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Journal articles on the topic 'Resource allocation'

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Published: 4 June 2021
Last updated: 29 July 2024

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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 (December 27, 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 all requested resources from a single counter. The proposed methodology for allocating resources divides them according to their cost. The paper’s framework for allocating resources consists of various parts. The Unified Heterogeneous Resource Allocation Manager (UHRAM) part of the framework collects and manages resources from several cloud resource providers. The resource identifier is one of the components in the framework, which is coupled to UHRAM to determine the cost of the resources. The low-cost resources are scheduled and to be in a ready state for allocation. The proposed ECRA is simulated and compared based on parameters like total computation time, response time and resource allocation percentage with existing resource allocation methods. The results prove that the proposed ECRA is efficient in allocating the resources in minimum response time and it allocates maximum resources for lower cost.
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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 (March 31, 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 experiential learning, incubation spaces, and networking events, institutions can create a vibrant ecosystem that nurtures innovation and encourages entrepreneurial ventures. This paper presents an innovative approach to optimizing the resource allocation of university innovation and entrepreneurship education through the application of a collaborative filtering algorithm, enhanced by Flemingo Optimized Collaborative Filtering Classification (FOCFC). The study aims to address the challenge of efficiently allocating resources such as funding, mentorship, and infrastructure to support innovation and entrepreneurship initiatives within universities. Through simulated experiments and empirical validations, the effectiveness of the FOCFC-enhanced collaborative filtering algorithm is evaluated in recommending resource allocations tailored to the unique needs and preferences of students and entrepreneurial ventures. Results demonstrate significant improvements in accuracy and efficiency compared to traditional methods, with the FOCFC model achieving a precision rate of 95% in recommending resource allocations. Additionally, the model provides valuable insights into emerging trends and opportunities in the innovation and entrepreneurship ecosystem, enabling universities to adapt their resource allocation strategies proactively. These findings highlight the potential of collaborative filtering algorithms with FOCFC in optimizing resource allocation for university innovation and entrepreneurship education, fostering a supportive and conducive environment for entrepreneurial success.
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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 (June 6, 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 gives priority to low-cost resources. The cost denotes the service cost of the resource. The requested resource is assigned to the user by the CSP, who provides the specific resource at a low cost. This proposed method suggests a UHRAM for collecting and allocating the resources from the different CSPs. UHRAM is a centralized hub for delivering requested resources to users, and it maintains a repository of details about the resources from all CSPs in the heterogeneous cloud. The proposed method is implemented with the user’s data. The results from the comparison show that the proposed cost-based resource allocation method is more efficient than existing methods.
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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 (March 30, 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 Round Robin based Resource allocation and Utilization technique is proposed in this work. The Round Robin technique allocates resources to the request in an efficient with equal priority. Similarly, the proposed technique reduces collision and takes less time for mapping a resource with a request. The experimental results shows improved accuracy than the traditional resource allocation technique.
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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 (April 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. So, all these drawbacks need to be solved in any upcoming resource allocation technique. Hence in this paper, the efficiency of the resource allocation mechanism is improved by proposing two allocation models. Both the allocation models have used the Genetic Algorithm to overcome all the aforesaid drawbacks. However, one of the allocation models includes penalty function and the other does not consider the economic reliability. Both the models are implemented and experimented with different number of jobs and resources. The proposed models are compared with the conventional resource allocation models in terms of utilization, cost factor, failure rate and make span.
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6

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 mathematical model of resource allocation and cost function. Generation of random forests and prediction of target objects are based on historical data. Resource allocation steps are based on predictive structure. The invention provides a resource allocation method that satisfies task completion degree constraints and includes a resource allocation algorithm based on random forest with a high probability of finding an optimal solution. It also addresses the issue that intelligent optimization algorithms such as genetic algorithms are prone to fall into local optimum.
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7

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

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8

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

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9

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 (February 2001): 48–49. http://dx.doi.org/10.1016/s1360-1385(00)01842-2.

