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Journal articles on the topic 'Resource allocating'
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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 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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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 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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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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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 overall cost of the resource, communication channel cost, reliability and popularity factor. We have proposed a framework for communication between resource owner and cloud using Resource Cloud Communication Paradigm (RCCP). We extend the CloudSim[2] adding provisioner policies and Efficient Resource Allocation (ERA) algorithm in VMM allocation policy as a decision support for resource provisioner.
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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 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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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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Ma, Ding, M. Onderwater, F. Wetzels, et al. "Cost-Efficient Allocation of Additional Resources for the Service Placement Problem in Next-Generation Internet." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/517409.
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One of the major challenges in next-generation Internet is to allocate services to nodes in the network. This problem, known as theservice placement problem, can be solved by layered graph approach. However, due to the existence of resource bottleneck, the requests are rejected from the beginning in the resource constrained network. In this paper we propose two iterative algorithms for efficient allocation of additional resources in order to improve the ratio of accepted service placement requests. To this end, we (1) introduce a new concept of sensitivity for each service node to locate the bottleneck node, (2) state the problem of allocating additional resources, and (3) use sensitivity to propose a simple iterative algorithm and an utilization-based iterative algorithm for efficient resource allocation. The performance of these two algorithms is evaluated by simulation experiments in a variety of parameter settings. The results show that the proposed algorithms increase request acceptance ratio significantly by allocating additional resources into the bottleneck node and links. The utilization-based iterative algorithm also decreases the long-term cost by making efficient use of additional resources.
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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 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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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 (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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Deng, Hongyu, Cheng Wu, and Yiming Wang. "A cognitive gateway-based spectrum sharing method in downlink round robin scheduling of LTE system." Modern Physics Letters B 31, no. 19-21 (2017): 1740070. http://dx.doi.org/10.1142/s021798491740070x.
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A key technique of LTE is how to allocate efficiently the resource of radio spectrum. Traditional Round Robin (RR) scheduling scheme may lead to too many resource residues when allocating resources. When the number of users in the current transmission time interval (TTI) is not the greatest common divisor of resource block groups (RBGs), and such a phenomenon lasts for a long time, the spectrum utilization would be greatly decreased. In this paper, a novel spectrum allocation scheme of cognitive gateway (CG) was proposed, in which the LTE spectrum utilization and CG’s throughput were greatly increased by allocating idle resource blocks in the shared TTI in LTE system to CG. Our simulation results show that the spectrum resource sharing method can improve LTE spectral utilization and increase the CG’s throughput as well as network use time.
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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 propose an algorithm with approximation ratio of 12.
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Davies, Mark. "Allocating resources in mental health: a clinician's guide to involvement." Advances in Psychiatric Treatment 12, no. 5 (2006): 384–91. http://dx.doi.org/10.1192/apt.12.5.384.
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With fundamental changes to the way services are commissioned and resourced within the UK's National Health Service (NHS), optimising the efficient and effective use of resources has become a key task for mental health clinicians and managers. A core step in this process is ensuring that resources are optimally allocated across the service. This article outlines steps in resource allocation, including understanding how resources are managed through budgets, the link between resource matching and care delivery, and methods of reallocating resources to improve service performance. Influencing appropriate allocation is a critical role for psychiatrists, working with both managers and commissioners in the decision-making process. Understanding resource allocation from a management perspective should improve the ability of psychiatrists to influence this process more effectively.
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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 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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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 algorithm implementations in cloud computing, Direction Method of Multipliers (ADMM) algorithm for SDN Networking, FFR algorithm application in cellular network, Fairness-based Distributed Resource Allocation (FDRA) algorithm for 5G networks, then each the results in each area are discussed in critique point of view.
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Thapa, Ram Bahadur. "Financial Resource Mobilization in Pokhara Sub-Municipal Corporation." Journal of Nepalese Business Studies 1, no. 1 (2006): 81–84. http://dx.doi.org/10.3126/jnbs.v1i1.42.
