Resource Allocation Mechanism Research Articles

Три волны научной мобилизации на Востоке России (1930-е – начало 1950-х годов)

The research focuses on the phenomenon of formation and subsequent transformation of the social institution of science and the corporate unity of academia in the regional aspect in relation to the eastern regions of Russia during Stalin’s era. The most important principle of the study was the adaptation to the goals of the historic and scientific analysis of the basic concept of social mobilization, which includes such backbone elements as doctrinal imperatives, state priorities, campaign nature, centralized and administrative-distributive mechanism of resource allocation for scientific research, control over scientific teams and groups of scholars that combined incentives and preferences with obligation and compulsion.
 It has been established that Stalin’s era featured two divergent trends that influenced the transformation of the domestic social institution of science: the first resulted in the possibilities of self-development of science as a system of knowledge, and the second acted as a derivative of priorities and needs of government institutions for the scientific and technical support of strategic projects. In the 1930s, a mechanism of coordination of intra-scientific and state interests and needs was formed and consolidated on the basis of embedding and development of mobilization practices in scientific activity (the formation and reinforcement of hierarchy, directivity, norms and practices of conflict consolidation, motivational mechanisms of academic work, etc.).

Developing a Rural Healthcare System

Background: Healthcare related welfare is a matter of resource allocation. In modern market economies, redistribution in Western societies is accomplished through state welfare systems to balance the inequality of market resource allocation. However, resource allocation in socialist countries has its own peculiarities. And the relationship between market transformation (social transformation) and resource allocation methods in post-socialist countries is an important academic topic. This paper examines the changes in the rural cooperative medical system（RCMS）in China in order to explore the changes in rural cooperative healthcare governance mechanisms during recent social transformation. Objectives: The main purpose of this paper is to place the rural cooperative health care system（RCMS）in a macroeconomic-political context and to compare the changes in cooperative health care resource allocation mechanisms across time. Methods: The article undertakes a narrative literature review approach and method. The review used the following sources: Web of Science and PubMed. The search used the keywords" rural cooperative medical care system", "community benefits", " public service-based welfare" and "governance",and limited to articles published between January 2019 and June 2022. The selection of articles was based on the inclusion/exclusion criteria (keywords defined and time span of publication). Results: From collectivized cooperative medical care System（RCMS） to today's New Rural Cooperative Medical System（NRCMS), its governance mechanism has changed and the nature of welfare has changed with it. It has also changed from community welfare, where organized grassroots communities supply medical services, to government-led public service-based welfare. Main Contribution to Evidence-Based Practice: The paper shows the process of change in China's rural cooperative healthcarel system（RCMS), mainly analyzing the change of healthcare welfare governance mechanism. Thus ,it provides a reference for governmental health policies development and for international researchers.

Performance of Fuzzy Inference System for Adaptive Resource Allocation in C-V2X Networks

Mode 4 of 3GPP Cellular Vehicle-to-Everything (C-V2X) uses a new Sensing-Based Semi-Persistent Scheduling (SB-SPS) algorithm to manage its radio resources. SB-SPS applies a probabilistic approach to provide the resource allocation in the system. The resource keep probability (Prk) variable plays an essential role in the resource allocation mechanism. Most of the previous works used a fixed Prk value. However, the Packet Delivery Ratio (PDR) can be improved by adapting the optimal Prk value. Hence, we propose a Fuzzy Inference System (FIS) with two inputs, distance, and Channel State Information (CSI) to determine the suitable Prk. The simulation results show that the proposed FIS method outperforms the other methods for sparse and congested road scenarios, with total numbers of vehicles at 200 and 400, respectively.

