Balancing Resource Utilization Research Articles

Monitoring Dissolved Oxygen Concentrations in the Coastal Waters of Zhejiang Using Landsat-8/9 Imagery

The Zhejiang coastal waters (ZCW), which exhibit various turbidity levels, including low, medium, and high turbidity levels, are vital for regional ecological balance and sustainable marine resource utilization. Dissolved oxygen (DO) significantly affects marine organism survival and ecosystem health, yet there is limited research on remote sensing monitoring of DO in the ZCW, and the underlying mechanisms are unclear. This study addresses this gap by utilizing high-resolution Landsat 8/9 imagery and sea surface temperature (SST) data to develop a multiple linear regression (MLR) model for DO estimation. Compared to previous studies that utilize remote sensing band reflectance data as inputs, the results show that the red and blue bands are more suitable for establishing DO inversion models for such water bodies. The model was applied to analyze variations in the DO concentrations in the ZCW from 2013 to 2023, with a focus on Hangzhou Bay (HZB), Xiangshan Bay (XSB), Sanmen Bay (SMB), and Yueqing Bay (YQB). The temporal and spatial distributions of DO concentrations and their relationships with environmental factors, such as chlorophyll-a (Chl-a) concentrations, total suspended matter (TSM) concentrations, and thermal effluents, are analyzed. The results reveal significant seasonal fluctuations in DO concentrations, which peak in winter (e.g., 9.02 mg/L in HZB) and decrease in summer (e.g., 6.83 mg/L in HZB). Changes in the aquatic environment, particularly in the thermal effluents from the Sanmen Nuclear Power Plant (SNPP), significantly decrease coastal dissolved oxygen (DO) concentrations near drainage outlets. Chl-a and TSM directly or indirectly affect DO concentrations, with notable correlations observed in XSB. This study offers a novel approach for monitoring and managing water quality in the ZCW, facilitating the early detection of potential hypoxia issues in critical zones, such as nuclear power plant heat discharge outlets.

An integrated optimization method to task scheduling and VM placement for green datacenters

In the realm of cloud computing, effective resource allocation can significantly enhance the energy efficiency of datacenters. Task scheduling and Virtual Machine Placement (VMP) are two pivotal aspects of resource allocation. However, in current research, they are often treated separately, overlooking the potential for integrated optimization. In this paper, we propose an integrated solution for task scheduling and VMP in energy-efficient datacenters, based on queueing theory and Deep Reinforcement Learning (DRL) methods. This novel and comprehensive approach provides an alternative perspective for resource scheduling strategies in datacenters. We construct a queueing theory model for task scheduling, aiming to minimize the number of VMs that need to be instantiated, while ensuring that Service Level Agreement (SLA) violation remains at a low level. Furthermore, we design a VMP algorithm based on DRL for real-time selection of Physical Hosts (PHs) for deploying VMs. Finally, we conduct a simulation evaluation using a small-scale datacenter. The experimental results demonstrate that our method consistently ensures a lower rate of SLA violation. Compared to existing algorithms, the DRL-based VMP algorithm enables a more balanced utilization of the various resources in the PHs and reduces the total power consumption of the datacenter by more than 10% on average.

Research on VR e-learning based on load balancing algorithm in intelligent music education system

This paper aims to explore the application of virtual reality (VR) electronic learning based on load balancing algorithm in the simulation of intelligent music education system. This paper designs and implements an intelligent music education system simulation, which uses load balancing algorithm and virtual reality (VR) technology to provide the convenience of e-learning and personalized learning experience. The load balancing algorithm allocates and manages multiple tasks or requests in the system to ensure the balanced utilization of resources and the efficient operation of the system. In the intelligent music education system, the load balancing algorithm can be used to manage user requests, allocate appropriate resources and services, and ensure that the system can respond quickly and provide a stable learning environment. This paper introduces the architecture and implementation details of the system, including the design and optimization of load balancing algorithm and the application of VR technology. The results show that we point out that VR e-learning based on load balancing algorithm has potential in the simulation of intelligent music education system, which can provide high-quality personalized music learning experience and provide useful enlightenment for innovation in the field of music education.

