Fair Resource Allocation Research Articles

Study on the Economic Valuation of Water Resources and the Allocation of Rights and Interests in Water Resources

With the increasing scarcity and uneven distribution of water resources around the world, scientific assessment and rational allocation of water resources have become urgent tasks in today’s society. This study focuses on the assessment of the economic value of water resources and the allocation of water resources’ rights and interests, aiming to explore how to accurately assess the economic value of water resources and how to realize a reasonable and fair allocation of resources’ rights and interests among different stakeholders. The importance of water resources and their values in economic, ecological and social aspects are analyzed from the background of global water resources issues. Reveal the multidimensional value of water resources by integrating market and non-market valuation methods. The study further examines the existing equity allocation mechanisms, analyzes their strengths and weaknesses, and points out that the traditional mechanisms may lead to over-exploitation of resources and imbalance of equity. The systematic theoretical discussions and practical case studies provide new perspectives and methods for water resources management and policy making

NASLMRP: Design of a Negotiation Aware Service Level Agreement Model for Resource Provisioning in Cloud Environments

Cloud resource provisioning requires examining tasks, dependencies, deadlines, and capacity distribution. Scalability is hindered by incomplete or complex models. Comprehensive models with low-to-moderate QoS are unsuitable for real-time scenarios. This research proposes a Negotiation Aware SLA Model for Resource Provisioning in cloud deployments to address these challenges. In the proposed model, a task-level SLA maximizes resource allocation fairness and incorporates task dependency for correlated task types. This process's new tasks are processed by an efficient hierarchical task clustering process. Priority is assigned to each task. For efficient provisioning, an Elephant Herding Optimization (EHO) model allocates resources to these clusters based on task deadline and make-span levels. The EHO Model suggests a fitness function that shortens the make-span and raises deadline awareness. Q-Learning is used in the VM-aware negotiation framework for capacity tuning and task-shifting to post-process allocated tasks for faster task execution with minimal overhead. Because of these operations, the proposed model outperforms state-of-the-art models in heterogeneous cloud configurations and across multiple task types. The proposed model outperformed existing models in terms of make-span, deadline hit ratio, 9.2% lower computational cycles, 4.9% lower energy consumption, and 5.4% lower computational complexity, making it suitable for large-scale, real-time task scheduling.

The Effect of Distributive Justice, Procedural Justice, Interactional Justice on Engagement and Village Apparatus Performance in Indonesia

Indonesia is the third largest democracy in the world, in an effort to improve the quality of democracy, Law No. 23 of 2014 concerning Regional Government and Law No. 6 of 2014 concerning Villages were drafted. Development is expected to be carried out based on the authority of government organisations at the central level, provincial level, district level, sub-district level and village level. This study aims to investigate the effect of distributive justice, procedural justice, and interactional justice on village apparatus engagement, investigate the effect of distributive justice, procedural justice, and interactional justice on village apparatus performance, the effect of village apparatus engagement on village apparatus performance and the effect of distributive justice, procedural justice, and interactional justice on village apparatus performance through village apparatus engagement. This study uses the following research methods: the research sample totalled 340 respondents with non-probability sampling technique, model measurement using Confirmatory Factor Analysis (CFA) and structural test model using Stuctural Equation Model (SEM).The results showed that 1) distributive justice, procedural justice, interactional justice have a positive and significant influence on village apparatus attachment, 2) distributive justice, procedural justice, interactional justice have a positive and significant influence on village apparatus performance, 3) village apparatus engagementhas a positive and significant impact on village apparatus performance, 4) village apparatus engagementplays an important role as a mediator in influencing between distributive justice, procedural justice, interactional justice, and village apparatus performance. These findings suggest that an increase in justice within the village apparatus can strengthen the village apparatus' attachment, which in turn, improves its performance in carrying out the duties and responsibilities of the village apparatus.This research has important implications for village governance management. Village governments and other stakeholders may consider efforts to improve fairness in resource allocation and decision-making processes within the village apparatus. In addition, increasing the engagement of village apparatus members through the development of relationships among village apparatus members and ethical interactions may also improve their performance.

