and Ganesh Reddy Karri. "MONWS: Multi-Objective Normalization Workflow Scheduling for Cloud Computing." Applied Sciences 13, no. 2 (2023): 1101. http://dx.doi.org/10.3390/app13021101.

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Cloud computing is a prominent approach for complex scientific and business workflow applications in the pay-as-you-go model. Workflow scheduling poses a challenge in cloud computing due to its widespread applications in physics, astronomy, bioinformatics, and healthcare, etc. Resource allocation for workflow scheduling is problematic due to the computationally intensive nature of the workflow, the interdependence of tasks, and the heterogeneity of cloud resources. During resource allocation, the time and cost of execution are significant issues in the cloud-computing environment, which can po

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Ahmed, Kosrat Dlshad, and Subhi R. M. Zeebaree. "Resource Allocation in Fog Computing: A Review." International Journal of Science and Business 5, no. 2 (2021): 54–63. https://doi.org/10.5281/zenodo.4461876.

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Coupled with resource retrained system and devices, Internet of things network requires effective utilization of Fog Computing system. With increasing demand and requirements for enhancing the performance, fog computing scenario requires increased output, less latency, greater performance etc. This review takes the concept of fog computing one step further by reviewing available literature on the components of fog computing to determine the effective outcomes for different settings and management of the network consideration. Contributing to the development appropriate settings and management

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Thapliyal, Nitin, and Priti Dimri. "Load Balancing in Cloud Computing Based on Honey Bee Foraging Behavior and Load Balance Min-Min Scheduling Algorithm." International Journal of Electrical and Electronics Research 10, no. 1 (2022): 1–6. http://dx.doi.org/10.37391/ijeer.100101.

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Cloud computing relies on the collection and distribution of services from internet-based data centers. With the large resource pool available in internet wide range of users are accessing the cloud. Load balance is important feature involving resource allocation to prevent overloading of any system or optimal use of resources. Major load in cloud network are concerned with CPU, memory and network. This cloud computing aspect has not yet earned too much coverage. Although load balancing is an important feature for cloud computing, concurrent computing etc. In these areas, several algorithms we

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V. Baby Deepa and R. Jeya. "Dynamic resource allocation with otpimization techniques for qos in cloud computing." Scientific Temper 15, spl-1 (2024): 45–55. https://doi.org/10.58414/scientifictemper.2024.15.spl.06.

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Ensuring the quality of service (QoS) in cloud computing environments requires efficient resource allocation mechanisms to manage dynamic workloads and meet user demands. This paper proposes a dynamic resource allocation strategy that integrates gravitational search optimization (GSO) with Harris Hawks optimization (HHO) to optimize resource utilization and maintain QoS in cloud infrastructures. The proposed hybrid approach combines the global search capabilities of GSO, inspired by the law of gravity, with the exploitation and exploration strategies of HHO, mimicking the cooperative hunting b

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Shubhangi Kharche. "An Adaptive Deep Reinforcement Learning Framework for Optimizing Dynamic Resource Allocation in Federated Cloud Computing Environments." Journal of Information Systems Engineering and Management 10, no. 38s (2025): 942–57. https://doi.org/10.52783/jisem.v10i38s.7009.

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ederated cloud computing requires dynamic resource allocation which becomes challenging because resources vary in nature and workloads differ by demand and decision-making needs to operate in real time. The proposed research develops an adaptive deep reinforcement learning (DRL) platform for optimizing resource distribution in such complex infrastructure. The framework implements DRL to handle automatic cloud resource distribution across federated clouds for creating efficient operations with reduced delays and better scalability. Planned adaptive learning methods within the proposed system en

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Pujiyanta, Ardi, Lukito Edi Nugroho, and Widyawan Widyawan. "Resource allocation model for grid computing environment." International Journal of Advances in Intelligent Informatics 6, no. 2 (2020): 185. http://dx.doi.org/10.26555/ijain.v6i2.496.

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Grid computing is a collection of heterogeneous resources that is highly dynamic and unpredictable. It is typically used for solving scientific or technical problems that require a large number of computer processing cycles or access to substantial amounts of data. Various resource allocation strategies have been used to make resource use more productive, with subsequent distributed environmental performance increases. The user sends a job by providing a predetermined time limit for running that job. Then, the scheduler gives priority to work according to the request and scheduling policy and

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