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Journal articles on the topic 'Resource allocation'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Edavalath, Sheena, and Manikandasaran S. Sundaram. "Cost-based resource allocation method for efficient allocation of resources in a heterogeneous cloud environment." Scientific Temper 14, no. 04 (2023): 1339–44. http://dx.doi.org/10.58414/scientifictemper.2023.14.4.41.

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Cloud computing is appealing due to features like adaptability, portability, utility service and on-demand service. Cloud resource providers are a source of computing, and each provider delivers different types of resources. In an active cloud environment, timely resource allocation is more important. In order to increase the effectiveness and user-friendliness of resource allocation in the heterogeneous cloud, the paper suggests an efficient cost-based resource allocation (ECRA) method and framework. In the heterogeneous cloud, there is no centralized resource allocation manager (CRAM) to get

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Zheng, Junjun. "Optimization of Resource Allocation of University Innovation and Entrepreneurship Education Based on Collaborative Filtering Algorithm." Journal of Electrical Systems 20, no. 3s (2024): 1853–62. http://dx.doi.org/10.52783/jes.1724.

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Entrepreneurship education resource allocation involves the strategic distribution of resources to support programs and initiatives aimed at fostering entrepreneurial skills and mindset among students. These resources can include funding, faculty support, curriculum development, mentorship opportunities, and access to networks and facilities. Effective resource allocation ensures that entrepreneurship education programs are adequately equipped to provide students with the knowledge, skills, and support needed to succeed as entrepreneurs. By prioritizing resource allocation to areas such as exp

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1Wang, Xiaojing *2 Tong Wei 1Ren Jia 1Ding Linjie 2. Liu Jingning. "WEIGHTED FAIRNESS RESOURCE ALLOCATION OF DISKS IN XEN." International Journal on Cloud Computing: Services and Architecture(IJCCSA) 2, June (2018): 01–10. https://doi.org/10.5281/zenodo.1438577.

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Virtualization improves the utilization ratio of resource, saving considerable amount of equipment, energy, management, etc. However, the benefits of virtualization are at the penalty of fairness but inefficient resource sharing. Various tasks in virtual domains often have different demands of resources. Therefore, allocation of resource and scheduling function are particularly important. A weighted max-min fairness allocation of disk resources is proposed in virtual platform. We focus on properties of sharing incentive, initial irrelevant and Pareto efficiency while designing the allocation a

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Edavalath, Sheena, and Manikandasaran S. Sundaram. "MARCR: Method of allocating resources based on cost of the resources in a heterogeneous cloud environment." Scientific Temper 14, no. 03 (2023): 576–81. http://dx.doi.org/10.58414/scientifictemper.2023.14.3.03.

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The cloud is an intelligent technology that provides requested services to users. It offers unlimited services for the users. Many small and medium-scale industries are startup their businesses to the next level using cloud computing. The services have been provided to the users by allocating the requested resources. Allocating resources without waste and with the finest allocation is a critical task in the cloud. This paper proposes a method for allocating resources using the cost of the resource. Resource allocation follows a priority system when allocating resources. The proposed method giv

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M, Sumathi, Niranjana B, Akshaya C, Ajitha M, and Bhavadharanee M. "Round Robin Based Efficient Resource Allocation and Utilization in an Organization." International Research Journal of Multidisciplinary Technovation 2, no. 2 (2020): 16–22. http://dx.doi.org/10.34256/irjmt2023.

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In an organization, resource allocation to a request is a complex task. Traditionally, resource allocation is done through manually with high time consumption. Similarly, collision is occurring for allocating a single resource to multiple requests. Thus, leads to complex problems and slow-down the working process. The existing resource allocation technique, allocate resources continuously to a specific request and omit another request. This kind of allocation technique also leads to lots of critical issues. That is the non-allocated process never gets a resource. To overcome these issues, the

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Sathish, Kuppani, and A. Rama Mohan Reddy. "Resource Allocation Mechanism with New Models for Grid Environment." International Journal of Grid and High Performance Computing 5, no. 2 (2013): 1–26. http://dx.doi.org/10.4018/jghpc.2013040101.

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Resource allocation is playing a vital role in grid environment because of the dynamic and heterogeneous nature of grid resources. Literature offers numerous studies and techniques to solve the grid resource allocation problem. Some of the drawbacks occur during grid resource allocation are low utilization, less economic reliability and increased waiting time of the jobs. These problems were occurred because of the inconsiderable level in the code of allocating right resources to right jobs, poor economic model and lack of provision to minimize the waiting time of jobs to get their resources.

