Optimal Resource Allocation Scheme Research Articles

Distributed Event-Triggered Algorithm Designs for Resource Allocation Problems via a Universal Scalar Function-Based Analysis.

In this article, we are concerned with distributed algorithm designs for resource allocation problems via event-triggered communication. The target is to search an optimal resource allocation scheme such that the summation of objective functions is minimized. Due to communication efficiency and privacy concerns, distributed algorithms with event-triggered communications are proposed in this article. The communication is only permitted or triggered if variation of gradient of the local objective function exceeds a threshold. By constructing a novel technical lemma and a universal scalar function, the convergence and linear convergence rates are established under some mild assumptions. Extensive numerical experiments on the IEEE 118-bus power system demonstrate that: Compared to the periodic algorithms, such as ADMM and Mirror-P-EXTRA, the proposed algorithms not only remarkably reduce the communication times but also have competitive convergence speed. The latter is striking that it implies there exist useless communications in the periodic algorithms that are censored by the proposed event-triggered strategy.

Enhancing urban system resilience to earthquake disasters: Impact of interdependence and resource allocation

During the post-disaster recovery process of the urban system (US), it is critical to understand the interdependencies of critical infrastructure systems (CISs) and strategically allocate resources among them. However, due to the complexity of the problem and the limitations of the perspective, the existing research usually ignores the implicit impact of interdependence and resource allocation on urban resilience. To bridge this gap, this study establishes a multilayer network-based methodological framework to characterize various types of interdependencies between different CISs and integrate the US as a complex “system of systems”. Then, the system functionality of the US under different resource allocation strategies is quantified and optimized by resilience metrics. This proposed framework was demonstrated in a virtual US including a transportation subsystem (TS), an electric power supply subsystem (EPSS), and a community subsystem (CS) under catastrophic earthquakes. The sensitivity of urban resilience to interdependencies is investigated, and the corresponding results reveal that urban resilience is most sensitive to the interdependence between TS and EPSS. In particular, when there exists strong interdependence between the TS and EPSS, the optimal resource allocation strategy to maximize urban resilience is assigning resource allocation coefficients of 0.1, 0.8, and 0.1 for the TS, EPSS, and CS, respectively. These results can be effectively applied in future planning and investment in urban resilience.

An improved resource allocation method for mapping service function chains based on A3C

AbstractNetwork function virtualization (NFV) technology deploys network functions as software functions on a generalised hardware platform and provides customised network services in the form of service function chain (SFC), which improves the flexibility and scalability of network services and reduces network service costs. However, irrational resource allocation during service function chain mapping will cause problems such as low resource utilisation, long service request processing time and low mapping rate. To address the unreasonable problem of service mapping resource allocation, an improved service function chain mapping resource allocation method (SA3C) based on the Asynchronous advantageous action evaluation algorithm (A3C) is proposed. This study proposes an SFC mapping model and a mathematical model for joint allocation, which modeled the minimization of processing time as a Markov process. The main network was trained and multiple sub‐networks were generated in parallel using the ternary and deep reinforcement learning algorithm A3C, with the goal of identifying the optimal resource allocation strategy. The experimental simulation results show that compared with the Actor‐Critic (AC) and Policy Gradient (PG) methods, SA3C algorithm can improve the resource utilisation by 9.85%, reduce the total processing time by 10.72%, and improve the mapping rate by 6.72%, by reasonably allocating node computational resources and link bandwidth communication resources.