A Truthful Mechanism for Multibase Station Resource Allocation in Metaverse Digital Twin Framework

The concept of the metaverse has gained increasing attention in recent years, and the development of various new technologies, including digital twin technology, has made it possible to see the metaverse coming to pass. Many academics have begun to investigate various problems after realizing the importance of digital twin technology in building the metaverse. However, when utilizing digital twin technology to construct a metaverse, there remains limited research on how to allocate multibase station resources. This research translates a multibase station wireless resource allocation problem into an integer linear programming constraint model when virtual service providers construct a metaverse. In addition, the optimal VCG reverse auction (OPT-VCGRA) mechanism is designed to maximize social welfare and solve the problem of IoT devices competing for base station wireless resources. Specifically, the problem of the optimal allocation of wireless channel resources and payment rule based on the Vickrey–Clarke–Groves mechanism is solved to achieve optimal allocation and calculation of payment prices. Since the optimal allocation problem is NP-hard, this paper also designs a metaverse digital twin resource allocation and pricing (MDTRAP) mechanism based on monotonic allocation and key value theory. The mechanism sends the resource allocation results of multiple base stations to IoT devices and calculates the price payment when building a metaverse in the real world. This paper shows that both auction mechanisms have incentive compatibility and individual rationality properties. Through experiments, this paper compares the two mechanisms in terms of social welfare, the number of winners, and the overall payment. The MDTRAP mechanism performs similarly to the OPT-VCGRA mechanism in terms of social welfare, the number of winners, and channel utilization but is far superior to the OPT-VCGRA mechanism in terms of execution time and total payment. The trustful experiment also verified the truthfulness of the MDTRAP mechanism. The experimental results show that the MDTRAP mechanism can be used to solve the resource allocation problem of multiple base stations to IoT devices when building a metaverse in the real world and can effectively maximize social welfare.

An Efficient Multi-Dimensional Resource Allocation Mechanism for Beam-Hopping in LEO Satellite Network

Low Earth Orbit (LEO) satellite communication networks have become an important means to provide internet access services for areas with limited infrastructure. Compared with the Geostationary Earth Orbit (GEO) satellites, the LEO satellites have limited on-board communication caching and calculating resources. Furthermore, the distribution of traffic requests is dynamically changing and uneven due to the relative movement between the LEO satellites and the ground. Therefore, how to schedule the multi-dimensional resources is an important issue for the LEO satellite communication networks. Beam-hopping is an efficient approach to improve the resource utilization by dynamically allocating time, power, and frequency according to the traffic requests. This paper proposes an efficient multi-dimensional resource allocation mechanism for beam-hopping in LEO satellite networks, which simultaneously satisfies the GEO interference avoidance. First, we construct the beam-hopping model of LEO satellites, and formulate the resource optimization problem. Second, we provide the weighted greedy strategy to determine the illumination pattern. In order to reduce the search space, the cells are clustered to non-interference clusters. Then, an improved genetic algorithm is provided to jointly allocate the communication resources. Finally, we construct various simulations to evaluate our proposed mechanism. Compared with the random-BH, polling-BH and traditional genetic algorithm, our algorithm achieves better performance in terms of both system throughput, access success rate, average delay and fairness between cells. The performance improvement is more significant in scenarios where traffic demand is unevenly distributed.

Differences of resource allocation to active and passive states in visual working memory.

Scholars have long sought to understand the separation between the active and passive states in visual working memory. Results of recent behavioral studies have provided insight into the independence of storage resources in these two states. To explore how humans distribute these resources in the active and passive states in visual working memory, we adopted the classic double-retro-cue paradigm combined with a continuous reported color wheel to ascertain whether the precision of representations maintained in active and passive states are adjustable according to the frequency of spatial cues. The results showed that two distinct resource allocation mechanisms exist in these two states beyond traditional visual working memory theory and provide further support for the separation hypothesis.

Robotic Edge Resource Allocation for Agricultural Cyber-Physical System

Cloud-aided robots are increasingly adopted in realizing Cyber-Physical-Social System (CPSS) to reduce human efforts and enhance the system performance with diverse robotic applications. However, the multi-hop distance from robot to cloud data centre elevates the data transfer delay that often inhibits the deadline-satisfied task execution of robotic applications. Thanks to edge computing, this issue can be well addressed by harnessing computing resources in proximity of data sources. As edge resources are constrained in energy and processing capacity, sharing resources among the tasks of multiple robotic applications is critical to ensure. Therefore, in this paper, we develop a congestion game-theoretic robotic edge resource allocation mechanism for CPSS, which not only maintains the Quality-of-Service (QoS) by meeting task completion deadlines but also satisfies the energy constraints of resources. Here, Agriculture 4.0 is considered as a use case for the proposed mechanism which can be extended for other domains. Nevertheless, the performance evaluation of proposed mechanism is conducted in an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iFogSim</i> simulated edge computing environment. In comparison with existing greedy, heuristic, and evolutionary benchmarks, our mechanism is proven to offer overall 20% improvement in deadline satisfaction, energy consumption, and resource utilization.