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Artiushenko, Bohdan. "A Nucleolus-Based Approach for Cloud Resource Allocation." NaUKMA Research Papers. Computer Science 7 (May 12, 2025): 25–30. https://doi.org/10.18523/2617-3808.2024.7.25-30.

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Cloud computing has transformed organizational operations by enabling flexible resource allocation and reducing upfront hardware investments. However, the growing complexity of resource management, particularly for computing instances, has led to challenges in cost control and resource allocation. Fair allocation policies, such as max-min fairness and Dominant Resource Fairness, aim to distribute resources fairly among users. In recent years, the FinOps framework has emerged to address cloud cost management, empowering teams to manage their own resource usage and budgets. The allocation of res

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Lei, Xiaoli. "Resource Sharing Algorithm of Ideological and Political Course Based on Random Forest." Mathematical Problems in Engineering 2022 (May 21, 2022): 1–8. http://dx.doi.org/10.1155/2022/8765166.

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Three aspects of the system’s online resource distribution and application are built around subject, object, and intermediary resources. The invention relates to a method for allocating resources based on the random forest algorithm. The resource allocation process entails the following steps: constructing a mathematical model of the resource allocation process, defining a mathematical model of the resource allocation process for the target object, and designing the cost function. The training data set for random forest is constructed using the classification concept. It is based on the mathem

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Othmen, Salwa, Wahida Mansouri, and Radhia Khdhir. "Applying Artificial Intelligence Techniques For Resource Management in the Internet of Things (IoT)." International journal of electrical and computer engineering systems 16, no. 2 (2025): 183–94. https://doi.org/10.32985/ijeces.16.2.1.

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Internet of Things (IoT) applications in smart cities (SCs) rely on free-flow services streamlined by artificial intelligence (AI) paradigms. However, the nature of resource constraint prevails due to external infrastructure costs and energy-based allocations. Existing approaches to smart city resource distribution rely on static thresholds or reactive responses, which are not always sufficient. These approaches may limit system performance and scalability in dynamic IoT environments owing to increased energy consumption, postponed resource allocation, and frequent device failures. This articl

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Willmott, Yvonne. "Resource allocation." Nursing Standard 4, no. 28 (1990): 46. http://dx.doi.org/10.7748/ns.4.28.46.s51.

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Milner, Philip. "RESOURCE ALLOCATION." Lancet 332, no. 8612 (1988): 686–87. http://dx.doi.org/10.1016/s0140-6736(88)90501-6.

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Saunders, Fenella. "Resource Allocation." American Scientist 107, no. 2 (2019): 66. http://dx.doi.org/10.1511/2019.107.2.66.

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Kingston-Smith, Alison. "Resource allocation." Trends in Plant Science 6, no. 2 (2001): 48–49. http://dx.doi.org/10.1016/s1360-1385(00)01842-2.

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Williams, E. S., C. M. Scott, and R. Brazil. "Resource allocation." BMJ 306, no. 6889 (1993): 1415. http://dx.doi.org/10.1136/bmj.306.6889.1415.

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Holloway, Frank. "Resource allocation." Psychiatry 6, no. 2 (2007): 72–75. http://dx.doi.org/10.1016/j.mppsy.2006.11.003.

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Ewert, Alan, and Steve Hollenhorst. "Resource Allocation." Journal of Physical Education, Recreation & Dance 61, no. 8 (1990): 32–36. http://dx.doi.org/10.1080/07303084.1990.10604598.

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McConnell, Paul, and Sharon Einav. "Resource allocation." Current Opinion in Anaesthesiology 36, no. 2 (2023): 246–51. http://dx.doi.org/10.1097/aco.0000000000001254.

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18

Chen, Zheng, Zhaoquan Gu, and Yuexuan Wang. "Incentives against Max-Min Fairness in a Centralized Resource System." Wireless Communications and Mobile Computing 2021 (October 18, 2021): 1–13. http://dx.doi.org/10.1155/2021/5570104.

