RETRACTED ARTICLE: Dynamic resource allocation with optimized task scheduling and improved power management in cloud computing

Increasing the efficiency of resource allocation is the basis and guarantee for boosting high-quality economic development. Based on the panel data of Chinese industrial enterprises and cities from 2008 to 2013, this paper studies the influence of infrastructure construction demand shocks represented by local government debt expansion on the efficiency of sectoral resource allocation from the perspective of sectoral linkage. According to the empirical findings, local government debt significantly reduces the resource allocation efficiency of manufacturing sector that is highly related to infrastructure construction. This conclusion is still tenable after the robustness test using the simulated local government debt as an instrumental variable. Further mechanism tests show that there are two reasons for the decline of the efficiency of resource allocation in manufacturing sector that is highly related to infrastructure construction. First, more product demands and investments brought by the expansion of local government debt flow to less productive enterprises in the sector. Second, resource misallocation reduces the probability of high-productivity enterprises entering the market and low-productivity enterprises exiting the market, and the effect is more prominent in cities with high dependence on state-owned enterprises and high pressure on officials to be promoted. According to this study, the performance management of local government debt should be further strengthened, and particular attention should be paid to the influence of local government debt on enterprise investment and financing crowding out and resource misallocation.

International evidence on the effects of directed credit programmes on efficiency of resource allocation in developing countries: The case of development bank lendings

AI-based resource allocation techniques in D2D communication: Open issues and future directions

ABSTRACTIn this article, we analyzed the impact mechanism of Internet finance on the efficiency of regional financial resource allocation. A data envelopment analysis–Malmquist model is used to measure the efficiency of financial resource allocation in 30 provinces in China, and the impact of Internet finance on the efficiency of regional financial resource allocation is tested using a spatial Durbin model. The results show clear spatial agglomeration and spillover effects in the efficiency of regional financial resource allocation. In addition, developing Internet finance can promote the efficiency and technological progress of regional financial resource allocation and can enhance efficiency and technological progress in the allocation of financial resources in adjacent regions. However, it can hurt the technical efficiency of regional financial resource allocation.

In the face of increasingly growing health demands and the impact of various public health emergencies, it is of great significance to study the regional differences in the allocation efficiency of the rural public health resources and its improvement mechanism. In this paper, the game competition relationship is included in the evaluation model, and the game cross-efficiency model is used to measure the allocation efficiency of the rural public health resources in 31 provinces of China from 2008 to 2017. Then, the Theil index model and the Gini index model are applied in exploring the regional differences in the allocation efficiency of rural public health resources and its sources. Finally, the bootstrap truncated regression model is used to analyze the influencing factors of the allocation efficiency of the rural public health resources in China. The results show that, first, the total allocation efficiency level of the rural public health resources in China from 2008 to 2017 is relatively low, and it presents a U-shaped trend, first falling and then rising. Second, the changing trend of the allocation efficiency of the rural public health resources in the eastern, central, and western regions of China is similar to that in the nationwide region, and it shows a gradient trend that “the allocation efficiency in the eastern region is high, the allocation efficiency in the western region is low, and the allocation efficiency in the Central region is at the medium level”. However, the gap among the three regions is continually narrowing. Third, the calculation results of the Theil index and the Gini index show that intra-regional differences are the major source of the regional differences in the allocation efficiency of the rural public health resources in China, and the inter-regional differences demonstrate an expansion trend. Finally, the improvement of the education level and the social support level will generally improve the allocation efficiency of the rural public health resources in China and its three regions. The increased governmental financial support and urbanization level will reduce the allocation efficiency of the rural public health resources in China and its three regions. The economic development level, the living conditions and the population density are the important influencing factors of the allocation efficiency differences of the rural public health resources in the three regions. Therefore, on the basis of ensuring the increase of the total supply of the rural public health resources, more attention should be paid to the improvement of the allocation efficiency. Moreover, on the basis of continually narrowing the inter-regional differences among the eastern, central, and western regions, more attention should be paid to the intra-regional differences of the allocation efficiency of the rural public health resources among the different provinces. The various economic and social policies should be constantly optimized to jointly improve the allocation efficiency of the rural public health resources.