Intelligent Hierarchical Admission Control for Low-Earth Orbit Satellites Based on Deep Reinforcement Learning.

Low-Earth orbit (LEO) satellites have limited on-board resources, user terminals are unevenly distributed in the constantly changing coverage area, and the service requirements vary significantly. It is urgent to optimize resource allocation under the constraint of limited satellite spectrum resources and ensure the fairness of service admission control. Therefore, we propose an intelligent hierarchical admission control (IHAC) strategy based on deep reinforcement learning (DRL). This strategy combines the deep deterministic policy gradient (DDPG) and the deep Q network (DQN) intelligent algorithm to construct upper and lower hierarchical resource allocation and admission control frameworks. The upper controller considers the state features of each ground zone and satellite resources from a global perspective, and determines the beam resource allocation ratio of each ground zone. The lower controller formulates the admission control policy based on the decision of the upper controller and the detailed information of the users' services. At the same time, a designed reward and punishment mechanism is used to optimize the decisions of the upper and lower controllers. The fairness of users' services admissions in each ground zone is achieved as far as possible while ensuring the reasonable allocation of beam resources among zones. Finally, online decision-making and offline learning were combined, so that the controller could make full use of a large number of historical data to learn and generate intelligent strategies with stronger adaptive ability while interacting with the network environment in real time. A large number of simulation results show that IHAC has better performance in terms of a successful service admission rate, service drop rate, and fair resource allocation. Among them, the number of accepted services increased by 20.36% on average, the packet loss rate decreased by 17.56% on average, and the resource fairness increased by 17.16% on average.

Cooperative optimization techniques in distributed MAC protocols – a survey

Purpose Cross-layer approach in media access control (MAC) layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in wireless sensor network (WSN) and Internet of Things (IoT) applications. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Game theory optimization for distributed may increase the network performance. The purpose of this study is to survey the various operations that can be carried out using distributive and adaptive MAC protocol. Hill climbing distributed MAC does not need a central coordination system and location-based transmission with neighbor awareness reduces transmission power. Design/methodology/approach Distributed MAC in wireless networks is used to address the challenges like network lifetime, reduced energy consumption and for improving delay performance. In this paper, a survey is made on various cooperative communications in MAC protocols, optimization techniques used to improve MAC performance in various applications and mathematical approaches involved in game theory optimization for MAC protocol. Findings Spatial reuse of channel improved by 3%–29%, and multichannel improves throughput by 8% using distributed MAC protocol. Nash equilibrium is found to perform well, which focuses on energy utility in the network by individual players. Fuzzy logic improves channel selection by 17% and secondary users’ involvement by 8%. Cross-layer approach in MAC layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in WSN and IoT applications. Cross-layer and cooperative communication give energy savings of 27% and reduces hop distance by 4.7%. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Research limitations/implications Other optimization techniques can be applied for WSN to analyze the performance. Practical implications Game theory optimization for distributed may increase the network performance. Optimal cuckoo search improves throughput by 90% and reduces delay by 91%. Stochastic approaches detect 80% attacks even in 90% malicious nodes. Social implications Channel allocations in centralized or static manner must be based on traffic demands whether dynamic traffic or fluctuated traffic. Usage of multimedia devices also increased which in turn increased the demand for high throughput. Cochannel interference keep on changing or mitigations occur which can be handled by proper resource allocations. Network survival is by efficient usage of valid patis in the network by avoiding transmission failures and time slots’ effective usage. Originality/value Literature survey is carried out to find the methods which give better performance.

Data-driven fair resource allocation for novel emerging epidemics: a COVID-19 Convalescent Plasma case study

Epidemics are a serious public health threat, and the resources for mitigating their effects are typically limited. Decision-makers face challenges in forecasting the supply and demand for these resources as prior information about the disease is often not available, the behaviour of the disease can periodically change (either naturally or as a result of public health policies) and can differ by geographical region. In this work, we discuss a model that is suitable for short-term real-time supply and demand forecasting during emerging outbreaks. We consider a case study of demand forecasting and allocating scarce quantities of COVID-19 Convalescent Plasma (CCP) in an international multi-site Randomized Controlled Trial (RCT) involving multiple hospital hubs across Canada (excluding Québec). We propose a data-driven mixed-integer programming (MIP) resource allocation model that assigns available resources to maximize a notion of fairness among the resource-demanding entities. Numerical results from applying our MIP model to the case study suggest that our approach can help balance the supply and demand of limited products such as CCP and minimize the unmet demand ratios of the demand entities. We analyse the sensitivity of our model to different allocation settings and show that our model assigns equitable allocations across the entities.