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11

Williams, E. S., C. M. Scott, and R. Brazil. "Resource allocation." BMJ 306, no. 6889 (May 22, 1993): 1415. http://dx.doi.org/10.1136/bmj.306.6889.1415.

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

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13

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

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14

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

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15

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 the node can gain more resources if it misreports its resource demands. Surprisingly, if a node misrepresents itself by creating several fictitious nodes but keeps the sum of these nodes’ resource demands the same, the node can achieve more resources evidently. We further present some illustrative examples to verify the results, and we show that a node can achieve 1.83 times resource if it misrepresents itself as two nodes. Finally, we discuss the influence of node’s misrepresenting behavior in tree graph: some child nodes gain fewer resources even if their parent node gains more resources by creating two fictitious nodes.
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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 (February 2, 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 resource to its users fairly. The resource allocation problem is a complex optimization problem with mixed-discrete variables, so whether a resource allocation algorithm can quickly and accurately give the resource allocation scheme is the key to its practical application. According to the characteristics of the problem, we select the multi-agent twin delayed deep deterministic policy gradient (MATD3) to solve the upper slice resource allocation and the discrete and continuous twin delayed deep deterministic policy gradient (DCTD3) to solve the lower user resource allocation. It is crucial to accurately characterize the state, environment, and reward of reinforcement learning for solving practical problems. Thus, we provide an effective definition of the environment, state, action, and reward of MATD3 and DCTD3 for solving the bi-level resource allocation problem. We conduct some simulation experiments and compare it with the multi-agent deep deterministic policy gradient (MADDPG) algorithm and nested bi-level evolutionary algorithm (NBLEA). The experimental results show that the proposed algorithm can quickly provide a better resource allocation scheme.
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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 allocation after Ya’an earthquake in China to verify the applicability of the proposed model.
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18

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 (December 18, 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 resources and increasing consumer demands. Therefore, many unique models and techniques have been proposed to allocate resources efficiently. In the light of the uniqueness of the models and techniques, the main aim of the resource allocation is to limit the overhead/expenses associated with it. This research aims to present a comprehensive, structured literature review on different aspects of resource allocation in cloud computing, including strategic, target resources, optimization, scheduling and power. More than 50 articles, between year 2007 and 2019, related to resource allocation in cloud computing have been shortlisted through a structured mechanism and they are reviewed under clearly defined objectives. It presents a topical taxonomy of resource allocation dimensions, and articles under each category are discussed and analysed. Lastly, salient future directions in this area are discussed.
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19

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 bundles, leading to drastic (often exponential) reductions in computational complexity. We then use this algorithm in the context of self-interested agents to design a combinatorial auction for allocating resources. We empirically demonstrate the effectiveness of our approach by showing that it can, in minutes, optimally solve problems for which a straightforward combinatorial resource-allocation technique would require the agents to enumerate up to 2^100 resource bundles and the auctioneer to solve an NP-complete problem with an input of that size.
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20

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 (May 16, 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, evolutionary algorithms, and deep reinforcement learning methods in dynamically allocating resources to meet performance objectives while minimizing costs. Through a comprehensive evaluation of these approaches using real-world datasets and simulation experiments, we highlight their strengths, limitations, and comparative performance. Our findings provide valuable insights into the effectiveness of AI-driven resource allocation strategies, enabling cloud providers and practitioners to make informed decisions for enhancing cloud infrastructure management
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Mondal, Sakib A. "Resource allocation problem under single resource assignment." RAIRO - Operations Research 52, no. 2 (April 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 propose an algorithm with approximation ratio of 12.
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Lindlbauer, Niklas Martin, Tim Folta, Constance E. Helfat, John R. Busenbark, Marco S. Giarratana, Gwendolyn Kuo-fang Lee, Catherine Maritan, et al. "Resource Allocation and Resource Redeployment." Academy of Management Proceedings 2020, no. 1 (August 2020): 13886. http://dx.doi.org/10.5465/ambpp.2020.13886symposium.