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Resource allocating mechanisms draw much attention from various areas, and exploring the truthfulness of these mechanisms is a very hot topic. In this paper, we focus on the max-min fair allocation in a centralized resource system and explore whether the allocation is truthful when a node behaves strategically. The max-min fair allocation enables nodes receive appropriate resources, and we introduce an efficient algorithm to find out the allocation. To explore whether the allocation is truthful, we analyze how the allocation varies when a new node is added to the system, and we discuss whether

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HamaAli, Kurdistan Wns, and Subhi R. M. Zeebaree. "Resources Allocation for Distributed Systems: A Review." International Journal of Science and Business 5, no. 2 (2021): 76–88. https://doi.org/10.5281/zenodo.4462088.

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Resource allocation in a distributed system is the process of allocating the workload across multiple resources to optimize the required performance criteria. Different techniques are used to manage resource allocation in such distributed systems. The resource allocation for distributed systems such as cloud computing, cellular network, Software-Defined Networking (SDN), Radar Imaging and 5G Networks are used. In this paper many resource allocation algorithms in different environment and area that mentioned before are reviewed, compared and summarized. for instance, Cloud-based computation alg

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Kenny, Danelle, Kim-Huong Nguyen, Zachary Breig, Lana Friesen, and Tracy Comans. "Decisions, Decisions: Observations of Resource Allocation Under Consumer-Directed Care." Healthcare 13, no. 5 (2025): 516. https://doi.org/10.3390/healthcare13050516.

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Introduction: Resource trade-offs are a universal feature of decision-making in healthcare. Public funding for home care is an example of a complex resource allocation decision, requiring balance between the needs of the individual and the capacity of the welfare system to meet those needs across the population. Under consumer-directed care policies, responsibility for resource allocation decisions rests with the care recipient, but there is no existing measure of allocative efficiency resulting from these consumer-led decisions. Our research considers resource allocation decisions by home car

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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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Yu, Zhipeng, Fangqing Gu, Hailin Liu, and Yutao Lai. "5G Multi-Slices Bi-Level Resource Allocation by Reinforcement Learning." Mathematics 11, no. 3 (2023): 760. http://dx.doi.org/10.3390/math11030760.

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As the centralized unit (CU)—distributed unit (DU) separation in the fifth generation mobile network (5G), the multi-slice and multi-scenario, can be better applied in wireless communication. The development of the 5G network to vertical industries makes its resource allocation also have an obvious hierarchical structure. In this paper, we propose a bi-level resource allocation model. The up-level objective in this model refers to the profit of the 5G operator through the base station allocating resources to slices. The lower-level objective in this model refers to the slices allocating the re

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Wang, Yanyan, and Baiqing Sun. "A Multiobjective Allocation Model for Emergency Resources That Balance Efficiency and Fairness." Mathematical Problems in Engineering 2018 (October 14, 2018): 1–8. http://dx.doi.org/10.1155/2018/7943498.

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Efficiency and fairness are two important goals of disaster rescue. However, the existing models usually unilaterally consider the efficiency or fairness of resource allocation. Based on this, a multiobjective emergency resource allocation model that can balance efficiency and fairness is proposed. The object of the proposed model is to minimize the total allocating costs of resources and the total losses caused by insufficient resources. Then the particle swarm optimization is applied to solve the model. Finally, a computational example is conducted based on the emergency relief resource allo

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Manzoor, Muhammad Faraz, Adnan Abid, Muhammad Shoaib Farooq, Naeem A. Azam, and Uzma Farooq. "Resource Allocation Techniques in Cloud Computing: A Review and Future Directions." Elektronika ir Elektrotechnika 26, no. 6 (2020): 40–51. http://dx.doi.org/10.5755/j01.eie.26.6.25865.

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Cloud computing has become a very important computing model to process data and execute computationally concentrated applications in pay-per-use method. Resource allocation is a process in which the resources are allocated to consumers by cloud providers based on their flexible requirements. As the data is expanding every day, allocating resources efficiently according to the consumer demand has also become very important, keeping Service Level Agreement (SLA) between service providers and consumers in prospect. This task of resource allocation becomes more challenging due to finite available

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Dolgov, D. A., and E. H. Durfee. "Resource Allocation Among Agents with MDP-Induced Preferences." Journal of Artificial Intelligence Research 27 (December 26, 2006): 505–49. http://dx.doi.org/10.1613/jair.2102.