Dynamic Virtual Resource Allocation Mechanism for Survivable Services in Emerging NFV-Enabled Vehicular Networks

Vehicular ad-hoc network (VANET) is an emerging aspect of the 5G vertical application. Network function virtualization (NFV) is the key enabling technology of 5G and beyond 5G (B5G) networks. In NFV-enabled vehicular and 5G networks, all underlying nodes (e.g. vehicular, edge, core) can be completely virtualized and easy to be managed and allocated. Network service providers can implement each dynamically requested virtual network service (VNS), having arbitrary topology and customized resource demands, on top of the NFV-enabled networks. However, network elements (e.g. nodes and links) may come into failures accidentally. Consequently, it will lead to the performance degradation of implemented VNSs that run on top of the failed network elements. It is vital to guarantee the survivable services even though the network elements fail accidentally. Therefore, we propose the dynamic virtual resource allocation mechanism in this paper. Firstly, we introduce the business model and formulate the dynamic virtual resource allocation in NFV-enabled networks. Secondly, we detail all modules of our proposed mechanism. Especially, the initial resource allocation and re-allocation modules of achieving the survivable network services are detailed. Finally, we execute the comprehensive simulations by comparing with the typical virtual resource allocation mechanisms. The simulation results are discussed so as to highlight the merits of the proposed mechanism.

MARL based resource allocation scheme leveraging vehicular cloudlet in automotive-industry 5.0

Automotive-Industry 5.0 will use Beyond Fifth-Generation (B5G) communications to provide robust, abundant computation resources and energy-efficient data sharing among various Intelligent Transportation System (ITS) entities. Based on the vehicle communication network, the Internet of Vehicles (IoV) is created, where vehicles’ resources, including processing, storage, sensing, and communication units, can be leveraged to construct Vehicular Cloudlet (VC) to realize resource sharing. As Connected and Autonomous Vehicles (CAV) onboard computing is becoming more potent, VC resources (comprising stationary and moving vehicles’ idle resources) seems a promising solution to tackle the incessant computing requirements of vehicles. Furthermore, such spare computing resources can significantly reduce task requests’ delay and transmission costs. In order to maximize the utility of task requests in the system under the maximum time constraint, this paper proposes a Secondary Resource Allocation (SRA) mechanism based on a dual time scale. The request service process is regarded as M/M/1 queuing model and considers each task request in the same time slot as an agent. A Partially Observable Markov Decision Process (POMDP) is constructed and combined with the Multi-Agent Reinforcement Learning (MARL) algorithm known as QMix, which exploits the overall vehicle state and queue state to reach effective computing resource allocation decisions. There are two main performance metrics: the system’s total utility and task completion rate. Simulation results reveal that the task completion rate is increased by 13%. Furthermore, compared with the deep deterministic policy optimization method, our proposed algorithm can improve the overall utility value by 70% and the task completion rate by 6%.

Rectifying inequities in resource collection in young children.

Numerous studies have documented children's understanding of fairness through their ability to rectify inequities when distributing resources to others. Understanding fairness, however, involves more than just applying norms of equity when distributing resources. Children must also navigate situations in which resources are collected from them for a common good. The developmental origins and the trajectory of equitable resource collection are understudied in the literature on children's prosocial behavior. Experiment 1 presented 4- to 8-year-olds (N=130) with characters who started with different amounts of resources that were available for both personal use and a group project in school. Participants were asked how a teacher should fairly collect resources from the two characters, contrasting the teacher taking the same amount of resources from each individual (preserving the inequity) or leaving each individual with the same amount of resources (rectifying the inequity). Four- and 5-year-olds responded randomly; 6- to 8-year-olds preferred to rectify the inequity. Experiment 2 reproduced this finding on a new group of 5- to 7-year-olds (N=69), eliciting justifications for their choice. Justifications in terms of fairness related to equitable choices. Experiment 3 reproduced this finding again in a new group of 5- to 7-year-olds (N=77), contrasting children's preference for equitable resource collection with that of resource distribution. Children were more likely to rectify an inequity when collecting resources than when distributing resources to individuals who started with an inequity. This difference was driven more by the younger children in the sample. We discuss potential mechanisms for these findings in terms of children's developing concepts of fairness. RESEARCH HIGHLIGHTS: Across three experiments, children developed preferences for equitable collection of resources by age 6. Preferences for equitable resource collection were more likely to be justified by appealing to concepts of fairness. Although preferences for equitable resource collection emerged slightly before equitable resource distribution, these data suggest children develop a unified mechanism for prosocial resource allocation.