Modeling and Analyzing the Strategy Game “Factorio” Using Modular Petri Nets and the General-Purpose Petri Net Simulator

The video game Factorio is a popular real-time strategy game that hosts a variety of gameplay scenarios analogous to real-world problems such as factory building, pipeline optimization, and conveyor belt logistics. It also considers environmental costs such as pollution and energy consumption. This game focuses on automation and resource management within the context of creating and maintaining factories. Players are challenged to design efficient factories, automate production, control energy consumption, and balance resource utilization, all while defending against environmental challenges such as pollution and hostile enemies. Factorio is a complex software; however, studies have demonstrated its ability to enhance student satisfaction when integrated as an educational module at the university level. This paper aims to utilize the power of Petri Net theory and the practical tool GPenSIM to model and simulate several Factorio pipelines and score them against one another, exploring which pipeline configuration is optimal. The primary objective is to make a mathematical model (Petri Net) to better understand the game’s complex mechanisms, identify bottlenecks within the system, and explore opportunities for optimization within the network of modules. The insights gained from this paper may find practical applications in real-world scenarios.

Spectrum fragmentation-aware dynamic network slicing deployment in computing power networks based on elastic optical networks

Abstract The widespread application of AI with high computing requirements has driven the rapid development of the computing field. Computing Power Networks (CPNs) have been recognized as solutions to providing on-demand computing services, and its service provisioning can be modeled as a network slicing deployment problem. Elastic Optical Networks (EONs) offer the flexibility to allocate spectrum resources, making them well-suited for network slicing technology. Consequently, EON-based CPNs have attracted considerable attention. However, the unbalanced distribution of computing resources leads to inefficient computing resource utilization. Meanwhile, spectrum resources may be isolated and difficult for other services. This phenomenon is known as spectrum fragmentation, leading to inefficient spectrum resource utilization. To achieve balanced and efficient resource utilization, this paper first analyzes the main reasons for load unbalance and spectrum fragmentation in CPNs: mismatched slicing deployment and inappropriate resource scheduling. Therefore, a dynamic network slicing scheme based on traffic prediction (DNS-TP) is designed. Its core highlight is cooperative optimization slicing deployment and resource scheduling based on spectrum fragmentation awareness. Simulation results show that the proposed scheme enhances the network slicing acceptance ratio, computing and spectrum resource utilization while exhibiting strong performance in resource balancing.

The Impact of Digital Technologies on Memory and Memory Studies

With the widespread integration of smartphones, computers, and the internet, information access and processing have undergone significant changes. This paper investigates both positive and negative implications, acknowledging the extension of cognitive capacities through easy access to vast databases and external memory aids while also addressing concerns about diminished memory consolidation and reliance on shallow encoding strategies. Examining the interdisciplinary field of memory studies, the study also highlights collaborative efforts among scholars in psychology, neuroscience, sociology, and information science to comprehend the impact of digital technologies on memory, and emphasizes the challenges and future directions in memory research, including issues like digital amnesia, information overload, and privacy concerns. Overall, the paper underscores the need for understanding the relationship between human memory and digital tools, enabling the development of strategies to enhance memory, counteract potential adverse effects, and promote a balanced utilization of digital resources in memory-related tasks.

Rational Utilization of Sediment Resources Improves Rice Yield and Nitrogen Use Efficiency under Salt Stress

Soil salinization negatively affects rice growth and yield; however, how different sludge sources regulate rice growth and yield under salt stress was rarely investigated. This study evaluated the performance of two salt-tolerant rice cultivars, Chaoyou 1000 and Longliangyou 506, grown in two sediment sources, pond sediment (PS) and river sludge (RS), under salt stress (56 ds m−1 brine irrigation) with conventional soil (CS) used as the control. The results showed that the rice yield under the PS and RS treatments was enhanced by 51.0% and 43.6% as compared with CS, respectively, owing to an improvement in spikelet per panicle, 1000-grain weight, dry matter accumulation, and the chlorophyll content in both rice cultivars. Compared with CS, the total nitrogen accumulation, nitrogen grain production efficiency, nitrogen harvest index, and nitrogen partial productivity under the PS and RS treatments were increased by 18.9–28.9%, 17.0–20.6%, 7.2–16.6%, and 43.8–50.9%, respectively. Moreover, rice grown in PS and RS showed higher activities of nitrogen metabolism-related enzymes (nitrate reductase, glutamine synthetase, and glutamate synthetase) at the heading stage and higher K+ and K+/Na+ contents in the leaves. Overall, a balanced utilization of sediment resources (especially pond sediment) can effectively alleviate salt stress and improve the yield and nitrogen use efficiency in rice.

Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses

Graph Neural Networks (GNNs) are emerging as a powerful tool for learning from graph-structured data and performing sophisticated inference tasks in various application domains. Although GNNs have been shown to be effective on modest-sized graphs, training them on large-scale graphs remains a significant challenge due to the lack of efficient storage access and caching methods for graph data. Existing frameworks for training GNNs use CPUs for graph sampling and feature aggregation, while the training and updating of model weights are executed on GPUs. However, our in-depth profiling shows CPUs cannot achieve the graph sampling and feature aggregation throughput required to keep up with GPUs. Furthermore, when the graph and its embeddings do not fit in the CPU memory, the overhead introduced by the operating system, say for handling page-faults, causes gross under-utilization of hardware and prolonged end-to-end execution time. To address these issues, we propose the GPU Initiated Direct Storage Access (GIDS) dataloader, to enable GPU-oriented GNN training for large-scale graphs while efficiently utilizing all hardware resources, such as CPU memory, storage, and GPU memory. The GIDS dataloader first addresses memory capacity constraints by enabling GPU threads to directly fetch feature vectors from storage. Then, we introduce a set of innovative solutions, including the dynamic storage access accumulator, constant CPU buffer, and GPU software cache with window buffering, to balance resource utilization across the entire system for improved end-to-end training throughput. Our evaluation using a single GPU on terabyte-scale GNN datasets shows that the GIDS dataloader accelerates the overall DGL GNN training pipeline by up to 582× when compared to the current, state-of-the-art DGL dataloader.

Axicabtagene Ciloleucel versus Tisagenlecleucel for Relapsed or Refractory Large B Cell Lymphoma: A Systematic Review and Meta-Analysis

Axicabtagene ciloleucel (axi-cel) and tisagenlecleucel (tisa-cel) are CD19-directed chimeric antigen receptor T cell (CAR-T) therapies approved for relapsed/refractory aggressive large B cell lymphoma (LBCL). Significant costs and complex manufacturing underscore the importance of evidence-based counseling regarding the outcomes of these treatments. With the aim of examining the efficacy and safety of axi-cel versus tisa-cel in patients with relapsed/refractory aggressive LBCL, we performed a systematic literature search of comparative studies evaluating outcomes in relapsed/refractory aggressive LBCL after treatment with axi-cel or tisa-cel. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) for response, progression-free survival (PFS), overall survival (OS), cytokine release syndrome (CRS), immune effector cell-associated neurotoxicity syndrome (ICANS), and hematotoxicity. Meta-analysis and meta-regression were used to generate summary statistics. A total of 2372 participants were included in the 8 studies in our analysis. The dropout rate between apheresis and infusion was 13% for axi-cel versus 18% for tisa-cel, and the median time from apheresis to infusion was 32 days versus 45 days. Axi-cel showed higher odds for a complete response (OR, 1.65; P < .001) and was associated with higher odds for PFS at 1 year after infusion (OR, .60; P < .001). OS appeared to be improved with axi-cel (OR, .84; 95% CI, .68 to 1.02; P = .08), whereas the cumulative incidence of nonrelapse mortality (NRM) was 11.5% for axi-cel versus 3.7% for tisa-cel (P = .002). The main predictors for survival were lactate dehydrogenase level, Eastern Cooperative Oncology Group Performance Status, and response to bridging, and axi-cel maintained superior efficacy even in elderly patients. In terms of safety, axi-cel was associated with significantly higher odds of any-grade CRS (OR, 3.23; P < .001), but not of grade ≥3 CRS (P = .92). Axi-cel was associated with significantly higher odds of severe ICANS grade ≥3 (OR, 4.03; P < .001). In terms of hematotoxicity, axi-cel was significantly associated with higher odds of severe neutropenia at 1 month after infusion (OR, 2.06; P = .003). As a result, axi-cel was associated with significantly greater resource utilization, including prolonged hospital stay, more frequent intensive care admission, and use of agents such as tocilizumab for toxicity management. We provide strong evidence of the greater efficacy of axi-cel versus tisa-cel in relapsed/refractory aggressive LBCL. The higher toxicity and NRM seen with axi-cel might not counterbalance the overall results, highlighting the need for timely intervention and careful selection of patients, balancing resource utilization and clinical benefit.