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23

Yingjie, Xu. "Application of BP Neural Network to Optimize the Allocation of Art Teaching Resources." Tobacco Regulatory Science 7, no. 5 (September 30, 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 on multi rate cognition. From the point of view that there are a pair of primary users and a pair of secondary users in the network, this method constructs a resource allocation delay model, obtains the resource allocation delay under different modes, and dynamically adjusts the transmission rate on the allocation resource block. The art teaching resource allocation scheduling problem is modeled as a nonlinear optimization problem, and the constraints of the optimization problem are given, which are integrated into greedy computing. The global optimal solution of the problem is carried out by using the method, and the allocation of art teaching resources is completed. Simulation results show that the proposed algorithm greatly improves the efficiency and effect of teaching network resource allocation.
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24

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 (February 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 paper proposes a latency-aware max-min algorithm (LAM) for allocation of resources in cloud infrastructures. The proposed algorithm was designed to address challenges associated with resource allocation such as variations in user demands and on-demand access to unlimited resources. It is capable of allocating resources in a cloud-based environment with the target of enhancing infrastructure-level performance and maximization of profits with the optimum allocation of resources. A priority value is also associated with each user, which is calculated by analytic hierarchy process (AHP). The results validate the superiority for LAM due to better performance in comparison to other state-of-the-art algorithms with flexibility in resource allocation for fluctuating resource demand patterns.
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25

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 (January 2, 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 penalties with positional-dependent weights) and resource consumption cost minimization is polynomially solvable. In addition, under the convex resource allocation, we show that scheduling (resp. resource consumption) cost minimization is solvable in polynomial time subject to the resource consumption (resp. scheduling) cost being bounded.
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26

Cigler, Ludek, and Boi Faltings. "Symmetric Subgame Perfect Equilibria in Resource Allocation." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 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 show that for any convention, there exists a symmetric subgame perfect equilibrium which implements it. We present two conventions: bourgeois, where agents stick to the first allocation; and market, where agents pay for the use of resources, and observe a global coordination signal which allows them to alternate between different allocations. We define price of anonymity of a convention as the ratio between the maximum social payoff of any (asymmetric) strategy profile and the expected social payoff of the convention. We show that while the price of anonymity of the bourgeois convention is infinite, the market convention decreases this price by reducing the conflict between the agents.
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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 (April 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 an approach for Constraint Programming and Multi-Agent System mixing based on controller agent concept. The authors present concrete constraints and agents to be used in a distributed architecture based on the proposed approach for M2M services and agricultural decision support. The platform is built in Java using general interfaces of both MAS and Constraint Satisfaction Problem (CSP) platforms and the conception is made by agent UML (AUML).
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28

Schultz, Jack C., Fakhri A. Bazzaz, and John Grace. "Plant Resource Allocation." Ecology 79, no. 2 (March 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 (January 2001): 83–88. http://dx.doi.org/10.1080/1360310120063383.

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30

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 (June 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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31

Carr-Hill, Roy, Alison Eastwood, and Pip Stephenson. "RESOURCE ALLOCATION REVIEW." Lancet 332, no. 8603 (July 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 (February 28, 2011): 11–26. http://dx.doi.org/10.1080/01947648.2011.550823.

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33

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

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34

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

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35

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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36

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

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37

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 (September 13, 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 slicing and optimal resource allocation to achieve performance optimization using requirement-based network slicing. The proposed approach includes three phases: (1) Slice Creation by Machine Learning methods (SCML), (2) Slice Isolation through Resource Allocation (SIRA) of requests via a multi-criteria decision-making approach, and (3) Slice Management through Resource Transfer (SMART). We receive a set of Network Service Requests (NSRs) from users. After receiving the NSRs, the SCML is used to form slices, and SIRA and SMART are used to allocate resources to these slices. Accurately measuring the acceptance ratio and resource efficiency helps to enhance overall performance. The simulation results show that the SMART scheme can dynamically change the resource allocation according to the test conditions. For a range of network situations and Network Service Requests (NSRs), the performance benefit is studied. The findings of the simulation are compared to those of the literature in order to illustrate the usefulness of the proposed work.
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Seto, Katherine, Grantly R. Galland, Alice McDonald, Angela Abolhassani, Kamal Azmi, Hussain Sinan, Trent Timmiss, Megan Bailey, and Quentin Hanich. "Resource allocation in transboundary tuna fisheries: A global analysis." Ambio 50, no. 1 (September 3, 2020): 242–59. http://dx.doi.org/10.1007/s13280-020-01371-3.