Unintended consequences of heavy air pollution control: efficiency losses, resource misallocation, and firm innovation in China.

Disentangling the heterogeneous effects of environmental regulations on firms' productivity and reducing the destructive effects of policies are important for achieving the goal of sustainable development. However, there is insufficient discussion of the micro-mechanisms of regulation. This study takes advantage of the good exogenous impact of China's first action plan, the Atmosphere Ten Articles, which has been formulated and implemented to solve prominent environmental problems, and constructs a difference-in-difference model based on the data of listed companies from 2012 to 2019. Results show that the policy generally has a significantly negative impact on the companies' TFP. This finding proved to be valid after a series of robustness tests, such as replacing the explained variables, eliminating the impact of penecontemporaneous policies, using propensity score matching, and applying instrumental variable methods. Due to the resource allocation mechanism, the policy undermines the companies' TFP, especially those in PM2.5 regulatory areas and state-owned companies. However, the strict emission reduction constraint instead pushes firms to innovate, and as a result, the policy's innovation mechanism promotes firms' productivity, which eventually offsets the negative effects arising from resource allocation distortions.

Situation-Aware Orchestration of Resource Allocation and Task Scheduling for Collaborative Rendering in IoT Visualization

Three dimensional rendering enabled IoT visualization provides an immersive operation view across large physical environments by contextually aggregating and visualizing numerous data streams from various systems. The massive resource demand to support real-time and high-quality rendering services can be fulfilled by collaborative rendering among resource-constrained wireless devices. To deliver reliable performance, one main challenge is to achieve reliable and sustainable collaboration in a dynamic IoT system with heterogeneous resource capacity and changing user intent. To overcome such issues, we propose a situation-aware orchestration mechanism of resource allocation and task scheduling. The proposed technique achieves objective-driven exploration of collaboration opportunity among heterogeneous resource by three steps: recognizing dynamic condition of resource and task, including resource reliability and computational demand; understanding the mutual impact of resource condition and task performance in the aspect of energy consumption and latency; precise alignment of resource capacity and task demands via a redundant task scheduling scheme. The proposed task scheduling problem is formulated as an optimization model with the objective of maximizing collaboration utility. A genetic algorithm (GA) with adaptive mating-distance is designed to tackle the NP-hard problem, which improves the optimal solution in simulation by approximately 25% and 30% compared to conventional GA and Greedy algorithm, respectively.

Multiple linear regression-based energy-aware resource allocation in the Fog computing environment

Fog computing is a promising computing paradigm for time-sensitive Internet of Things (IoT) applications. It helps to process data close to the users, in order to deliver faster processing outcomes than the Cloud; it also helps to reduce network traffic. The computation environment in the Fog computing is highly dynamic and most of the Fog devices are battery powered hence the chances of application failure is high which leads to delaying the application outcome. On the other hand, if we rerun the application in other devices after the failure it will not comply with time-sensitiveness. To solve this problem, we need to run applications in an energy-efficient manner which is a challenging task due to the dynamic nature of Fog computing environment. It is required to schedule application in such a way that the application should not fail due to the unavailability of energy. In this paper, we propose a multiple linear, regression-based resource allocation mechanism to run applications in an energy-aware manner in the Fog computing environment to minimise failures due to energy constraint. Prior works lack of energy-aware application execution considering dynamism of Fog environment. Hence, we propose A multiple linear regression-based approach which can achieve such objectives. We present a sustainable energy-aware framework and algorithm which execute applications in Fog environment in an energy-aware manner. The trade-off between energy-efficient allocation and application execution time has been investigated and shown to have a minimum negative impact on the system for energy-aware allocation. We compared our proposed method with existing approaches. Our proposed approach minimises the delay and processing by 20%, and 17% compared with the existing one. Furthermore, SLA violation decrease by 57% for the proposed energy-aware allocation.