Optimal resource allocation strategy for integrated sensing and communication cellular network with multiple user demands

AbstractFuture communication networks are widely considered to be able to provide both high‐speed communication service and reliable sensing service. Integrated sensing and communication (ISAC) is considered to be the key technology to realize this vision. Appropriate resource allocation strategy is essential for ensuring the quality of both communication and sensing services in terms of ISAC. Existing ISAC‐based resource allocation schemes mainly focus on the coexistence of sensing and communication. However, in multi‐user ISAC networks, the services required by users are diverse, including sensing‐only, communication‐only and dual‐function cases. To this end, a resource allocation strategy is proposed based on user quality of service (QoS). Specifically, the sum rate of cellular network is maximized via optimizing spectrum resource selection and transmit power, while satisfying the sensing QoS. The optimal solution to the entire problem is achieved by first demonstrating that the communication rate in this paper is a monotonically increasing function of sensing power, which allows to obtain the optimal power allocation scheme. Subsequently, a matching method is employed to obtain the optimal spectrum sharing scheme. Numerical results validate the effectiveness of the proposed algorithm and also unveil a flexible trade‐off in ISAC systems over the benchmark scheme.

Autoencoder-Embedded Iterated Local Search for Energy-Minimized Task Schedules of Human–Cyber–Physical Systems

This work considers a task scheduling problem with deadline constraints in human-cyber-physical systems. To find its energy-efficient schedules in a short time, an autoencoder-embedded iterated local search algorithm is proposed to solve it. Iterated local search is selected as a main scheduler. In order to handle real-time requirements and high computational load involved in the problem solution, a Long Short-Term Memory-based AutoEncoder model (LSTM-AE) is constructed to capture the relevant and complementary features of the considered problem. The model is used to find low-order and high-fit sub-solutions via unsupervised end-to-end learning, and generates promising solutions in an informative low-dimensional solution space. To further reduce computational burden, a two-stage optimization framework is constructed, which includes an off-line training phase and an online optimization one. The former trains LSTM-AE by using expert knowledge and historical data. The latter designs optimal resource allocation strategies to build a high-quality initial solution. Then, LSTM-AE-assisted local search operators are proposed and used to reform the initial solution and generate better ones. Various numerical experiments are performed to compare the proposed method with several classic heuristics and some recently-developed methods. The results show its superiority over them. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In human-cyber-physical systems, a task scheduling problem is usually solved by using heuristics due to the limited computational resources. Nevertheless, fast dispatching rules tend to perform poorly. Meta-heuristics can find a relatively high-quality schedule but are time-consuming, especially for a population-based algorithm that requires to evaluate a fitness function for many candidate solutions at each iteration. To balance computational burden and solution quality, our idea is to combine machine-learning methods with meta-heuristics. Specially, we integrate a long short-term memory-based autoencoder model into iterated local search to improve the latter’s optimization ability. The combination of meta-heuristics and machine learning techniques makes it possible to obtain a high-quality schedule for the concerned problems in a short time. Theoretic analysis and experimental results show that the proposed method well outperforms its competitive peers.

The Time-Sensitive Networking Scheduling Algorithm Based on Q-learning

Abstract Time-Sensitive Networking (TSN) occupies a vital position in modern communication domains, with the 802.1Qbv standard being an important network technology designed to meet real-time requirements. This standard requires network traffic to be transmitted within strict time windows, presenting challenges in network planning, necessitating efficient resource allocation and scheduling strategies. This paper addresses the 802.1Qbv planning problem through the introduction of reinforcement learning algorithms, offering an automated and intelligent solution. We have designed a reinforcement learning agent capable of perceiving network status, learning optimal resource allocation strategies, and dynamically adjusting in real-time environments. Through simulation and experimentation, we have validated the effectiveness of our proposed method, comparing it with traditional planning approaches. The contribution of this study lies in introducing a novel solution to the 802.1Qbv planning problem for time-sensitive networks, enhancing network resource utilization and performance. This approach offers strong support for the development and enhancement of TSN-like networks, holding significant importance for meeting the growing demands of real-time applications.

Multi-objective optimization and demand variation analysis on a water energy food nexus system

The interdependence of water, energy, and food resources demands a holistic approach to sustainable resource management. This study focuses on the mathematical optimization of the water-energy-food nexus system in Sinaloa state, Mexico. Using a multi-objective optimization model, we aimed to identify optimal resource allocation strategies and assess the implications of demand variation scenarios in the domestic sector. By considering various scenarios, ranging from reduced to increased demand, the study analyzes the trade-offs between resource consumption, economic benefits, and environmental impacts. The results reveal Pareto fronts that provide insights into efficient resource allocation, highlighting the relationships between water, energy, and food demand. The study provides recommendations for policymakers and contributes to knowledge on water-energy-food nexus optimization, emphasizing the need for integrated approaches to support sustainable resource management.