Study on the mechanical properties and deterioration mechanism of recycled aggregate concrete for low-grade highway pavements

This study is motivated by the imperative for sustainable construction practices, specifically focusing on recycling construction waste into low-grade highway pavements to enhance environmental sustainability and resource efficiency. The investigation explores the workability, mechanical properties, and interfacial transition zones (ITZs) of recycled aggregate concrete (RAC), emphasizing the incorporation of recycled coarse aggregate (RCA) from construction waste. The primary objective is to delineate the optimal mixture proportions of RAC that effectively balance resource utilization with the technical requisites for low-grade highway pavements. Through rigorous orthogonal testing and analysis, a mixture was identified that achieves a high RCA replacement rate while preserving workability and mechanical properties. Additionally, nanoindentation tests were employed to probe the nanomechanical properties of the ITZs in RAC, aiming to elucidate the underlying degradation mechanisms. Results reveal that the porous and rough surface of RCA significantly impacts the properties of RAC, with a marked decrease in elastic modulus and an increase in ITZ width identified as primary contributors to mechanical deterioration. Consequently, this study not only provides crucial insights for the application of RAC in low-grade highway pavements but also sets the stage for future research aimed at enhancing the sustainability and performance of recycled construction materials.

Resource overload problems with tardiness penalty: structural properties and solution approaches

AbstractIn this paper, we consider a resource overload problem and add a tardiness penalty to the objective function when a prescribed project makespan is exceeded, which enables a trade-off between a balanced resource utilization and a project delay. For the tardiness penalty, we distinguish between a constant and variable delay cost variant. Based on the structural properties of the resource overload problem, we show that the search space of the resource overload problem with tardiness penalty can also be reduced utilizing quasistable schedules. In addition, we discuss the application of these findings to further problems, which include objectives composed of a locally concave and a concave function or a reward structure for an early project completion instead of a tardiness penalty. As solution approaches, we present mixed-integer linear model formulations as well as a novel genetic algorithm with a decoding procedure, which exploits the devised structural properties. The performance of the genetic algorithm is improved by implementing learning methods and utilizing lower bounds. Finally, we present results from experiments on small to medium sized problem instances.

Improved task scheduling strategy for balancing resource utilization and service quality in mobile edge computing environment

Improved task scheduling strategy for balancing resource utilization and service quality in mobile edge computing environment

Influence of Ho spontaneous aggregation on the coercivity and magnetic domain movement of sintered NdY-Fe-B magnets

Development of NdY-Fe-B magnets not only reduces the cost of Nd-Fe-B type magnets but also promotes the balanced utilization of rare-earth resources. However, the lower coercivity of NdY-Fe-B magnets hinders their further development. This study investigated the coercivity of NdY-Fe-B magnets via intergranular doping of Ho76Al12Ga12 powders. Although this process improved the coercivity, the remanence substantially deteriorated with the increasing content of the Ho76Al12Ga12 alloy. A coercivity of 1.37 T was achieved in a NdY-Fe-B magnet with a Ho76Al12Ga12 alloy doping of 7 wt%, showing a coercivity increment (ΔHcj) of 0.64 T compared to the original magnet. This can be attributed to the observed microstructural and elemental distributions, as well as the analysis of the magnetic domain wall movement. An abnormal Ho-biased layer is observed between the Y-rich core and the Ho-rich shell in the matrix grains, exhibiting a higher anisotropy field compared than the shell layer. Further analysis revealed that the movement of magnetic domain wall movement was obstructed by the Ho-biased layer, demonstrating a domain-wall pinning effect within the matrix grains. Additionally, the formation of a Ho-rich shell in the outer area of the 2:14:1 matrix grain and the presence of a continuous uniform grain boundary phase were found to be important for improving both coercivity and thermal stability.