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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, while equity principles rarely determine dedicated rights. Further, the current system of annual negotiations reduces certainty, trust, and transparency, counteracting many benefits asserted by rights-based management proponents. We suggest one means of gaining traction may be to shift conversations from allocative rights toward weighting of principles already identified by most tRFMOs. Incorporating principles into resource allocation remains a major opportunity, with important implications for current and future access to fish.
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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 (February 18, 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-learning resource allocation model from the perspective of education equity. First, a resource allocation model of e-learning resources was constructed, and the e-learning resource access request allocation and e-learning resource allocation methods were described in detail. Then, the e-learning resource allocation framework was constructed, and the e-learning resource allocation strategy was given from the perspective of education equity. The experimental results prove that the proposed e-learning resource allocation model can effectively avoid wasting resources and realize rational distribution of, efficient access to and extensive sharing of resources.
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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 (July 31, 2021): 805–15. http://dx.doi.org/10.18001/trs.7.4.1.35.

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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, threat sources, vulnerabilities and security measures. With the further analysis of information security factors and their affecting mechanisms, the basic theoretical framework of information security resource allocation is established based on the evolutionary game. The information security resource allocation problem is divided into the internal resource allocation and external resource allocation. External resource allocation is subdivided into complementary external resource allocation, alternative external resource allocation and weakly related external resource allocation. Under this framework, the subject relationship in various situations is analyzed. This research work can conduct a reasonable allocation of resources related to information security.
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Cigler, L., and B. Faltings. "Symmetric Subgame-Perfect Equilibria in Resource Allocation." Journal of Artificial Intelligence Research 49 (February 26, 2014): 323–61. http://dx.doi.org/10.1613/jair.4166.

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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, where the resources are all homogeneous. Bhaskar proposed one way to achieve this in 2-agent, 1-resource 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 show that for any convention, there exists a symmetric subgame-perfect equilibrium which implements it. We present two conventions: bourgeois, where agents stick to the first allocation; and market, where agents pay for the use of resources, and observe a global coordination signal which allows them to alternate between different allocations. We define price of anonymity of a convention as a ratio between the maximum social payoff of any (asymmetric) strategy profile and the expected social payoff of the subgame-perfect equilibrium which implements the convention. We show that while the price of anonymity of the bourgeois convention is infinite, the market convention decreases this price by reducing the conflict between the agents.
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Karahda, Aarti, and Shobhit Kumar Prasad. "The Mental Health Care Act 2017 and Mental Health Resource Allocation in India." Annals of Indian Psychiatry 8, no. 1 (2024): 83–88. http://dx.doi.org/10.4103/aip.aip_80_22.

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Abstract Mental health policymakers are now tasked with maximizing the efficient and effective use of mental health resources as a result of fundamental changes to mental health laws. A crucial step in this process is ensuring optimal resource allocation across the service. Multiple biases prevent policymakers from allocating resources to mental health, resulting in a violation of the right to health, an increase in suffering, and a heavy economic burden associated with mental illness. This article provides a summary of Indian mental health policy, examines Indian public perceptions of mental health, and assesses the impact of these perceptions on legislation and mental health resource allocation. Understanding resource allocation from the perspective of policymakers can enhance psychiatrists’ ability to influence the process.
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Bredereck, Robert, Andrzej Kaczmarczyk, Junjie Luo, Rolf Niedermeier, and Florian Sachse. "On Improving Resource Allocations by Sharing." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (June 28, 2022): 4875–83. http://dx.doi.org/10.1609/aaai.v36i5.20416.