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Local bodies can play a vital role in nation building process through efficient mobilization of local financial resource. Financial resource mobilization includes both raising the adequate revenue and optimal allocation of funds to meet the local people’s needs. Municipality finances its expenditures with internal and external revenues. It has to increase its efficiency in allocating financial resources to maximize the marginal productivity of resources.
 
 Journal of Nepalese Business Studies Vol.1(1) 2004 pp.81-84
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Deco, Gustavo, and Jürgen Ebmeyer. "Coarse Coding Resource-Allocating Network." Neural Computation 5, no. 1 (1993): 105–14. http://dx.doi.org/10.1162/neco.1993.5.1.105.
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In recent years localized receptive fields have been the subject of intensive research, due to their learning speed and efficient reconstruction of hypersurfaces. A very efficient implementation for such a network was proposed recently by Platt (1991). This resource-allocating network (RAN) allocates a new neuron whenever an unknown pattern is presented at its input layer. In this paper we introduce a new network architecture and learning paradigm. The aim of our approach is to incorporate "coarse coding" to the resource-allocating network. The network presented here provides for each input coordinate a separate layer, which consists of one-dimensional, locally tuned gaussian neurons. In the following layer multidimensional receptive fields are built by using pi-neurons. Linear neurons aggregate the outputs of the pi-neurons in order to approximate the required input-output mapping. The learning process follows the ideas of the resource-allocating network of Platt but due to the extended architecture of our network other improvements of the learning process had to be defined. Compared to the resource-allocating network a more compact network with comparable accuracy is obtained.
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Campbell, Thomas C. "Water: Allocating a Scarce Resource." Journal - American Water Works Association 77, no. 9 (1985): 53–56. http://dx.doi.org/10.1002/j.1551-8833.1985.tb05606.x.
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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 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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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. 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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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 combination of auto-scaling algorithms, resource allocation and scheduling for allocating the appropriate resources and scheduled them. This model consists of three new algorithms namely Grey Wolf Optimization and Fuzzy rules based Resource allocation and Scheduling Algorithm (GWOFRSA), Auto-Scaling Algorithm for Cloud based Web Application (ASACWA) and Auto-Scaling Algorithm for handling Distributed Computing Tasks (ASADCT). Here, we introduce new auto-scaling algorithms for enhancing the performance of cloud services. In this work, the optimization technique is used to predict the cloud server workload, resource requirements and it also uses fuzzy rules for monitoring the resource utilization and the size of virtual machine allocation process. According to the workload prediction, the completion time is estimated for each cloud server. The experiments are conducted by using a simulator called CloudSim environment of Java programming and compared with the existing works available in this direction in terms of resource utilization and enhance the cloud performance with better Quality of Service of Virtual Machine allocation, Missed Deadline, Demand Satisfaction, Power Utilization, CPU Load and throughput.
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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 sharing the data with a central server. The resource allocation algorithm is responsible for allocating resources to the edge nodes efficiently. The secure communication protocol is used to protect the privacy of the data during the federated learning process. The proposed framework is evaluated using simulations. The results show that the proposed framework can achieve better performance than traditional resource allocation algorithms.</strong>
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Zhang, Bin, and Yi Liu. "Distributed Resource Allocation for Green HetNets with Renewable Energy Resources." International Journal of Pattern Recognition and Artificial Intelligence 35, no. 08 (2021): 2159029. http://dx.doi.org/10.1142/s0218001421590291.
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In fifth generation (5G) systems, green heterogeneous network (HetNet) is capable of achieving energy efficiency by densely deploying renewable-powered small cells. However, the small cells may suffer performance degradation due to the limited backhaul from macro base station (BS) and renewable intermittency. In this paper, we introduce a distributed HetNet architecture in which the renewable-powered small cell BSs collaboratively exchange information and allocate the spectrum and power resources by themselves. Considering the uncertainty of the available spectrum, renewable energy supply and traffic loads, a stochastic optimization problem is formulated to maximize the energy efficiency for distributed small cell BSs. A distributed resource allocation algorithm is proposed to obtain the optimal spectrum and power allocating strategies for each small cell. Finally, the numerical results demonstrate the effectiveness of the proposed algorithm.