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Allocating scarce resources among agents to maximize global utility is, in general, computationally challenging. We focus on problems where resources enable agents to execute actions in stochastic environments, modeled as Markov decision processes (MDPs), such that the value of a resource bundle is defined as the expected value of the optimal MDP policy realizable given these resources. We present an algorithm that simultaneously solves the resource-allocation and the policy-optimization problems. This allows us to avoid explicitly representing utilities over exponentially many resource bundle

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Mrs., K.S.Saraswathi Devi. "Federated Learning-Based Resource Allocation for Cloud-Edge Computing." IJAPR Journal UGC Indexed & Care Listed 7, no. 1 (2022): 120–25. https://doi.org/10.5281/zenodo.8311909.

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<strong>Cloud-edge computing is a promising paradigm that can address the challenges of latency, bandwidth, and privacy in cloud computing. However, the edge nodes have limited resources, so it is important to allocate resources efficiently. This paper proposes a federated learning-based resource allocation framework for cloud-edge computing. The proposed framework consists of three main components: a federated learning algorithm, a resource allocation algorithm, and a secure communication protocol. The federated learning algorithm is responsible for training a machine learning model without s

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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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Lindlbauer, Niklas Martin, Tim Folta, Constance E. Helfat, et al. "Resource Allocation and Resource Redeployment." Academy of Management Proceedings 2020, no. 1 (2020): 13886. http://dx.doi.org/10.5465/ambpp.2020.13886symposium.

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Yingjie, Xu. "Application of BP Neural Network to Optimize the Allocation of Art Teaching Resources." Tobacco Regulatory Science 7, no. 5 (2021): 4122–32. http://dx.doi.org/10.18001/trs.7.5.1.188.

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Reasonable allocation of art teaching resources can improve the management efficiency of art teaching resources. There is a large delay in the allocation of art teaching resources, which leads to the long occupation time of network resource allocation channel. The traditional method of network experiment resource allocation is to assign resource tasks for different channels to complete the resource allocation. When the network resource allocation channel occupies a long time, the allocation efficiency is reduced. This paper proposes an optimal allocation method of art teaching resources based

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Shakil, Kashish Ara, Mansaf Alam, and Samiya Khan. "A latency-aware max-min algorithm for resource allocation in cloud." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 671. http://dx.doi.org/10.11591/ijece.v11i1.pp671-685.

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Cloud computing is an emerging distributed computing paradigm. However, it requires certain initiatives that need to be tailored for the cloud environment such as the provision of an on-the-fly mechanism for providing resource availability based on the rapidly changing demands of the customers. Although, resource allocation is an important problem and has been widely studied, there are certain criteria that need to be considered. These criteria include meeting user’s quality of service (QoS) requirements. High QoS can be guaranteed only if resources are allocated in an optimal manner. This pap

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Kashish, Ara Shakil, Alam Mansaf, and Khan Samiya. "A latency-aware max-min algorithm for resource allocation in cloud." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 671–85. https://doi.org/10.11591/ijece.v11i1.pp671-685.

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Cloud computing is an emerging distributed computing paradigm. However, it requires certain initiatives that need to be tailored for the cloud environment such as the provision of an on-the-fly mechanism for providing resource availability based on the rapidly changing demands of the customers. Although, resource allocation is an important problem and has been widely studied, there are certain criteria that need to be considered. These criteria include meeting user’s quality of service (QoS) requirements. High QoS can be guaranteed only if resources are allocated in an optimal manner. Th

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Wang, Yi-Chun, Si-Han Wang, and Ji-Bo Wang. "Resource Allocation Scheduling with Position-Dependent Weights and Generalized Earliness–Tardiness Cost." Mathematics 11, no. 1 (2023): 222. http://dx.doi.org/10.3390/math11010222.

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Under just-in-time production, this paper studies a single machine common due-window (denoted by CONW) assignment scheduling problem with position-dependent weights and resource allocations. A job’s actual processing time can be determined by the resource assigned to the job. A resource allocation model is divided into linear and convex resource allocations. Under the linear and convex resource allocation models, our goal is to find an optimal due-window location, job sequence and resource allocation. We prove that the weighted sum of scheduling cost (including general earliness–tardiness pena

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Mondal, Sakib A. "Resource allocation problem under single resource assignment." RAIRO - Operations Research 52, no. 2 (2018): 371–82. http://dx.doi.org/10.1051/ro/2017035.