Priority-aware traffic routing and resource allocation mechanism for space-division multiplexing elastic optical networks

In recent years, the introduction of new technologies and applications connected to the Internet has demonstrated the physical incapacity of current optical backbone networks in providing resources soon. One of the main proposals to deal with this problem today is space-division multiplexing elastic optical networks. However, to transport the massive amount of data that is being generated today, these backbone networks should provide efficient resilience mechanisms. We propose a routing algorithm with a protection mechanism that considers traffic priority for elastic optical networks space-division multiplexing. The results obtained demonstrate the proposed algorithm’s high efficiency in establishing resilient high priority connections compared with recently proposed algorithms in the literature.

Impact of inter-provincial power resource allocation on enterprise production behavior from a multi-scale correlation perspective

The endowment of production factors is an important factor that restricts the choice of enterprise operation strategy, further affecting the regional industrial structure and the quality of economic development. Due to the fact of the divided power market and the administrative system with a spatial nesting structure in China, the power resource allocation policies have an important and heterogeneous impact on enterprise production behavior. Therefore, this study systematically explores the cross-level influence mechanism of regional power resource allocation on enterprise production efficiency, power efficiency, and location transfer with a hierarchical linear model (HLM) and the large sample data of 239,483 power-consuming enterprises in China's manufacturing industry from 2008 to 2015. The results indicate that: (1) There are nested relationships between the data and the HLM model is reasonable. That is, the enterprise production behavior under different provincial government management is heterogeneous. (2) The power resource allocation plays an important role in the choice of enterprise production behavior. Specifically, the increase of power resource allocation deepens the negative impact of enterprise factor substitution on enterprise production efficiency, weakens the positive impact of enterprise R&D intensity on enterprise power efficiency, and has a direct positive effect on enterprise location transfer. The above findings improve the research chain of economic consequences of resource allocation, deepen the understanding of the specific impact mechanism of energy policies on enterprise behavior, and provide a decision-making reference for the government to optimize the power supply structure and power grid layout.

Increased cortical activation and enhanced functional connectivity in the prefrontal cortex ensure dynamic postural balance during dual-task obstacle negotiation in the older adults: A fNIRS study

ObjectiveBy analyzing the cortical activation and functional connectivity of the prefrontal cortex (PFC) during dual-task obstacle negotiation in the older adults, cognitive resources allocation and neural regulatory mechanisms of aging brain were shed light on in complex walking conditions. MethodsTwenty-eight healthy right-handed subjects participated in the study, including 15 men and 13 women (age: 68.6 ± 4.1 years, height: 162.96 ± 6.05 cm, weight: 63.63 ± 9.64 kg). There were four tasks: Normal Walk (NW), Obstacle Negotiation during Normal Walk (NW + ON), Walk while performing Cognitive Task (WCT), and Obstacle Negotiation during Walk while performing Cognitive Task (WCT + ON). Participants wore functional near-infrared spectroscopy (fNIRS) to collect hemodynamic signals from various regions of interest (ROIs) in the PFC, while the three-dimensional motion capture system was used to test the gait velocity. Cognitive task data was recorded. ResultsIn WCT + ON, the HbO2 concentration change value (△HbO2) of the left dorsolateral prefrontal cortex was significantly greater than that in the other three tasks (p < 0.05), and the△HbO2 of the right dorsolateral prefrontal cortex was significantly greater than that in NW + ON (p < 0.05). The gait velocities in the four tasks were significantly different (p < 0.05) (NW > WCT > NW + ON > WCT + ON). There was no significant difference in cognitive performance between in the WCT and WCT + ON (p > 0.05). In WCT + ON, the left and right dorsolateral prefrontal areas had strong functional connectivity and the left frontal pole was most widely connected to the other ROIs. Compared to that in NW, the functional connectivity of the left prefrontal lobe was significantly enhanced in WCT + ON (p < 0.05). ConclusionsAs walking difficulty increased, the PFC activation in the older adults changed from right-sided to bilateral activation, indicating that the left PFC cognitive resources compensated for the right PFC in dual-task obstacle negotiation. The cognitive resources recruitment in dual-task obstacle negotiation might be achieved by synchronization and coordination of associated brain areas in the PFC, primarily to maintain dynamic postural balance when walking.