Priority in task scheduling and resource allocation for cloud computing has attracted significant attention from the research community. However, traditional scheduling algorithms often lack the ability to differentiate between tasks with varying levels of importance. This limitation presents a challenge when cloud servers must handle diverse tasks with distinct priority classes and strict quality of service requirements. To address these challenges in cloud computing environments, particularly within the infrastructure of service models, we propose a novel, self-adaptive, multiclass priority algorithm with VM clustering for resource allocation. This algorithm implements a four-tiered prioritization system to optimize key objectives, including makespan and energy consumption, while simultaneously optimizing resource utilization, degree of imbalance, and waiting time. Additionally, we propose a resource prioritization and load-balancing model based on the clustering technique. The proposed work was validated through multiple simulations using the CloudSim simulator, comparing its performance against well-known task scheduling algorithms. The simulation results and analysis demonstrate that the proposed algorithm effectively optimizes makespan and energy consumption. Specifically, our work achieved percentage improvements ranging from +0.97% to +26.80% in makespan and +3.68% to +49.49% in energy consumption while also improving other performance metrics, including throughput, resource utilization, and load balancing. This novel model demonstrably enhances task scheduling and resource allocation efficiency, particularly in complex scenarios with tight deadlines and multiclass priorities.

The emergence of edge computing has led to some data and tasks being decentralized to eligible edge servers. Obviously, the capacity of edge servers is limited. As tasks and data grow, the load on edge servers will increase. A reasonable allocation of resources to edge servers makes them better able to serve users, so the issue of resource allocation strategies needs to be studied. In this paper, we build a task arrival process as an M/M/1 queuing theory model, which is based on the Dominant Resource Fairness (DRF) allocation algorithm. In this model, the corresponding weight is calculated for each task. Moreover, the subsequent resource allocation process is carried out according to the weighted DRF allocation algorithm. So the efficiency of resource allocation, as well as the quality of service (QoS) is improved. In this paper, the effectiveness of the algorithm is verified experimentally. The experimental results show that the algorithm has improved the resource allocation process.

Digital consumption, driven by the widespread application of information technology, has led to more efficient resource allocation and industrial structure optimization. This promotes green transformation and has a positive impact on carbon productivity. This research examined 275 cities across China, employing a difference-in-differences approach alongside the national information consumption pilot policy to carry out a quasi-natural experiment. The study found that the information consumption pilot policy enhances carbon productivity at the 1% significance level. After controlling for other variables, the regions affected by the information consumption policy saw an increase in carbon productivity that was 0.233 higher than in the regions that were not affected. This growth reflects the positive impact of the information consumption pilot policy or measures on carbon productivity. Meanwhile, the increase in technological resources and the transformation of the industrial framework encouraged by the policy indirectly promote the development of carbon productivity. The information consumption pilot policy promotes technological innovation and increases resource density, leading to more efficient technology application and resource allocation. It also drives industrial structure optimization, particularly accelerating the development of low-carbon industries, thereby effectively enhancing carbon productivity. This study provides theoretical and empirical references for the promotion of carbon productivity through digital consumption.