A novel machine learning-based framework for channel bandwidth allocation and optimization in distributed computing environments

Efficient utilization of network resources, particularly channel bandwidth allocation, is critical for optimizing the overall system performance and ensuring fair resource allocation among multiple distributed computing nodes. Traditional methods for channel bandwidth allocation, based on fixed allocation schemes or static heuristics, often need more adaptability to dynamic changes in the network and may not fully exploit the system’s potential. To address these limitations, we employ reinforcement learning algorithms to learn optimal channel allocation policies by intermingling with the environment and getting feedback on the outcomes of their actions. This allows devices to adapt to changing network conditions and optimize resource usage. Our proposed framework is experimentally evaluated through simulation experiments. The results demonstrate that the framework consistently achieves higher system throughput than conventional static allocation methods and state-of-the-art bandwidth allocation techniques. It also exhibits lower latency values, indicating faster data transmission and reduced communication delays. Additionally, the hybrid approach shows improved resource utilization efficiency, efficiently leveraging the strengths of both Q-learning and reinforcement learning for optimized resource allocation and management.

How reforms hamper priority-setting in health care: an interview study with local decision-makers in London.

The fair allocation of scarce resources for health remains a salient topic in health care systems. Approaches for setting priorities in an equitable manner include technical ones based on health economic analyses, and ethical ones based on procedural justice. Knowledge on real-world factors that influence prioritisation at a local level, however, remains sparse. This article contributes to the empirical literature on priority-setting at the meso level by exploring how health care planners make decisions on which services to fund and to prioritise, and to what extent they consider principles of fair priority-setting. It presents the findings of an interview study with commissioners and stakeholders in South London between 2017 and 2018. Interviewees considered principles of fair prioritisation such as transparency and accountability important for offering guidance. However, the data show that in practice the adherence to principles is hampered by the difficulty of conceptualising and operationalising principles on the one hand, and the political realities in relation to reform processes on the other. To address this challenge, we apply insights from the policy and political sciences and propose a set of considerations by which current frameworks of priority-setting might be adapted to better incorporate issues of context and politics.

Healthcare resource allocation decisions and non-emergency treatments in the aftermath of Covid-19 pandemic. How should children with chronic illness feature in prioritisation processes?

Background: In the aftermath of the Coronavirus disease 2019 (Covid-19) pandemic, allocation of non-urgent medical interventions is a persistent ethical challenge as health systems currently face an unprecedented backlog of patients requiring treatment. Difficult decisions must be made that prioritise certain patients over others. Ethical resource allocation requires that the needs of all patients are considered properly, but at present there is no guidance that can help support such decision-making which explicitly considers the needs of children with chronic and complex conditions. Methods: This paper reviews the NHS guidance for priorities and operational planning and examines how the needs of children with chronic illness are addressed in NHS objectives for restoring services and meeting elective care demands. Results: The usual criteria for prioritisation featured in the NHS guidance fail to account for the distinct needs of children with chronic illnesses and fail to match more general considerations of what constitutes fair resource allocation decisions. To address this issue, two considerations, namely 'protecting age-related opportunity' and 'recognising complexity of care,' are proposed as additions to the existing approach. Conclusion: By providing a broader conception of needs, these criteria address inefficiencies of the current guidance and relevant ethical frameworks and help to embed a currently missing children-related ethical approach to healthcare policy making in general.