Admission Control for 5G Core Network Slicing Based on Deep Reinforcement Learning

Network slicing is a promising technology for providing customized logical and virtualized networks for the fifth-generation (5G) use-cases (enhanced mobile broadband, ultrareliable low-latency communications, and massive machine-type communications), which pose distinct quality of service (QoS) requirements. Admission control and resource allocation mechanisms are pivotal for realizing network slicing efficiently, but existing mechanisms focus on slicing the radio access network. This article proposes an approach encompassing intelligent and efficient mechanisms for admission control and resource allocation for network slicing in the 5G core network. The admission control mechanism introduces two solutions, one based on reinforcement learning (called SARA) and the other based on deep reinforcement learning (called DSARA). SARA and DSARA consider the QoS requirements of 5G use-cases, differentiate network core nodes from edge nodes, and process slice requests in time windows to favor the service provider’s profit and resource utilization. Results show that SARA and DSARA overcome existing mechanisms for managing admission control and resource allocation in 5G core network slicing.

Pricing-based resource allocation in three-tier edge computing for social welfare maximization

Edge computing is a promising computing paradigm for Internet of Everything and AI-driven applications where substantial computing resources are pushed to the edge of the network in close proximity to the end users. Unlike most of the existing works concentrating on system-side metrics such as job response time, we study how the entities in edge computing interact with each other. Specifically, we study a three-tier edge computing market that consists of edge servers, brokers, and edge users, where brokers are introduced to connect edge servers and edge users, and to facilitate resource deployment and maintenance for edge users. Our goal is to maximize social welfare. The uniqueness of this market, such as the agents’ private information and selfishness, prevents one from using standard optimization techniques. Therefore, we propose a pricing-based resource allocation mechanism via iterative bidding, called MECM, for the three-tier edge computing market. Our theoretical results show that MECM converges to the social optimum with a provable convergence rate of O(1k), where k is the number of iterations, and has desirable properties, i.e., budget balance and individual rationality. Our extensive simulations validate MECM’s performance and its properties in various scenarios.

FedSup: A communication-efficient federated learning fatigue driving behaviors supervision approach

With the proliferation of smart devices and the Internet of Vehicles (IoV) technologies, intelligent fatigue detection has become one of the most-used methods in our daily driving. Data sharing among vehicles can be used to optimize fatigue detection models and ensure driving safety. However, data privacy issues hinder the sharing process. Besides, due to the limitation of communication and computing resources, it is difficult to carry out training and data transmission on vehicles. To tackle these challenges, we propose FedSup, a communication-efficient federated learning method for fatigue driving behaviors supervision. Inspired by the resources allocation mechanism in edge intelligence, FedSup dynamically optimizes the sharing model with tailored client–edge–cloud architecture and reduces communication overhead by a Bayesian Convolutional Neural Network (BCNN) data selection strategy. To improve the sharing model optimize efficiency, we further propose an asynchronous parameters aggregation algorithm to automatically adjust the mixing weight of each edge model parameter. Extensive experiments demonstrate that the FedSup method is suitable for IoV scenarios and outperforms related federated learning methods in terms of communication overhead and model accuracy.

Optimizing protection resource allocation for traffic-driven epidemic spreading.

Optimizing the allocation of protection resources to control the spreading process in networks is a central problem in public health and network security. In this paper, we propose a comprehensive adjustable resource allocation mechanism in which the over allocation of resources can be also numerically reflected and study the effects of this mechanism on traffic-driven epidemic spreading. We observe that an inappropriate resource allocation scheme can induce epidemic spreading, while an optimized heterogeneous resource allocation scheme can significantly suppress the outbreak of the epidemic. The phenomenon can be explained by the role of nodes induced by the heterogeneous network structure and traffic flow distribution. Theoretical analysis also gives an exact solution to the epidemic threshold and reveals the optimal allocation scheme. Compared to the uniform allocation scheme, the increase in traffic flow will aggravate the decline of the epidemic threshold for the heterogeneous resource allocation scheme. This indicates that the uneven resource allocation makes the network performance of suppressing epidemic degrade with the traffic load level. Finally, it is demonstrated that real-world network topology also confirms the results.