Deep Learning Framework for Two-Way MISO Wireless-Powered Interference Channels

In this paper, we present a realistic and novel protocol for two-way communication in multiple-input-single-output (MISO) wireless-powered interference channels, namely, a simultaneous wireless information and power transfer (SWIPT) then wireless information transfer (WIT) protocol. In the protocol considered, transmitters first perform SWIPT in a forward link (FL) and receivers and then execute WIT using the harvested energy in a backward link (BL). Given that the operation of SWIPT in the FL affects the performance of WIT in the BL, our aim is to find a resource allocation strategy that maximizes the sum spectral efficiency (SE) of the FL and BL, which requires the joint optimization of the transmit beamforming vector, transmit power, and energy harvesting (EH) ratio in the FL and the receive beamforming vector in the BL. To deal with the non-convexity of the optimization problem, a deep learning (DL) framework is devised, in which the optimal resource allocation strategy is approximated by a well-designed deep neural network (DNN) model consisting of six independent DNN modules where the sigmoid and softmax functions are jointly utilized to properly model each control parameter. Furthermore, a two-stage training method is proposed where the DNN model is initialized using suboptimal solutions that are found using a low complexity algorithm in a supervised manner before it is fine-tuned using the main training based on unsupervised learning. Through intensive simulations performed in various environments, we confirm that the proposed method improves the training performance of the DNN model while reducing the training overhead. As a result, the proposed DL-based resource allocation achieves a near-optimal performance in terms of the sum SE with a low computation time.

An Optimal Resource Allocation Scheme with Carrier Aggregation in 5G Network under Unlicensed Band

Carrier aggregation (CA) technology in the Fifth-Generation (5G) network can be considered as a major feature for high-rate data services, which can be combined with the concept of 5G in unlicensed band (5G-U) for data offloading to further enhance the efficiency of the scarce licensed spectral resource. However, the introduction of cellular services in an unlicensed spectrum can force unlicensed band users to silent mode. Meanwhile, the coexistence among different radio access technologies operating on the same frequency band is a complex issue, especially considering the requirement diversity of quality of service (QoS) in networking. In this paper, we propose an optimal resource allocation mechanism to maximize system performance satisfaction for multiple types of services with different QoS requirements in the 5G-U network with a CA feature to address the challenge. Additionally, the performance of services in Wi-Fi system is protected by reserving the minimum spectrum resource when offloading for the 5G-U network. Analytical results on the mean ergodic achievable rate of Wi-Fi services and the mean packet delay of 5G services are provided to maximize the system performance satisfaction. Numerical results demonstrate the effectiveness of the proposed algorithm, wherein optimal spectrum and power allocation can contribute to the maximum system satisfaction in a 5G-U network with a CA feature.

Resource Allocation Strategy for Satellite Edge Computing Based on Task Dependency

Satellite edge computing has attracted the attention of many scholars, but the limited resources of satellite networks bring great difficulties to the processing of edge-computing-dependent tasks. Therefore, under the system model of the satellite-terrestrial joint network architecture, this paper proposes an efficient scheduling strategy based on task degrees and a resource allocation strategy based on the improved sparrow search algorithm, aiming at the low success rate of application processing caused by the dependency between tasks, limited resources, and unreasonable resource allocation in the satellite edge network, which leads to the decline in user experience. The scheduling strategy determines the processing order of tasks by selecting subtasks with an in-degree of 0 each time. The improved sparrow search algorithm incorporates opposition-based learning, random search mechanisms, and Cauchy mutation to enhance search capability and improve global convergence. By utilizing the improved sparrow search algorithm, an optimal resource allocation strategy is derived, resulting in reduced processing latency for subtasks. The simulation results show that the performance of the proposed algorithm is better than other baseline schemes and can improve the processing success rate of applications.