A bipartite graph based proportional fair scheduling strategy to improve throughput with multiple resource blocks

<p>The fifth-generation wireless communication is expected to provide a huge amount of capacity to cater to the need of an increasing number of mobile consumers, which can be satisfied by device-to-device (D2D) communication. Reusing the cellular user’s resources in an efficient manner helps to increase the spectrum efficiency of the network but it leads to severe interference. The important point in reusing cellular user resources is that D2D communication should not affect the cellular user’s efficiency. After achieving this requirement, the focus is now turned toward the allocation of resources to D2D communication. This resource allocation strategy is to be designed in such a way that it will not affect communication among the cellular user (CU). This scheme improves various performance objectives. This paper aims at designing a proportional fair resource allocation algorithm based on the bipartite graph which maintains the quality of service (QoS) of CUs while providing D2D communication. This algorithm can be merged with any other scheme of resource allocation for improving QoS and adopting changing channels. In this scheme, a D2D pair can be allocated with one or more than one resource blocks. The MATLAB simulations analyze the performance of the proposed scheme.</p>

A Novel Method of Teaching Quality Monitoring and Evaluation from the Perspective of Educational Balance

Exploring the problem of teaching quality monitoring and evaluation from the perspective of educational balance can help educators and policy makers discover educational inequality problems, thereby promoting fair allocation of educational resources and opportunities. In light of this, this study attempts to research teaching quality monitoring and evaluation from the perspective of educational balance. At first, the mechanism of promoting educational balance by information technology was expounded, and the principle and necessity of a comprehensive assessment based on the evaluation results of different evaluators were explained. Then, this study innovatively introduced the whitening weight function (WWF)-grey evaluation model into the conventional grey clustering evaluation, calculated the unit comprehensive clustering coefficients of the indexes of teaching quality evaluation from the perspective of educational balance, and gave fair, accurate, and effective evaluations. After that, the grey GM (1,1) model was applied to the prediction of teaching quality improvement effect, the future trend of teaching quality changes was predicted based on history data, and the results provided references for teaching reform and educational decision-making. At last, experimental results verified the validity of the proposed method.

RIS-Assisted Fixed NOMA: Outage Probability Analysis and Transmit Power Optimization

Reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) has the ability to overcome the challenges of the wireless environment like random fluctuations, shadowing, and mobility in an energy efficient way when compared to multiple input-multiple output (MIMO)-NOMA systems. The NOMA system can deliver controlled channel gains, improved coverage, increased energy efficiency, and enhanced fairness in resource allocation with the help of RIS. RIS-assisted NOMA will be one of the primary potential components of sixth-generation (6G) networks, due to its appealing advantages. The analytical outage probability expressions for smart RIS-assisted fixed NOMA (FNOMA) are derived in this paper, taking into account the instances of RIS as a smart reflector (SR) and an access point (AP). The analytical and simulation findings are found to be extremely comparable. In order to effectively maximize the sum capacity, the formulas for optimal powers to be assigned for a two-user case are also established. According to simulations, RIS-assisted FNOMA surpasses FNOMA in terms of outage and sum capacity. With the aid of RIS and the optimal power assignment, RIS-AP-FNOMA offers ≈62% improvement in sum capacity over the FNOMA system for a signal-to-noise ratio (SNR) of 10 dB and 32 elements in RIS. A significant improvement is also brought about by the increase in reflective elements.

Vivace-Distributed: A novel congestion control mechanism for JointCloud environments

As the customer base of cloud computing services continues to grow, the demand for providing cloud resources in an economic manner has become increasingly important. To incorporate multiple clouds, JointCloud has been proposed for sharing cloud services. However, efficient communication among clouds is essential to enable resource sharing in JointCloud, which poses a critical challenge. The existing congestion control mechanisms for JointCloud have been founded to be inadequate in sharing network resources effectively and fairly, resulting in performance shortcomings. The maximum achievable transmission rate is limited to only 50% of the bandwidth, and the minimum and maximum transmission rates for different clouds in JointCloud may have a difference of up to ten times in magnitude.We propose a novel distributed congestion control mechanism for the JointCloud environment, which leverages ideas from rich literature on distributed online learning. The proposed mechanism operates in a distributed manner, with an aggregative variable representing the network condition integrated into each sender’s utility function. Our theoretical analyses establish that the proposed mechanism achieves a unique Nash equilibrium, enabling fair resource allocation among all senders. Furthermore, our simulation evaluations demonstrate that the proposed mechanism outperforms the existing congestion control mechanism in JointCloud environment by achieving a 20% higher throughput in the presence of at least 1% random loss, a 50% higher throughput for short TCP flows, and a throughput ratio of 1 when sharing bandwidth with conventional mechanisms, which indicates a perfect TCP friendliness towards other mechanisms. In summary, our proposed congestion control mechanism is the first to work in a distributed manner, which improves the efficiency and fairness of the congestion control mechanism. The proposed mechanism has the potential to significantly enhance the performance of communication among clouds, making a noteworthy contribution to cloud resource sharing.