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We consider a NP-hard resource allocation problem of allocating a set of resources to meet demands over a time period at the minimum cost. Each resource has a start time, finish time, availability and cost. The objective of the problem is to assign resources to meet the demands so that the overall cost is minimum. It is necessary that only one resource contributes to the demand of a slot. This constraint will be referred to as single resource assignment (SRA) constraint. We would refer to the problem as the S_RA problem. So far, only 16-approximation to this problem is known. In this paper, we

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Ogbomida, A. O. "POLITICS AND SCARCITY: UNPACKING RESOURCE ALLOCATION DYNAMICS IN NIGERIAN HIGHER EDUCATION." Interdisciplinary Journal of Educational Practice 10, no. 3 (2023): 11–23. https://doi.org/10.5281/zenodo.8239632.

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Resource allocation in Nigerian higher education institutions has long been a pressing issue, with inadequate funding being a major concern. Despite the UNESCO recommendation of allocating a minimum of 26% of annual budget to education, Nigeria allocates only 6% to 7% for higher education. This study delves into the complex and politically charged landscape of resource allocation in Nigerian universities, where meager resources are distributed among various units, departments, and individuals. The allocation of resources is not only a managerial challenge but also a subject of intense research

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Md.Mahbub-Or-Rashid, Julkar Nayeen Mahi Md., Afrin Jeba Jenia, and Tuj Johura Fatema. "Achieving an Efficient Approach through using Resource Allocation, Management and Load Balancing for Cloud Data Centers." Recent Trends in Cloud Computing and Web Engineering 3, no. 1 (2021): 1–10. https://doi.org/10.5281/zenodo.4683400.

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Appropriate resource allocation and management in cloud datacenters has become a crucial consideration for the progress of cloud datacenters. Since allocating resources in a planned and convenient way has a prevailing impact in the issue of "Cloud computing" along these lines the cloud server farms must manage the complexities in making sense of and settling the strategies to control, dispense, utilize, work and move the resources in an enormously effective way. Maintaining Quality of Service (QoS) in cloud datacenters may also become tougher if resources (like- CPU, Hard disk, Memor

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Moummadi, Kamal, Rachida Abidar, and Hicham Medromi. "Distributed Resource Allocation." International Journal of Mobile Computing and Multimedia Communications 4, no. 2 (2012): 49–62. http://dx.doi.org/10.4018/jmcmc.2012040104.

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The growth of technological capabilities of mobile devices, the evolution of wireless communication technologies, and the maturity of embedded systems contributed to expand the Machine to machine (M2M) concept. M2M refers to data communication between machines without human intervention. The objective of this paper is to present the grand schemes of a model to be used in an agricultural Decision support System. The authors start by explaining and justifying the need for a hybrid system that uses both Multi-Agent System (MAS) and Constraint Programming (CP) paradigms. Then, the authors propose

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Schultz, Jack C., Fakhri A. Bazzaz, and John Grace. "Plant Resource Allocation." Ecology 79, no. 2 (1998): 746. http://dx.doi.org/10.2307/176968.

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Field, Trevor, and Jakob Klingert. "Resource Allocation Models." Perspectives: Policy and Practice in Higher Education 5, no. 3 (2001): 83–88. http://dx.doi.org/10.1080/1360310120063383.

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CHEVALEYRE, YANN, PAUL E. DUNNE, ULLE ENDRISS, JÉRÔME LANG, NICOLAS MAUDET, and JUAN A. RODRÍGUEZ-AGUILAR. "Multiagent resource allocation." Knowledge Engineering Review 20, no. 2 (2005): 143–49. http://dx.doi.org/10.1017/s0269888905000470.

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Resource allocation in multiagent systems is a central research issue in the AgentLink community. The aim of the Technical Forum Group on Multiagent Resource Allocation (TFG-MARA) is to provide a venue for the exchange of ideas in this area and to foster collaboration between different research groups. In this article we report on the first meeting of TFG-MARA, which was held as part of the Second AgentLink III Technical Forum in Ljubljana.

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Carr-Hill, Roy, Alison Eastwood, and Pip Stephenson. "RESOURCE ALLOCATION REVIEW." Lancet 332, no. 8603 (1988): 168. http://dx.doi.org/10.1016/s0140-6736(88)90723-4.

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Noseworthy, Tom. "Health Resource Allocation." Journal of Legal Medicine 32, no. 1 (2011): 11–26. http://dx.doi.org/10.1080/01947648.2011.550823.