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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, 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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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 (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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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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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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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 resources among competing product teams within an organization can be modelled as a cooperative game, where teams with competing priorities must negotiate resource allocation based on their claims and the available budget.The article explores cloud resource allocation as a cooperative game, particularly in situations where the total budget is insufficient to meet all teams’ demands. Several resource allocation methods are discussed, including the proportional rule and the nucleolus-based approach, which seeks to minimize the coalitions’ incentives to deviate. The nucleolus method offers a stable and fair solution by distributing resources in a way that maximizes stability and reduces the likelihood of coalitions deviating from the overall allocation. This approach ensures that no team is allocated more than its claim and maintains fairness by adhering to principles such as claim boundaries, monotonicity, and resource constraints. Ultimately, the nucleolus-based method is proposed as an effective solution for allocating cloud resources in a cooperative and stable manner, ensuring that resource allocation is both fair and efficient.
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Mao, Li, De Yu Qi, Wei Wei Lin, Bo Liu, and Ye Da Li. "An Energy-Efficient Resource Scheduling Algorithm for Cloud Computing based on Resource Equivalence Optimization." International Journal of Grid and High Performance Computing 8, no. 2 (2016): 43–57. http://dx.doi.org/10.4018/ijghpc.2016040103.
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With the rapid growth of energy consumption in global data centers and IT systems, energy optimization has become an important issue to be solved in cloud data center. By introducing heterogeneous energy constraints of heterogeneous physical servers in cloud computing, an energy-efficient resource scheduling model for heterogeneous physical servers based on constraint satisfaction problems is presented. The method of model solving based on resource equivalence optimization is proposed, in which the resources in the same class are pruning treatment when allocating resource so as to reduce the solution space of the resource allocation model and speed up the model solution. Experimental results show that, compared with DynamicPower and MinPM, the proposed algorithm (EqPower) not only improves the performance of resource allocation, but also reduces energy consumption of cloud data center.
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Meadows, Graham. "Resource Allocation and Moral Theory: Easy and Hard Problems." Australasian Psychiatry 5, no. 5 (1997): 228–30. http://dx.doi.org/10.3109/10398569709082277.
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This paper is intended as a constructive response to“Managing Scarcity: a worked example using Burden and Efficacy”, from Professor Gavin Andrews. Professor Andrews” paper is helpful in proposing an approach to allocating resources which has some claims to being rational. However the presentation begs many questions. In this commentary, I set out to critique Professor Andrews” model of resource allocation from a moral theoretical perspective.
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Kazemi, Ali, and Daniel Eek. "EFFECTS OF GROUP GOAL AND RESOURCE VALENCE ON ALLOCATION PREFERENCES IN PUBLIC GOOD DILEMMAS." Social Behavior and Personality: an international journal 35, no. 6 (2007): 803–18. http://dx.doi.org/10.2224/sbp.2007.35.6.803.
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Previous research has not been conclusive as to whether people prefer different or identical allocation principles in distributions of positive and negative outcomes. Thus, in this study, the question of whether or not group goal accounts for preferred allocation of positive and negative outcomes was posed. As hypothesized for division of surpluses, the results showed that relationship-oriented goals predicted preferences for equality, whereas performance-oriented goals predicted preferences for equity. Moreover, the results were the same for allocation of deficits. This suggests that people implicitly have different orientations, or goals, in mind in group situations that similarly influence the way they prefer to allocate positive and negative outcomes. The results also showed that participants allocating deficits deviated to a larger extent from the allocation principles than did participants allocating surpluses.
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Patrão Neves, Maria do Céu. "Ethical health resources allocation: Why the distinction between ‘rationing’ and ‘rationalization’ matters." Revista de Bioética y Derecho, no. 50 (July 29, 2020): 63–79. http://dx.doi.org/10.1344/rbd2020.50.32044.