Managing Water-Energy-Carbon Nexus for Urban Areas With Ambiguous Moment Information

The rapid growth of electric load fuels urbanization but creates unsettling environmental challenges. In that regard, the energy system is a crucial part of consuming water and emitting greenhouse gases. To support reliable and economical energy system operations, with reduced water waste and carbon emissions, we design a viable co-optimization of water-energy-carbon nexus for an integrated energy system by adequately modelling essential interdependencies of power, gas, and water systems. A two-stage distributionally robust optimization method is proposed: the first-stage model minimizes the day-ahead reserve capacity scheduling for the next day, and the second-stage model enables real-time dispatch by considering the variability in renewable power generation. The carbon emissions by power generation and distribution are incorporated in both stages to ensure low-carbon operations. We use a two-stage moment-based distributionally robust approach to capture and represent the uncertainties in renewable generation. Instead of assuming a deterministic distribution, we characterize an ambiguity set with mean vectors and covariance matrices, which produces a family set of distributions. We conduct case studies using a modified IEEE 33-bus power system that includes and links a 20-node gas system and a 10-node water system. The comparative results indicate that the proposed co-optimization of water-energy-carbon nexus is superior than several benchmarks. By considering the close interdependencies of water, energy, and carbon, we create an optimal resource allocation scheme, which is capable of minimizing operation costs, lessening carbon emissions, and reducing water waste. The current research enables effective operation schemes for urban energy systems, illustrates a viable way to curb local environmental emissions, and increases water usage efficiency in the water-energy-carbon nexus.

URLLC-eMBB hierarchical network slicing for Internet of Vehicles: An AoI-sensitive approach

Network slicing has been considered as a promising candidate to meet the heterogeneous Quality of Service (QoS) requirements of various vehicular applications. In this paper, network slicing is utilized on the Road Side Unit (RSU) side for the heterogeneous data freshness requirements in Internet of Vehicles (IoV). The services in enhanced Mobile BroadBand (eMBB) slice are usually bandwidth-consuming, thus the performance can be characterized by throughput. Unlike eMBB slice, the services in Ultra-Reliable Low-Latency Communications (URLLC) slice are usually time-critical, calling for fresh content delivery within short delay, in this case, the trade-off can be made between Age of Information (AoI) and delay in URLLC slice. The resource limitation and coupling effect at both the inter-slice and the intra-slice level have brought challenges to the resource allocation of vehicular network slice. To this end, this paper models the inter- and intra-slice resource allocation problem as a Stackelberg game, in which the RSU acts as the leader and the two slices act as followers. To minimize the weighted sum of AoI and delay in URLLC slice while ensuring the throughput of eMBB slice, this paper proposes a multi-win Stackelberg game resource allocation (MWRA) algorithm. The proposed algorithm can achieve Stackelberg equilibrium (SE) and the SE point is taken as the optimal resource allocation strategy of inter- and intra-slice. Simulation shows the trade-off of inter- and intra-slice resource allocation, and further reveals the interaction of performance between different slices. Numerical results show that compared with the traditional hard slicing, the proposed algorithm can improve the AoI and delay performance of URLLC slice by about 30% under the premise of ensuring the throughput of eMBB slicing.

Network Resource Allocation Algorithm Using Reinforcement Learning Policy-Based Network in a Smart Grid Scenario

The exponential growth in user numbers has resulted in an overwhelming surge in data that the smart grid must process. To tackle this challenge, edge computing emerges as a vital solution. However, the current heuristic resource scheduling approaches often suffer from resource fragmentation and consequently get stuck in local optimum solutions. This paper introduces a novel network resource allocation method for multi-domain virtual networks with the support of edge computing. The approach entails modeling the edge network as a multi-domain virtual network model and formulating resource constraints specific to the edge computing network. Secondly, a policy network is constructed for reinforcement learning (RL) and an optimal resource allocation strategy is obtained under the premise of ensuring resource requirements. In the experimental section, our algorithm is compared with three other algorithms. The experimental results show that the algorithm has an average increase of 5.30%, 8.85%, 15.47% and 22.67% in long-term average revenue–cost ratio, virtual network request acceptance ratio, long-term average revenue and CPU resource utilization, respectively.