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von Oppen, M., and James G. Ryan. "Research resource allocation." Food Policy 10, no. 3 (1985): 253–64. http://dx.doi.org/10.1016/0306-9192(85)90064-8.

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Mjelde, K. M. "Fuzzy resource allocation." Fuzzy Sets and Systems 19, no. 3 (1986): 239–50. http://dx.doi.org/10.1016/0165-0114(86)90053-9.

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Kukushkin, N. S., I. S. Men'shikov, O. R. Men'shikova, and V. V. Morozov. "Resource allocation games." Computational Mathematics and Modeling 1, no. 4 (1990): 433–44. http://dx.doi.org/10.1007/bf01128293.

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Reiss, Michael. "Plant resource allocation." Trends in Ecology & Evolution 4, no. 12 (1989): 379–80. http://dx.doi.org/10.1016/0169-5347(89)90104-3.

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Cigler, Ludek, and Boi Faltings. "Symmetric Subgame Perfect Equilibria in Resource Allocation." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1326–32. http://dx.doi.org/10.1609/aaai.v26i1.8233.

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We analyze symmetric protocols to rationally coordinate on an asymmetric, efficient allocation in an infinitely repeated N-agent, C-resource allocation problems. (Bhaskar 2000) proposed one way to achieve this in 2-agent, 1-resource allocation games: Agents start by symmetrically randomizing their actions, and as soon as they each choose different actions, they start to follow a potentially asymmetric "convention" that prescribes their actions from then on. We extend the concept of convention to the general case of infinitely repeated resource allocation games with N agents and C resources. We

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Srinivasan, Thiruvenkadam, Sujitha Venkatapathy, Han-Gue Jo, and In-Ho Ra. "VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management." Journal of Sensor and Actuator Networks 12, no. 5 (2023): 65. http://dx.doi.org/10.3390/jsan12050065.

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Network slicing is widely regarded as the most critical technique for allocating network resources to varied user needs in 5G networks. A Software Defined Networking (SDN) and Network Function Virtualization (NFV) are two extensively used strategies for slicing the physical infrastructure according to use cases. The most efficient use of virtual networks is realized by the application of optimal resource allocation algorithms. Numerous research papers on 5G network resource allocation focus on network slicing or on the best resource allocation for the sliced network. This study uses network sl

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Caiyan, Jiang. "Design of an E-Learning Resource Allocation Model from the Perspective of Educational Equity." International Journal of Emerging Technologies in Learning (iJET) 17, no. 03 (2022): 50–67. http://dx.doi.org/10.3991/ijet.v17i03.29425.

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Nowadays, e-learning and ubiquitous learning have been very common learning methods. Considering the increasing importance of e-learning in public education, it is very necessary to analyze and study the current situation of e-learning resource allocation, as it will provide useful reference for the promotion of educational equity and rational allocation of educational resources across China. However, there have only been a few quantitative and practical studies on the balanced allocation of e-learning resources. To this end, taking English education as an example, this paper designed an e-lea

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49

Li, Jun, Kai Zou, Shang Xiang, Zhen Wan, and Lining Xing. "Resource Allocation to Information Security in Smart Cities Based on Evolutionary Game." Tobacco Regulatory Science 7, no. 4 (2021): 805–15. http://dx.doi.org/10.18001/trs.7.4.1.35.

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Abstract:

Smart city highly relies on cloud computing, Internet of Things and other new technology means, which bring hidden information risk diffusion to urban information security. How to reasonably allocate current urban resources, avoid these information security risks as much as possible, and obtain the highest benefits, have become a practical problem to the current healthy development of smart cities. Based on the discussion of related concepts and technical theories, the information security resource allocation influencing factors index system is constructed from the following aspects: resources

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50

Seto, Katherine, Grantly R. Galland, Alice McDonald, et al. "Resource allocation in transboundary tuna fisheries: A global analysis." Ambio 50, no. 1 (2020): 242–59. http://dx.doi.org/10.1007/s13280-020-01371-3.

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Abstract:

AbstractResource allocation is a fundamental and challenging component of common pool resource governance, particularly transboundary fisheries. We highlight the growing importance of allocation in fisheries governance, comparing approaches of the five tuna Regional Fisheries Management Organizations (tRFMOs). We find all tRFMOs except one have defined resources for allocation and outlined principles to guide allocation based on equity, citizenship, and legitimacy. However, all fall short of applying these principles in assigning fish resources. Most tRFMOs rely on historical catch or effort,

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