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Allocation of health resources has an irreducible ethical dimension, thus cannot be decided only technically, but must be ethically weighed, what paradigmatic experiences of macro (Oregon Basic Health Services Act, 1989) and micro allocation (God’s Committee, 1962) have shown. Justice is required in the enunciation of prioritization criteria, and transparency in its application. In situations of aggravated resource scarcity, it is common to take ‘allocate’ and ‘rationing’ as synonyms or claim that ‘allocate’ is always ‘rationing’. Rejecting these positions, there is a distinction between 'allocating' (resource management) from 'rationing' (allocation of limited resources to a limited number of persons) and 'rationalizing' (optimization of available resources). These distinctions are ethically pertinent, showing how only 'rationalization' respects justice, transparency and human dignity.
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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, Memory, Networks etc.) are not properly allocated. Therefore, an extraordinarily efficient scheme should be pursued for resource allocation and management in cloud data centers after the analyzing, diagnosing and well-identifying of existing problems. In this research work, a resource allocation scheme is going to be proposed in the form of an algorithm named “Dynamic and Efficient Resource Allocation and Management (DERAM)” algorithm which will mainly take into consideration–managing the CPU, memory, hard disk and Networks as the resources of cloud computing.
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Sandu, Roman, and Alexandr Shcherbakov. "A Resource Allocation Algorithm for a History-Aware Frame Graph." Journal of WSCG 31, no. 1-2 (2023): 63–70. http://dx.doi.org/10.24132/jwscg.2023.7.
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We consider the problem of memory consumption by a real-time GPU-accelerated graphical application. A history of a resource is defined for a particular frame to be the final contents of such a resource at the end of the previous frame. When organizing a graphical application using a frame rendering graph approach, it makes sense to implement automatic serving of resource history read requests of nodes. In absence of history resource requests, allocating resources for a fixed frame graph is the classic problem of dynamic storage allocation (DSA). In this paper, we formulate a generalization of DSA that enables memory reuse for resources with history requests and provide a practical approximate algorithm for solving it.
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Alodhaibi, Sultan, Robert L. Burdett, and Prasad K. D. V. Yarlagadda. "A Framework for Sharing Staff between Outbound and Inbound Airport Processes." Mathematics 8, no. 6 (2020): 895. http://dx.doi.org/10.3390/math8060895.
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This paper proposes an advanced simulation-optimization approach to evaluate and optimize the passenger flows within international airports. This approach allocates resources intelligently during the simulation process and balances demand and service quality. The resource allocation performed by our Advanced Resource Management (ARM) algorithm was used to develop an integrated system for arranging resources, identifying the proper resources, and allocating them throughout the model. It was used to investigate the influences of different staff allocation techniques on the inbound and outbound processes of an airport terminal. The purpose of the proposed simulation-optimization approach is to enhance passenger satisfaction through ensuring reasonable wait times during processing at the lowest cost possible (minimal staff hours).
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Feng, Lei, Wenjing Li, Peng Yu, and Xuesong Qiu. "An Enhanced OFDM Resource Allocation Algorithm in C-RAN Based 5G Public Safety Network." Mobile Information Systems 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/9586287.
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Public Safety Network (PSN) is the network for critical communication when disaster occurs. As a key technology in 5G, Cloud-Radio Access Network (C-RAN) can play an important role in PSN instead of LTE-based RAN. This paper firstly introduces C-RAN based PSN architecture and models the OFDM resource allocation problem in C-RAN based PSN as an integer quadratic programming, which allows the trade-off between expected bitrates and allocating fairness of PSN Service User (PSU). However, C-RAN based PSN needs to improve the efficiency of allocating algorithm because of a mass of PSU-RRH associations when disaster occurs. To deal with it, the resources allocating problem with integer variables is relaxed into one with continuous variables in the first step and an algorithm based on Generalized Bender’s Decomposition (GBD) is proposed to solve it. Then we use Feasible Pump (FP) method to get a feasible integer solution on the original OFDM resources allocation problem. The final experiments show the total throughput achieved by C-RAN based PSN is at most higher by 19.17% than the LTE-based one. And the average computational time of the proposed GBD and FP algorithm is at most lower than Barrier by 51.5% and GBD with no relaxation by 30.1%, respectively.