BackgroundWith the increasing global aging population, how to allocate older people care resources reasonably has become an increasingly urgent international issue. China, as the largest developing country, has made many efforts to actively respond to the challenges of an aging population. However, there are still problems with uneven allocation of older people care resources and low efficiency of allocation. Therefore, this study evaluates the regional differences and dynamic evolution of the equity and efficiency of older people care resource allocation in China from 2009 to 2020, and explores ways to change the current situation.MethodsThe data used in this study were derived from the “China Statistical Yearbook” and the “China Civil Affairs Statistical Yearbook” for the period of 2010–2021. Firstly, the equity of older people care resource allocation was measured using the Gini coefficient, the Theil index, the Older People Care Resource Density Index, and the Older People Care Resource Agglomeration Degree. Secondly, the dynamic Slack-Based Measure data envelopment analysis method was adopted to evaluate efficiency. Lastly, the Z-score is used to normalize the equity index and perform classification matching with the efficiency value. Spatial autocorrelation analysis and hotspot analysis were conducted using GIS technology to examine the dynamic evolution process of older people care resource allocation equity and efficiency, as well as their spatial distribution patterns and coordination across provinces from 2009 to 2020.ResultsThe equity analysis showed that the spatial distribution of various types of older people care resources was uneven, and the differences were mainly due to internal differences within each region, with the largest equity differences observed in western provinces. Currently, older people care resources are mainly concentrated in eastern regions, while the total amount of older people care resources in western regions and some central regions is relatively small, which cannot meet the older people care needs of residents. The efficiency analysis results showed that the efficiency of older people care resource allocation has been improving over the past 12 years, and in 2020, 77.42% of provinces were located on the efficiency frontier with an average efficiency value of 0.9396. Finally, the coordination analysis results showed that there were significant spatiotemporal differences in the equity and efficiency of older people care resources allocation.ConclusionWith the development of society and economy, the total amount and service capacity of older people care resources in China have greatly improved. However, there are still significant spatiotemporal differences in the equity and efficiency of older people care resource allocation. The development of older people care services in central and eastern provinces is unbalanced, and there is a polarization trend in terms of equity and efficiency of older people care resource allocation. Most provinces in western regions face the dual dilemma of inadequate older people care resources and low utilization efficiency. It is recommended that policymakers comprehensively consider population and geographic factors in different provinces, establish relevant allocation standards according to local conditions, improve the redistribution system, and focus on increasing the total amount of older people care resources in underdeveloped provinces while promoting resource flow.

This paper uses the circular-flow diagram to illustrate the efficiency of resource allocation at market equilibria. This diagram not only depicts the circulation of goods and money within a market economy but also suggests the efficiency of resource allocation at market equilibrium. However, economics instructors usually invoke the Edgeworth box diagram to demonstrate efficient resource allocation. Therefore, for an intermediate economics class, this paper proposes a simple method to describe the efficiency of resource allocation: the circular-flow diagram.

Cloud computing is one of the emerging models adopted in business organizations, IT Companies and mobile computing applications. Resources provisioned by cloud can be leased and utilized by clients remotely rather than purchasing it. An efficient allocation of resources is the key issue in the dynamic cloud environment due to the rapid increase in resource demand. Researchers in the literature have proposed various techniques which significantly improve the efficiency of cloud resource allocation, but still more ideal solutions are required. In this paper a resource allocation technique based on resource ranking and resource partitioning is proposed to enhance cloud performance and ensure QoS delivery. The proposed method achieved an efficient resource allocation in terms of response time and expected QoS delivery through resource ranking, resource partitioning and task classification. Simulation result of the proposed method outperforms the existing resource allocation methods.

research explores a dynamic resource allocation framework for Vehicular Ad Hoc Networks (VANETs) aimed at optimizing urban mobility applications through the integration of blockchain technology and Convolutional Neural Networks (CNNs). As urban environments face increasing transportation demands, efficient resource allocation in VANETs is essential to support reliable data transmission, reduce congestion, and enhance overall traffic management. The proposed approach leverages blockchain to establish secure, decentralized communication among vehicles, ensuring data integrity and preventing unauthorized access in the highly dynamic network. CNNs are employed to analyze traffic patterns and vehicular movement, extracting valuable insights from vast amounts of real-time data to make informed resource allocation decisions.