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Given an initial resource allocation, where some agents may envy others or where a different distribution of resources might lead to higher social welfare, our goal is to improve the allocation without reassigning resources. We consider a sharing concept allowing resources being shared with social network neighbors of the resource owners. To this end, we introduce a formal model that allows a central authority to compute an optimal sharing between neighbors based on an initial allocation. Advocating this point of view, we focus on the most basic scenario where a resource may be shared by two neighbors in a social network and each agent can participate in a bounded number of sharings. We present algorithms for optimizing utilitarian and egalitarian social welfare of allocations and for reducing the number of envious agents. In particular, we examine the computational complexity with respect to several natural parameters. Furthermore, we study cases with restricted social network structures and, among others, devise polynomial-time algorithms in path- and tree-like (hierarchical) social networks.
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Chen, Chao, Changjun Fan, and Xingxing Liang. "A Multiobjective Resource Allocation Algorithm for Robust Project Scheduling." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 7701–4. http://dx.doi.org/10.1166/jctn.2016.4426.

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Resource allocation is an important procedure which involves allocating finite resources to the activities of a given baseline schedule. Based on the conception of Pareto Optimization, a multiobjective optimization approach for the resource allocation problem is proposed in this paper. The problem is first described. Then the detailed procedure of the proposed algorithm is given. Finally, an extensive computational results obtained on a set of benchmark problems are reported.
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Vijayaraj, N., and T. Senthil Murugan. "Resource Allocation in Cloud using Multi Bidding Model with User Centric Behavior Analysis." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 1008–19. http://dx.doi.org/10.2174/2213275912666190404160733.

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Background: Number of resource allocation and bidding schemes had been enormously arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud services depending on the reputation would not produce fair result in cloud computing. Since the cloud users not only looking for the efficient services but in major they look towards the cost. So here there is a way of introducing the bidding option system that includes efficient user centric behavior analysis model to render the cloud services and resource allocation with low cost. Objective: The allocation of resources is not flexible and dynamic for the users in the recent days. This gave me the key idea and generated as a problem statement for my proposed work. Methods: An online auction framework that ensures multi bidding mechanism which utilizes user centric behavioral analysis to produce the efficient and reliable usage of cloud resources according to the user choice. Results: we implement Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to provide better allocation of cloud resources which ensures bidding and user behavior. Conclusion: Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis is implemented & system is designed in such a way to provide better allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained accordingly such that to produce efficient resource utilization. Further the work can be taken towards data analytics and prediction of user behavior while allocating the cloud resources.
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Arora, Manish, and M. Syamala Devi. "Design of Multi Agent System for Resource Allocation and Monitoring." International Journal of Agent Technologies and Systems 3, no. 1 (January 2011): 1–10. http://dx.doi.org/10.4018/jats.2011010101.

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The objective of Resource Allocation and Monitoring System is to make the procedures involved in allocating fund resources to competing clients transparent so that deserving candidates get funds. Proactive and goal directed behaviour of agents make the system transparent and intelligent. This paper presents design of Multi Agent Systems for Resource Allocation and Monitoring using Agent Unified Modelling Language (AUML) and implementation in agent based development tool. At a conceptual level, three agents are identified with their roles and responsibilities. The identified agents, functionalities, and interactions are also included and results show that multi agent technology can be used for effective decision making for resource allocation and monitoring problem.
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Schneckenburger, Sebastian, Britta Dorn, and Ulle Endriss. "Minimising inequality in multiagent resource allocation." Annals of Mathematics and Artificial Intelligence 90, no. 4 (March 23, 2022): 339–71. http://dx.doi.org/10.1007/s10472-022-09789-z.