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Aliu, Daniel, and Muyideen Omuya Momoh. "Optimal Resource Scheduling Algorithm for OFDMA-based Multicast Traffic Delivery over WIMAX Networks using Particle Swarm Optimization." International Journal of Software Engineering and Computer Systems 7, no. 2 (2021): 50–63. http://dx.doi.org/10.15282/ijsecs.7.2.2021.6.0089.
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Researchers are yet to entirely mapped out the difficulty in allocating optimal resources to mobile Worldwide Interoperability for Microwave Access (WiMAX) subscribers. This research presents an optimal scheduling algorithm for WiMAX resource allocation based on an Particle Swarm Optimization (PSO). In this work, sub-group creation is used to offer a PSO-based technique for allocating subcarriers and Orthogonal Frequency Division Multiplexing (OFDM) symbols to mobile WiMAX customers. The WiMAX network environment is organized into seven layers, with seven different modulation and coding algorithms proposed for sending packets to subscribers within each layer. By adopting an improved PSO-based WiMAX resource allocation method, an enhanced model for throughput maximization and channel data rate was implemented. The Aggregate Data Rate (ADR) and Channel Data Rate (CDR) for each scenario were obtained by simulating several scenarios of WiMAX multicast service to mobile users. Based on the performance evaluation of the enhanced algorithm for ADR and CDR, the results for the various layers and uniform distribution of users over the full layers were 350Mbps, 525Mbps, 700Mbps, 1050Mbps, 1050Mbps, 1400Mbps, 1575Mbps, and 1398Mbps. 6.98Mbps, 10.48Mbps, 13.97Mbps, 20.95Mbps, 20.95Mbps, 27.94Mbps, 31.5Mbps, and 28Mbps were also achieved for CDR. The significance of optimal resource allocation is to achieved a maximum ADR and CDR. The results showed a fair distribution of resources within the coverage area of the network.
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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 (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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Hamrick, Florence A., John H. Schuh, and Mack C. Shelley. "Predicting higher education graduation rates from institutional characteristics and resource allocation." education policy analysis archives 12 (May 4, 2004): 19. http://dx.doi.org/10.14507/epaa.v12n19.2004.
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This study incorporated institutional characteristics (e.g., Carnegie type, selectivity) and resource allocations (e.g., instructional expenditures, student affairs expenditures) into a statistical model to predict undergraduate graduation rates. Instructional expenditures, library expenditures, and a number of institutional classification variables were significant predictors of graduation rates. Based on these results, recommendations as well as warranted cautions are included about allocating academic financial resources to optimize graduation rates
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Qiao, Liang, Miao Dai, and Meng Na Li. "Integrated Water Resource Management in Yinchuan Plain." Applied Mechanics and Materials 448-453 (October 2013): 1057–61. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.1057.
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Based on the analysis of the current state of water resources and utilization characters, the optimizing and allocating model of water resources in Yinchuan plain is established by multi-objective planning methodology. Systematic viewpoint runs throughout the whole modeling process. Furthermore, by employing the established model, the proper allocation of the industrial and agricultural water, the ecological environment water and integrated development of surface water and groundwater are discussed. The more reasonable water utilization structure is suggested. The water resource for agriculture is decreasing, while the water resource for people life, industry and ecologic environment are increasing. This change of water utilization is correspond to economic and social development trend.
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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 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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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 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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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.
Full textAbstract:
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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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 (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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Pourebrahimi, Behnaz, and Koen Bertels. "Self-Adaptive Economic-Based Resource Allocation in Ad-Hoc Grids." International Journal of Embedded and Real-Time Communication Systems 3, no. 2 (2012): 111–30. http://dx.doi.org/10.4018/jertcs.2012040106.