Translating theories of justice into a practice model for triage of scarce intensive care resources during a pandemic.

During the COVID-19 pandemic, national triage guidelines were developed to address the anticipated shortage of life-saving resources, should ICU capacities be overloaded. Rationing and triage imply that in addition to individual patient interests, interests of population health have to be integrated. The transfer of theoretical and empirical knowledge into feasible and useful practice models and their implementation in clinical settings need to be improved. This paper analyzes how triage protocols could translate abstract theories of distributive justice into concrete material and procedural criteria for rationing intensive care resources during a pandemic. We reconstruct the development and implementation of a rationing protocol at a German university hospital: describing the ethical challenge of triage, clarifying the aspirational norms, and summarizing specific norms of fair triage and allocation for developing an institutional policy and practice model and implementing it. We reflect on how critical topics are seen by clinicians and what helped manage the perceived burdens of the triage dilemma. We analyze what can be learned from this debate regarding the difficult issues around triage protocols and their potential implementation into clinical settings. Analyzing the ought-to-is gap of triage, integrating abstract ethical principles into practical concepts, and evaluating those should clarify the benefits and risks of different allocation options. We seek to inform debates on triage concepts and policies to ensure the best possible treatment and fair allocation of resources as well as to help protect patients and professionals in worst-case scenarios.

Research on equity of medical resource allocation in Yangtze River Economic Belt under healthy China strategy.

This study aimed to assess the fairness of medical resource allocation in the Yangtze River Economic Belt, based on the Healthy China strategy. It aimed to identify the issues with resource allocation fairness and provide optimization suggestions. To assess the allocation fairness from a geographical population perspective, the study used the Health Resource Concentration and Entropy Weight TOPSIS methods. Additionally, the study analyzed the allocation fairness from an economic level angle, using the Concentration Curve and Concentration Index. The study found that the downstream area had higher resource allocation fairness than the midstream and upstream areas. The middle reaches had more resources than the upper and lower reaches, based on population concentration. The Entropy Weight TOPSIS method found that Shanghai, Zhejiang, Chongqing, and Jiangsu had the highest comprehensive score index of agglomeration. Furthermore, from 2013 to 2019, the fairness of medical resource distribution gradually improved for different economic levels. Government health expenditure and medical beds were distributed more equitably, while general practitioners had the highest level of unfairness. However, except for medical and health institutions, traditional Chinese medicine institutions, and primary health institutions, other medical resources were mostly distributed to areas with better economic conditions. The study found that the fairness of medical resource allocation in the Yangtze River Economic Belt varied greatly based on geographical population distribution, with inadequate spatial accessibility and service accessibility. Although the fairness of distribution based on economic levels improved over time, medical resources were still concentrated in better economic areas. The study recommends improving regional coordinated development to enhance the fairness of medical resource allocation in the Yangtze River Economic Belt.

Differentially Private Fair Division

Fairness and privacy are two important concerns in social decision-making processes such as resource allocation. We study privacy in the fair allocation of indivisible resources using the well-established framework of differential privacy. We present algorithms for approximate envy-freeness and proportionality when two instances are considered to be adjacent if they differ only on the utility of a single agent for a single item. On the other hand, we provide strong negative results for both fairness criteria when the adjacency notion allows the entire utility function of a single agent to change.