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AbstractWe analyse the problem of finding an allocation of resources in a multiagent system that is as fair as possible in terms of minimising inequality between the utility levels enjoyed by the individual agents. We use the well-known Atkinson index to measure inequality and we focus on the distributed approach to multiagent resource allocation, where new allocations emerge as the result of a sequence of local deals between groups of agents who agree on an exchange of some of the items in their possession. Our results show that it is possible to design systems that provide theoretical guarantees for optimal outcomes that minimise inequality, but also that there are significant computational hurdles to be overcome in the worst case. In particular, finding an optimal allocation is computationally intractable and under the distributed approach a large number of structurally complex deals, possibly involving many agents and items, may be required before convergence to a socially optimal allocation. This remains true even in severely restricted resource allocation scenarios where all agents have the same utility function. From a methodological point of view, while much work in multiagent resource allocation relies on combinatorial arguments, here we instead use insights from basic calculus.
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Tajer, Ali, Maha Zohdy, and Khawla Alnajjar. "Resource Allocation Under Sequential Resource Access." IEEE Transactions on Communications 66, no. 11 (November 2018): 5608–20. http://dx.doi.org/10.1109/tcomm.2018.2846657.

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49

Choi, Hyunseok, Yoonhyeong Lee, Gayeong Kim, Euisin Lee, and Youngju Nam. "Resource Cluster-Based Resource Search and Allocation Scheme for Vehicular Clouds in Vehicular Ad Hoc Networks." Sensors 24, no. 7 (March 28, 2024): 2175. http://dx.doi.org/10.3390/s24072175.

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Vehicular clouds represent an appealing approach, leveraging vehicles’ resources to generate value-added services. Thus, efficiently searching for and allocating resources is a challenge for the successful construction of vehicular clouds. Many recent schemes have relied on hierarchical network architectures using clusters to address this challenge. These clusters are typically constructed based on vehicle proximity, such as being on the same road or within the same region. However, this approach struggles to rapidly search for and consistently allocate resources, especially considering the diverse resource types and varying mobility of vehicles. To address these limitations, we propose the Resource Cluster-based Resource Search and Allocation (RCSA) scheme. RCSA constructs resource clusters based on resource types rather than vehicle proximity. This allows for more efficient resource searching and allocation. Within these resource clusters, RCSA supports both intra-resource cluster search for the same resource type and inter-resource cluster search for different resource types. In RCSA, vehicles with longer connection times and larger resource capacities are allocated in vehicular clouds to minimize cloud breakdowns and communication traffic. To handle the reconstruction of resource clusters due to vehicle mobility, RCSA implements mechanisms for replacing Resource Cluster Heads (RCHs) and managing Resource Cluster Members (RCMs). Simulation results validate the effectiveness of RCSA, demonstrating its superiority over existing schemes in terms of resource utilization, allocation efficiency, and overall performance.
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Sangaiah, Arun Kumar, Ali Asghar Rahmani Hosseinabadi, Morteza Babazadeh Shareh, Seyed Yaser Bozorgi Rad, Atekeh Zolfagharian, and Naveen Chilamkurti. "IoT Resource Allocation and Optimization Based on Heuristic Algorithm." Sensors 20, no. 2 (January 18, 2020): 539. http://dx.doi.org/10.3390/s20020539.

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The Internet of Things (IoT) is a distributed system that connects everything via internet. IoT infrastructure contains multiple resources and gateways. In such a system, the problem of optimizing IoT resource allocation and scheduling (IRAS) is vital, because resource allocation (RA) and scheduling deals with the mapping between recourses and gateways and is also responsible for optimally allocating resources to available gateways. In the IoT environment, a gateway may face hundreds of resources to connect. Therefore, manual resource allocation and scheduling is not possible. In this paper, the whale optimization algorithm (WOA) is used to solve the RA problem in IoT with the aim of optimal RA and reducing the total communication cost between resources and gateways. The proposed algorithm has been compared to the other existing algorithms. Results indicate the proper performance of the proposed algorithm. Based on various benchmarks, the proposed method, in terms of “total communication cost”, is better than other ones.
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