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Resource allocation is the process of discovering and allocating resources to requested tasks in a way that satisfy both user jobs and resource administrators. In ad-hoc Grids, resource allocation is a challenging undertaking as tasks and resources are distributed, heterogeneous in nature, owned by different individuals or organizations and they may arise spontaneously at any time with various requirements and availabilities. In this paper, the authors address an economic-based framework for resource allocation in ad-hoc Grids to deal with the dynamic nature of such networks. Within the economic framework, self-interested nodes in ad-hoc Grids are considered as consumers (buyers) and producers (sellers) of resources. Consumers and producers of resources are autonomous agents that cooperate through a simple, single metric namely the price that summarizes the global state of a network in a number. Adaptation is achieved by individual nodes through adopting a bidding strategy that adjusts the price according to the current state of the network in order to optimize the local utility of the node.
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Григорьева, М. П., О. В. Кружкова та Е. С. Кузнецова. "Модель и алгоритмы распределения внутренних ресурсов в организационной системе". Сибирский пожарно-спасательный вестник, № 3(34) (11 жовтня 2024): 41–55. http://dx.doi.org/10.34987/vestnik.sibpsa.2024.92.92.003.
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Рассмотрены механизмы распределения государственных ресурсов на содержание федеральных органов исполнительной власти в сложных условиях несбалансированности бюджетов и оптимизации финансирования для исполнения бюджетных полномочий в полном объеме, не снижая качества предоставления государственных услуг. Предложены модель и алгоритмы распределения ресурсов территориального органа федерального органа исполнительной власти (ФОИВ) на примере МЧС России. Авторами рассмотрены ключевые особенности планирования и распределения ресурсов при осуществлении основных видов деятельности. В рамках исследования разработаны алгоритм планирования ресурсов на содержание территориального органа МЧС России, алгоритм распределения ресурсов на материально-техническое обеспечение, алгоритм распределения ресурсов в целях стимулирования личного состава, алгоритм планирования и проведения закупок. Разработанные алгоритмы становятся основой модели процесса распределения ресурсов на содержание территориального органа МЧС России. Разработанная модель учитывает механизмы оптимального распределения ресурсов на основе математических моделей приоритетов и позволяет решать различные задачи оптимизации ресурсов по выбранной стратегии. The article considered mechanisms of allocation of state resources for the maintenance of federal executive authorities in the context of budget sequestration and optimization of financing for the full implementation of budgetary powers without reducing the quality of public services. A model and algorithms for the allocation of resources of a territorial body of the federal executive authority (FOIV) are proposed using the example of Emercom of Russia. The authors consider the key features of resource planning and allocation in the implementation of the main types of activities. As part of the study, an algorithm for resource planning for the maintenance of the Main Directorate of Emercom of Russia in the subject of Russian Federation, an algorithm for allocating resources for the maintenance of equipment, an algorithm for allocating resources to stimulate personnel, an algorithm for planning and conducting purchases were developed. All the described algorithms become the basis for the model of resources allocating process for the maintenance of a territorial body of federal executive authorities on the example of the Main Directorate of Emercom of Russia for a subject of Russian Federation. The developed model takes into account the mechanisms of optimal resource allocation based on mathematical models of priorities and allows solving various tasks of optimizing resources according to the chosen strategy.
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Cummings, Leslie E., and Jerome J. Vallen. "Considerations for Microcomputing Resource Decisions In Hri Programs." Hospitality Education and Research Journal 12, no. 2 (1988): 37–46. http://dx.doi.org/10.1177/109634808801200205.
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HRI decision-makers charged with putting in place appropriate microcomputer resources face the challenges of assessing needs, identifying educational objectives, and appropriating and allocating resources. In this paper are outlined major considerations for each area, supported by interviews, a literature review, and the experience of the authors. Vallen has been dean of a major hospitality administration program for 21 years. Cummings is a former computer analyst for a large computer firm and, for the past four years, has served as computing coordinator within the above program. Key terms: computing, funding, educational objectives, resource allocation.