Optimal Resource Allocation for Random Multiple Access Oriented SCMA-V2X Networks

In this paper, an optimal resource allocation scheme is proposed for random multiple access (RMA) oriented sparse code multiple access (SCMA) based vehicle-to-everything (V2X) networks. We consider an uplink RMA oriented SCMA, where vehicle-to-infrastructure (V2I) users and vehicle-to-vehicle (V2V) users are considered to randomly choose codebooks for transmission from a shared codebook pool. To avoid mutual interference between V2I users and V2V users, the total bandwidth is decomposed into two parts for transmission of V2I users and V2V users. A joint user-codebook selection and bandwidth allocation optimization problem is formulated to maximize the sum rate of V2I users while guaranteeing the reliability requirement of V2V users. This problem is a mixed-integer programming (MIP) problem with probabilistic constraint, thus it is impractical to directly solve. To solve the problem, the probabilistic constraint is converted into a non-probabilistic one by approximation. Subsequently, efficient but suboptimal staged algorithms are proposed to solve the joint optimization problem. Firstly, a new decoupled Q-learning based user-codebook selection algorithm (DQL-UCSA) is proposed to find the optimal user-codebook selection relationships, which completely address codebook collision problem. Under the optimal user-codebook selection relationships determined, the joint optimization problem is transformed into a single objective bandwidth allocation problem. Then, we solve the single objective problem using a convex optimization method, which maximizes the sum rate of V2I users. Simulation results show the DQL-UCSA can converge quickly and enable a significant performance improvement by addressing codebook collisions. Besides, the proposed RMA-SCMA with optimal bandwidth allocation (OBA) is significantly superior to conventional schemes in terms of sum rate.

Intelligent Resource Allocation for V2V Communication with Spectrum-Energy Efficiency Maximization.

Aiming to address the limitations of traditional resource allocation algorithms in the Internet of Vehicles (IoV), whereby they cannot meet the stringent demands for ultra-low latency and high reliability in vehicle-to-vehicle (V2V) communication, this paper proposes a wireless resource allocation algorithm for V2V communication based on the multi-agent deep Q-network (MDQN). The system model utilizes 5G network slicing technology as its fundamental feature and maximizes the weighted spectrum-energy efficiency (SEE) while satisfying reliability and latency constraints. In this approach, each V2V link is treated as an agent, and the state space, action, and reward function of MDQN are specifically designed. Through centralized training, the neural network parameters of MDQN are determined, and the optimal resource allocation strategy is achieved through distributed execution. Simulation results demonstrate the effectiveness of the proposed scheme in significantly improving the SEE of the network while maintaining a certain success rate for V2V link load transmission.

Optimized model for coordinated development of regional sustainable agriculture based on water–energy–land–carbon nexus system: A case study of Sichuan Province

In the context of global warming and resource scarcity, economic development and population growth are challenging the demand for water and land resources, and the complex relationship among water, land, energy and carbon dioxide makes it important that a process for efficiently managing water, energy, land and carbon dioxide in sustainable agricultural systems is developed. Therefore, considering the uncertainty of agricultural systems, this paper proposes a multiobjective optimization model for the sustainable management of water–energy–land–carbon dioxide systems that optimizes the allocation of water resources and land resources under the condition of minimizing agricultural carbon emissions and maximizing irrigation water productivity and energy economic productivity. The model uses the improved nondominated sorting genetic algorithm to address this issue, and the optimal resource allocation strategy is further obtained through the multiobjective decision model. This paper uses Sichuan Province, the main grain-producing region in Southwest China, as a case study to verify the effectiveness of the model and then provide a decision-making process for improving the utilization rate of agricultural resources and developing low-carbon agriculture. The results show that under the coordination of resource consumption, economic benefits and carbon emissions, the total allocation of water resources and the total allocated area of crop planting after optimization were 118.678 × 108m3 and 4598.796 × 103ha, respectively, decreases of 22.87% and 7.9%. The planting structure changed. The area planted with rice increased by 4.25%, the area planted with corn decreased by 15.5%, and the area planted with rapeseed decreased by 14.96%. Electricity, fertilizer and straw burning contributed significantly to agricultural carbon emissions, with average contributions of 68.0%, 15.9% and 6.6%, respectively. The multiobjective optimization model established in this paper can improve resource utilization and reduce the environmental impact of agriculture, which are of great significance in terms of achieving the goal of “carbon neutrality” and promoting the sustainable development of regional agriculture. The established model framework can be applied to regions with limited resources around the world.