Multirobot collaborative task dynamic scheduling based on multiagent reinforcement learning with heuristic graph convolution considering robot service performance

To address the problem of multirobot collaborative task scheduling considering the degradation of industrial robot performance and the recovery of robot performance through intervention of compensation measures, a robot collaborative task scheduling method based on multiagent reinforcement learning with heuristic graph convolution is proposed in this paper. Five types of constraints between tasks and robots from the temporal and spatial dimensions are designed, and a graph structure with different connection forms is utilized to represent the tasks, robots, and their mutual constraints. The method creates serviceability evaluation (SE) and error compensation (EC) agents to assess the robot's serviceability and compensate for positioning errors. Moreover, it combines heuristic rules and graph convolutional neural networks to create a heuristic graph convolution scheduling (HGCS) agent for task allocation and scheduling. The three agents are trained in a decentralized manner while executing in a centralized manner to achieve dynamic task scheduling that considers the robot's serviceability and error compensation intervention in practical scenarios. Through three case studies involving the evaluation of the robot core drivetrain component degradation status, compensation of industrial robot positioning errors, and multirobot collaborative task scheduling, the proposed method effectively addresses the performance changes in robots during the task allocation and scheduling process. Comparative evaluations with four relevant methods in various task scheduling scenarios demonstrate that the proposed method reduces the average task completion time by 17.99% and improves the resource utilization balance degree by 16.85%.

Valuasi Ekonomi Wilayah Adat Taman Sunsong Kabupaten Sekadau

This article aims to investigate the valuation of the indigenous territory of Taman Sunsong in Sekadau Regency, with a focus on uncovering the cultural, economic, and environmental values inherent in the Taman Sunsong Indigenous Community. The research employs a mixed-method approach, combining qualitative and quantitative methods. Field surveys were conducted to gather data on the social, cultural, and economic profiles of the indigenous community. In-depth interviews were carried out with traditional leaders and community members to gain a comprehensive understanding. Economic data was collected through surveys, market price approaches, and benefit transfer methods. The findings reveal that the Taman Sunsong Indigenous Community possesses unique cultural traits and a way of life governed by customary values, rituals, and religious practices. Their social life is rooted in strong communal interactions, while a close relationship with the surrounding environment also plays a significant role. The significance of preserving nature and culture is evident in the high value placed on the indigenous forest. The forest holds cultural significance and profound knowledge about ecosystem balance and sustainable resource utilization. The economy of the Taman Sunsong indigenous region relies on fields, gardens, traditional craftsmanship, and the potential for ecotourism. To assess the economic value of the indigenous territory, market price approaches and benefit transfer methods are employed. Market prices are used to gauge the value of agricultural commodities and forest products. The benefit transfer method is utilized to estimate the value of environmental services such as oxygen production by the forest.

Towards an Optimal Cloud-Based Resource Management Framework for Next-Generation Internet with Multi-Slice Capabilities

With the advent of 5G networks, the demand for improved mobile broadband, massive machine-type communication, and ultra-reliable, low-latency communication has surged, enabling a wide array of new applications. A key enabling technology in 5G networks is network slicing, which allows the creation of multiple virtual networks to support various use cases on a unified physical network. However, the limited availability of radio resources in the 5G cloud-Radio Access Network (C-RAN) and the ever-increasing data traffic volume necessitate efficient resource allocation algorithms to ensure quality of service (QoS) for each network slice. This paper proposes an Adaptive Slice Allocation (ASA) mechanism for the 5G C-RAN, designed to dynamically allocate resources and adapt to changing network conditions and traffic delay tolerances. The ASA system incorporates slice admission control and dynamic resource allocation to maximize network resource efficiency while meeting the QoS requirements of each slice. Through extensive simulations, we evaluate the ASA system’s performance in terms of resource consumption, average waiting time, and total blocking probability. Comparative analysis with a popular static slice allocation (SSA) approach demonstrates the superiority of the ASA system in achieving a balanced utilization of system resources, maintaining slice isolation, and provisioning QoS. The results highlight the effectiveness of the proposed ASA mechanism in optimizing future internet connectivity within the context of 5G C-RAN, paving the way for enhanced network performance and improved user experiences.

XVDPU: A High Performance CNN Accelerator on Versal Platform Powered by AI Engine