BackgroundTo analyze the equity and efficiency of health resource allocation in Sichuan Province and to provide a scientific basis for promoting health resource development.MethodsTheil index, coefficient of variation, equalization index and health resource agglomeration degree (HRAD) were used to analyze the equity of health resource allocation, and data envelopment analysis (DEA) was used to analyze the efficiency of health resource allocation in Sichuan Province from 2017 to 2021.ResultsThe Theil index and coefficient of variation of health technicians, licensed (assistant) physicians and registered nurses are larger than those of the number of beds, indicating that the equalization of the number of beds is relatively good. The equalization index of health resources in Sichuan Province ranges from 3.228 2 to 3.404 4 between 2017 and 2021, showing a gradually decreasing trend. The equalization index of Northwest Sichuan Economic Zone is the largest, indicating that the equity is relatively good. The equalization index of Chengdu Plain Economic Zone is the lowest, indicating that the equity is relatively poor. The HRAD of the number of beds, health technicians, licensed (assistant) physicians and registered nurses in Chengdu Plain Economic Zone, South Sichuan Economic Zone and Northeast Sichuan Economic Zone are all greater than 1, indicating that the health resources in these regions are better allocated by geography. The HRAD/ population agglomeration degree (PAD) of health technicians, licensed (assistant) physicians and registered nurses in South Sichuan Economic Zone, Northeast Sichuan Economic Zone and Panxi Economic Zone is less than 1, indicating that the health resources in these regions are insufficient relative to the agglomerated population. The comprehensive efficiency of health resource allocation in Sichuan Province and the five economic zones in 2020 is not 1, and the DEA is relatively ineffective. The Panxi Economic Zone, Northeast Sichuan Economic Zone and Northwest Sichuan Economic Zone all have relatively ineffective DEA.ConclusionsFrom 2017 to 2021, health resources in Sichuan Province continue to grow, but equity has gotten worse. The degree of variation in the health resource allocation among the five major economic zones in Sichuan Province is large, with the Chengdu Plain Economic Zone having better equity in the health resource allocation by population and geography, and the Panxi Economic Zone and the Northwest Sichuan Economic Zone having insufficient equity in the health resource allocation by population and geography. The efficiency of health resource allocation in the Panxi Economic Zone, Northeast Sichuan Economic Zone and Northwest Sichuan Economic Zone needs to be improved.

The medical and health resources in the Yangtze River Delta region are abundant, occupying a significant position in China's medical and health system with wide service coverage. The efficient allocation of medical and health resources in this region is a key issue affecting the high-quality development of medical and health services. Existing research on the representative region of the Yangtze River Delta Urban Agglomeration remains relatively limited, with insufficient analysis of the efficiency of medical and health resource allocation in the area, as well as a lack of in-depth exploration into its influencing factors and barriers. This study employs the undesirable output super-efficiency SBM model to calculate the static super-efficiency values of medical and health resource allocation in 41 cities of the Yangtze River Delta urban agglomeration from 2015 to 2020. The Malmquist index is utilized to analyze the changes in dynamic efficiency, and the panel Tobit regression model is utilized to analyze the influencing factors of comprehensive efficiency of medical and health resource allocation in the Yangtze River Delta urban agglomeration. The study highlights that the Yangtze River Delta urban agglomeration confronts multiple systemic challenges, such as pronounced disparities in healthcare resource allocation, uneven dissemination of medical technological innovations, insufficiently effective policy implementation, severe pandemic-induced impacts, enduring gaps in urban-rural healthcare resources, and financial accessibility barriers in low-efficiency cities. By integrating empirical evidence from existing studies and performing comparative case analyses, this research puts forward actionable recommendations. These strategies are designed to improve the equity and efficiency of medical and health resource allocation within the region, while offering transferable insights for other regions facing similar institutional and operational challenges.

Facing global market competition and supply chain risks, many production companies are leaning towards distributed manufacturing because of their ability to utilize a network of manufacturing resources located around the world. Deriving from information and communication technologies and artificial intelligence, the digital twin shop-floor (DTS) has received great attention from academia and industry. DTS is a virtual shop-floor that is almost identical to the physical shop-floor. Therefore, multiple physical shop-floors located in different places can easily be interconnected to realize a DT that is a distributed digital twin shop-floor (D2TS). However, some challenges still hinder effective and efficient resource allocation among D2TSs. In order to attempt to address the issues, firstly, this paper proposes an information architecture for D2TSs based on cloud–fog computing; secondly, a novel mechanism of D2TS resource allocation (D2TSRA) is designed. The proposed mechanism both makes full use of a digital twin to support dynamic allocation of geographic resources and avoids the centralized solutions of the digital twin which lead to a heavy burden on the network bandwidth; thirdly, the optimization problem in D2TSRA is solved by a BP neural network algorithm and an improved genetic algorithm; fourthly, a case study for distributed collaborative manufacturing of aero-engine casing is employed to validate the effectiveness and efficiency of the proposed method of resource allocation for D2TS; finally, the paper is summarized and the relevant research directions are prospected.