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Dhanalakshmi, B. K., K. C. Srikantaiah, and K. R. Venugopal. "Dynamic Computation of Threshold Value for Classifying Jobs in Cloud Computing for Efficient Resource Utilization." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4458–61. http://dx.doi.org/10.1166/jctn.2020.9097.
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Cloud computing is an instant use of resources and it is a trending technology in the field of computer science. Here, many jobs will be arriving continuously with different job size, at that point of time, allocating of resources for suitable virtual machines without allowing virtual machine to starving is a hindrance job. So, to avoid this hindrance, an algorithm Dynamic Computation of Threshold Value is proposed (DCTV) and based on the threshold value the jobs are classified in the initial stage, so this classification leads to allocation of resources precisely and efficient resource utilization. The experimental result shows that by using dynamic computation of threshold value the allocation of resource time is reduced and classification accuracy is improved compared to manual computation of threshold value.
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Amuji, Harrison O., Donatus E. Onwuegbuchunam, Geoffrey U. Ugwuanyim, et al. "A STOCHASTIC DYNAMIC PROGRAMMING MODEL OF POLICE RESOURCE ALLOCATION FOR CRIME CONTROL." Advances and Applications in Discrete Mathematics 42, no. 4 (2025): 401–14. https://doi.org/10.17654/0974165825026.
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In this paper, we developed a non-homogeneous Poisson process-based stochastic dynamic programming model for efficient allocation of police resources with precision and effective crime control. Another important model developed alongside the stochastic dynamic programming model was the optimal decision model for allocating police resources to regions with probabilities of intercepting crimes. The two developed models were applied to crime and logistics data from Area Command, Enugu for optimal resource allocation. From this work, we conclude that for effective crime prevention and control, Area Command should allocate ten (10) patrols to the five regions of interest in this order: to arrive at the optimal allocation of scarce resources for effective crime interception and control.
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Sharma, Neha, Lakshay Kumar, Namrata Dwivedi, Ashpinder Kaur, and Gagandeep Kaur. "Resource Allocation and Security Threat in Cloud Computing: A Survey." CGC International Journal of Contemporary Technology and Research 6, no. 2 (2024): 381–87. http://dx.doi.org/10.46860/cgcijctr.2024.06.10.381.
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Cloud computing is a cutting-edge technology with enormous commercial and enterprise potential. Apps and related data can be accessed from any location thanks to clouds. Companies can drastically lower the cost of their infrastructure by renting resources from the cloud for storage and other processing needs. They can also use pay-as-you-go application access available to the entire firm. Therefore, obtaining licenses for specific products is not necessary. However, allocating resources as efficiently as possible is one of the main challenges in cloud computing. Because the model is unique, resource allocation is done to lower its expenses. Meeting application and customer requirements and consumer needs presents additional issues in resource allocation. This study provides a detailed discussion of several resource allocation systems, security threats, and the difficulties they face. This work is anticipated to help researchers and cloud users overcome obstacles.
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Hou, Shoulu, Wei Ni, Ming Wang, Xiulei Liu, Qiang Tong, and Shiping Chen. "Bottleneck-Aware Resource Allocation for Service Processes." International Journal of Web Services Research 18, no. 3 (2021): 1–21. http://dx.doi.org/10.4018/ijwsr.2021070101.
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In 5G systems and beyond, traditional generic service models are no longer appropriate for highly customized and intelligent services. The process of reinventing service models involves allocating available resources, where the performance of service processes is determined by the activity node with the lowest service rate. This paper proposes a new bottleneck-aware resource allocation approach by formulating the resource allocation as a max-min problem. The approach can allocate resources proportional to the workload of each activity, which can guarantee that the service rates of activities within a process are equal or close-to-equal. Based on the business process simulator (i.e., BIMP) simulation results show that the approach is able to reduce the average cycle time and improve resource utilization, as compared to existing alternatives. The results also show that the approach can effectively mitigate the impact of bottleneck activity on the performance of service processes.