Nowadays, convolution neural networks (CNNs) are widely used in computer vision applications. However, the trends of higher accuracy and higher resolution generate larger networks. The requirements of computation or I/O are the key bottlenecks. In this paper, we propose XVDPU: the AI-Engine (AIE)-based CNN accelerator on Versal chips to meet heavy computation requirements. To resolve IO bottleneck, we adopt several techniques to improve data-reuse and reduce I/O requirements. An Arithmetic Logic Unit (ALU) is further proposed which can better balance resource utilization, new feature support, and efficiency of the whole system. We have successfully deployed more than 100 CNN models with our accelerator. Our experimental results show that the 96-AIE-core implementation can achieve 1653 frames per second (FPS) for ResNet50 on VCK190, which is 9.8 × faster than the design on ZCU102 running at 168.5 FPS. The 256-AIE-core implementation can further achieve 4050 FPS. We propose a tilling strategy to achieve feature-map-stationary (FMS) for high-definition CNN (HD-CNN) with the accelerator, achieving 3.8 × FPS improvement on Residual Channel Attention Network (RCAN) and 3.1 × on Super-Efficient Super-Resolution (SESR). This accelerator can also solve the 3D convolution task in disparity estimation, achieving end-to-end (E2E) performance of 10.1FPS with all the optimizations.

Surface Condensation Water under Salix psammophila Is the Main Water Source in Addition to Rainfall in the Kubuqi Desert

Amidst climate change, managing water and vegetation adaptability is vital in ecology and agriculture. Salix psammophila is key in deserts, maintaining ecological balance and combating desertification. Understanding its surface condensation and response to weather is critical for survival. This study aims to investigate the formation patterns of surface condensation water under S. psammophila at different irrigation levels and the influence of precipitation, temperature, relative humidity, and wind speed. Conducted in China’s Kubuqi Desert from June to September 2022, the study employed micro-lysimeters with four irrigation test sets and a control group, conducting detailed observations at various locations. The results indicate that S. psammophila significantly influences surface condensation water by blocking solar radiation and reducing wind speed. The drip irrigation system also regulates surface condensation water on S. psammophila. Moreover, meteorological factors such as 24 h maximum temperature, relative humidity difference, wind speed, and air vapor pressure deficit show significant correlations with surface condensation water formation. In conclusion, this study enhances the understanding of desert ecosystem water balance, vegetation adaptability, and efficient water resource utilization. It provides valuable scientific guidance for the conservation and restoration of desert ecosystems.

Enhancing Sustainable Management of Sibolangit Nature Tourism Park Through Comprehensive Stakeholder Analysis

The Sibolangit Nature Tourism Park (SNTP) holds immense potential as a natural attraction, but effective management faces challenges due to limited resource utilization and community engagement. To address this, a comprehensive stakeholder analysis was conducted. The research was conducted within SNTP, Deli Serdang Regency, between May and June 2021, utilizing questionnaires and in-depth interviews to gather primary data from 25 key informants. Stakeholders represented central and local governments, surrounding communities, and non-governmental organizations (NGOs) such as the Sibolangit Tourism Awareness Group. The collected data underwent thorough analysis to assess stakeholder interest and influence levels. Stakeholders were categorized into key players, context setters, and the crowd. Key players included the North Sumatra Natural Resources Conservation Center, SNTP, and the Deli Serdang Culture and Tourism Office, exerting significant influence in policy formulation and resource allocation. Context setters, represented by local communities, held substantial influence and played crucial roles in decision-making and implementation. The crowd, comprising NGOs, demonstrated high interest but lower influence in management. Collaboration among stakeholders, particularly central and local entities, emerged as pivotal for effective SNTP management. Stakeholders' roles significantly impacted management outcomes. This study highlights the necessity of holistic collaboration and balanced resource utilization for sustainable nature tourism management within SNTP. The findings contribute to a comprehensive understanding of stakeholder dynamics and their roles in achieving the objectives of SNTP management

Past and future adverse response of terrestrial water storages to increased vegetation growth in drylands

The response of terrestrial water storages to dryland vegetation growth remains poorly understood. Using multiple proxies from satellite observations and model outputs, we show an overall increase (decrease) in vegetation growth (terrestrial water storages) across drylands globally during 1982–2016. Terrestrial water storages in greening drylands correlate negatively with vegetation growth, particularly for cropland-dominated regions, and such response is pronounced when the growth rate of vegetation productivity is high. Reduction in terrestrial water storage is dominated by precipitation and evapotranspiration variability rather by than runoff. We predict reduction in terrestrial water storage of 41–84% by 2100, accompanying expansion of drylands by 4.1–10.6%. Our findings, which indicate sustained adverse response of terrestrial water storage to vegetation growth in drylands, highlight the need for concerted planning for balanced ecological restoration, agricultural management, and water resource utilization that will affect 5.17 billion people, 64% of whom live in developing countries.