Enabling Long-term Fairness in Dynamic Resource Allocation

We study the fairness of dynamic resource allocation problem under the α-fairness criterion. We recognize two different fairness objectives that naturally arise in this problem: the well-understood slot-fairness objective that aims to ensure fairness at every timeslot, and the less explored horizon-fairness objective that aims to ensure fairness across utilities accumulated over a time horizon. We argue that horizon-fairness comes at a lower price in terms of social welfare. We study horizon-fairness with the regret as a performance metric and show that vanishing regret cannot be achieved in presence of an unrestricted adversary. We propose restrictions on the adversary's capabilities corresponding to realistic scenarios and an online policy that indeed guarantees vanishing regret under these restrictions.

Rationing in healthcare-a scoping review.

Healthcare rationing has been the subject of numerous debates and concerns in the field of health economics in recent years. It is a concept which refers to the allocation of scarce healthcare resources and involves the use of different approaches to the delivery of health services and patient care. Regardless of the approach used, healthcare rationing fundamentally involves withholding potentially beneficial programs and/or treatments from certain people. As the demands placed on health services continue to rise and with that significant increases to the cost, healthcare rationing has become increasingly popular and is deemed necessary for the delivery of affordable, patient-care services. However, public discourse on this issue has largely been centered on ethical considerations with less focus on economic rationality. Establishing the economic rationality of healthcare rationing is essential in healthcare decision-making and consideration of its adoption by healthcare authorities and organizations. This scoping review of seven articles demonstrates that the economic rationality of healthcare rationing is the scarcity of healthcare resources amidst increased demand and costs. Therefore, supply, demand, and benefits are at the core of healthcare rationing practices and influence decisions on its suitability. Given the increased costs of care and resource scarcity, healthcare rationing is a suitable practice towards ensuring healthcare resources are allocated to people in a rational, equitable, and cost-effective manner. The rising costs and demands for care place significant pressure on healthcare authorities to identify suitable strategies for the allocation of healthcare resources. Healthcare rationing as a priority-setting strategy would support healthcare authorities identify mechanisms to allocate scarce resources in a cost-effective manner. When used in the context of a priority-setting approach, healthcare rationing helps healthcare organizations and practitioners to ensure that patient populations achieve maximum benefits at reasonable costs. It represents a fair allocation of healthcare resources to all populations, especially in low-income settings.

Intelligent-Slicing: An AI-Assisted Network Slicing Framework for 5G-and-Beyond Networks

5G-and-beyond networks are designed to fulfill the communication and computation requirements of various industries, which requires not only transporting the data, but also processing them to meet/address diverse key performance indicators (KPIs). Network Function Virtualization (NFV) has emerged to enable this vision by: (i) collecting the requirements of diverse services, using graphs of Virtual Network Functions (VNFs); and (ii) mapping these requirements into network management decisions. Because of the latter, we need to efficiently allocate computing and network resources to support the desired services, and because of the former such decisions must be jointly optimized considering all KPIs associated with supported services. Thus, this paper proposes an optimized, intelligent network slicing framework to maintain a high performance of network operation by supporting diverse and heterogeneous services, while meeting new KPIs, e.g., reliability, energy consumption, and data quality. Different from the existing works, which are mainly designed considering traditional metrics like throughput and latency, we present a novel methodology and resource allocation schemes that enable high-quality selection of radio points of access, VNF placement and data routing, as well as data compression ratios, from the end users to the cloud. Our results depict the efficiency of the proposed framework in enhancing the network performance when compared to baseline approaches that consider partial network view or fair resource allocation.

Partially Oblivious Congestion Control for the Internet via Reinforcement Learning

Despite years of research on transport protocols, the tussle between in-network and end-to-end congestion control has not been solved. This debate is due to the variance of conditions and assumptions in different network scenarios, e.g., cellular versus data center networks. Recently, the community has proposed a few transport protocols driven by machine learning, nonetheless limited to end-to-end approaches. In this paper, we present Owl, a transport protocol based on reinforcement learning, whose goal is to select the proper congestion window learning from end-to-end features and network signals, when available. We show that our solution converges to a fair resource allocation after the learning overhead. Our kernel implementation, deployed over emulated and large scale virtual network testbeds, outperforms all benchmark solutions based on end-to-end or in-network congestion control.