Optimal resource allocation strategies of competing new online delivery platforms using the Bass diffusion model

Optimal resource allocation strategies of competing new online delivery platforms using the Bass diffusion model

Energy-Efficient Task Transfer in Wireless Computing Power Networks

The Sixth Generation (6G) wireless communication aims to enable ubiquitous intelligent connectivity in future Space-Air-Ground-Ocean integrated networks, with extremely low latency and enhanced global coverage. However, the explosive growth in Internet of Things devices poses new challenges for smart devices to process the generated tremendous data with limited resources. In 6G networks, conventional mobile edge computing systems encounter serious problems to satisfy the requirements of ubiquitous computing and intelligence, with extremely high mobility, resource-limitation, and time-variability. In this paper, we propose the model of Wireless Computing Power Networks (WCPN), by jointly unifying the computing resources from both end devices and MEC servers. Furthermore, we formulate the new problem of task transfer, to optimize the allocation of computation and communication resources in WCPN. The main objective of task transfer is to minimize the execution latency and energy consumption with respect to resource limitations and task requirements. To solve the formulated problem, we propose a multi-agent Deep Reinforcement Learning (DRL) algorithm to find the optimal task transfer and resource allocation strategies. The DRL agents collaborate with others to train a global strategy model through the proposed asynchronous federated aggregation scheme. Numerical results show that the proposed scheme can improve computation efficiency, speed up convergence rate, and enhance utility performance.

Scheduling Policy for Value-of-Information (VoI) in Trajectory Estimation for Digital Twins

This paper presents an approach to schedule observations from different sensors in an environment to ensure their timely delivery and build a digital twin (DT) model of the system dynamics. At the cloud platform, DT models estimate and predict the system’s state, then compute the optimal scheduling policy and resource allocation strategy to be executed in the physical world. However, given limited network resources, partial state vector information, and measurement errors at the distributed sensing agents, the acquisition of data (i.e., observations) for efficient state estimation of system dynamics is a non-trivial problem. We propose a Value of Information (VoI)-based algorithm that provides a polynomial-time solution for selecting the most informative subset of sensing agents to improve confidence in the state estimation of DT models. Numerical results confirm that the proposed method outperforms other benchmarks, reducing the communication overhead by half while maintaining the required estimation accuracy.

The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19.

The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supply-constrained resource allocation strategies for controlling novel disease epidemics. To address the challenge of constrained resource optimization for managing diseases with complications like pre- and asymptomatic transmission, we develop an integro partial differential equation compartmental disease model which incorporates realistic latent, incubation, and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals. Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission. To analyze the influence of these realistic features on disease controllability, we find optimal strategies for reducing total infection sizes that allocate limited testing resources between 'clinical' testing, which targets symptomatic individuals, and 'non-clinical' testing, which targets non-symptomatic individuals. We apply our model not only to the original, delta, and omicron COVID-19 variants, but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions, which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness. We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies, while the relationship between incubation-latent mismatch, controllability, and optimal strategies is complicated. In particular, though greater degrees of presymptomatic transmission reduce disease controllability, they may increase or decrease the role of non-clinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length. Importantly, our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.