Resource Allocation In Systems Research Articles

Spectral-Energy Efficient Resource Allocation in RIS-Aided FD-MIMO Systems

Spectral-Energy Efficient Resource Allocation in RIS-Aided FD-MIMO Systems

Task Manager

In the realm of modern computing, efficient task management stands as a cornerstone for ensuring system performance and user productivity. The Task Manager, a fundamental component of operating systems, provides users with insights into system resource allocation and facilitates the management of running processes. This paper aims to explore the significance of task management in contemporary computing environments and delves into strategies for optimizing the functionality of the Task Manager. Keywords: Task Manager, System Performance, Resource Allocation, User Experience, Optimization, Machine Learning, Operating Systems

Construction and application of urban water resources spatial equilibrium model: A case study of Ulanqab City

Abstract Evaluating the water resources spatial equilibrium (WRSE) is the foundation for scientifically planning the construction of an optimized water resource allocation system and achieving the high-standard development of the China Water Network Project. To elucidate the characteristics of WRSE variation in Ulanqab City, this study explored a novel WRSE assessment model based on connection number and the subtraction set pair potential method. The results indicate that: (1) there exists a mismatch between the distribution of water resources in Ulanqab City and its socio-economic development. In 2020, the Gini coefficients of water resources total quantity concerning resident population, GDP, industrial output value, and irrigated land area were 0.48, 0.48, 0.51, and 0.28, respectively. (2) From 2011 to 2020, the overall WRSE level in Ulanqab City showed an increasing trend year by year. Among them, the WRSE levels in six counties improved, while Jining District consistently remained at a low equilibrium state due to the prominent contradiction between water supply and demand. (3) In the future, Ulanqab City can further enhance the WRSE level by promoting the reuse of recycled water, intensifying groundwater pollution control, and implementing a quota management system for agricultural water use.

Optimal Allocation of Water Resources in Canal Systems Based on the Improved Grey Wolf Algorithm

Xinjiang is located in the arid region of northwestern China, and agriculture accounts for an absolute share of total water use. Resource-based, engineering, structural, and managed water shortages coexist. Therefore, it is of great significance to vigorously develop water conservation technology and improve the efficiency of water transmission and distribution in canal systems. This research aims at addressing the problems of difficult manual regulation and the overall optimization of the final canal system, low-water-resource utilization efficiency, and management efficiency. Taking the branch-double two-stage canal system of Dongfeng branch canal in Mangxiang, Jinghe irrigation district, as a case study, and the rotation irrigation group and irrigation duration as decision variables, canal distribution is modeled with the goal of minimizing seepage losses. The improved grey wolf algorithm combined with particle swarm optimization is used for the first time and compared with the traditional grey wolf algorithm, genetic particle swarm optimization fusion algorithm, and northern goshawk algorithm. The results show that (1) on the basis of meeting the water discharge capacity and water demand requirements of the canal system, the diversion time of the water distribution scheme obtained by using the improved grey wolf algorithm is shortened from 11 d to 8.91 d compared with the traditional empirical water distribution scheme. (2) The improved grey wolf algorithm converges to the optimal value within 10 generations compared to the remaining methods, and the total water leakage is reduced from 16.15 × 104 m3 to 11.75 × 104 m3. (3) The number of gate adjustments is reduced, and the canal gates are opened and closed at the same time within each rotational irrigation group. The grey wolf algorithm improved by its combination with particle swarm has stronger optimization ability and convergence, which can better meet the requirements of efficient water resource allocation in irrigation canal systems, as well as a high application value.

Resource allocation in paired users: Optimization‐assisted user grouping for fairness improvement of NOMA

SummaryThe solution of resource allocation is based on NOMA and OMA structures that are suboptimal to satisfy the demanding QoS and higher data rate requirements compared to EE requirements in 5G cellular networks. In this work, the resource allocation problem in the hybrid MC‐NOMA system is solved. The system achieves the trade‐off between spectral and energy efficiency (EE) with the lowest rate of user requirements. The system's resource allocation, including power allocation, is considered the crucial factor and is identified as a single‐objective problem. The suggested work focuses on user pairing and allocation on paired users following processing. The user grouping is designed to connect near‐ and far‐users with high C3 to increase the fairness of NOMA. As a result, this work considers multiple goals, such as C3 maximization, spectrum efficiency, and energy efficiency. This paper aims to present a novel hybrid optimization model with the combination of coati and bald eagle optimization algorithms, to solve the defined optimization problem. The experimental results show that the suggested system outperforms the conventional algorithms by evaluating different performance measures such as channel correlation, spectrum efficiency, energy efficiency, and power allocation.

Dynamic resource allocation for energy-efficient downlink NOMA systems in 5G networks

Non-Orthogonal Multiple Access (NOMA) is a promising energy-efficient technology designed to satisfy the demands of future networks by efficiently sharing radio resources. In NOMA, the same radio resource is simultaneously assigned to two users at different power levels based on the NOMA-power domain. Resource allocation in NOMA presents a non-convex challenge, characterized as a non-deterministic polynomial (NP-hard) problem. This involves user and channel assignment and power allocation, making it an extraordinarily complex task to achieve an optimal solution. In this work, Simulated Annealing (SA) is proposed as an optimization technique for resource allocation in an energy-efficient downlink NOMA system. This resource allocation scheme addresses user and channel assignment, as well as power allocation, using SA as an efficient standalone approach to maximize energy efficiency in NOMA. SA is utilized to execute the assignment of users to channels, distribute the necessary power for each channel, and determine the power ratio among users sharing the same channel. The results obtained demonstrate a significant improvement in energy efficiency, outperforming the existing numerical methods by 22 %. The proposed SA scheme not only achieves a close optimal solution but also in less computational time, offering sufficient reliability in terms of energy efficiency enhancement when compared to the existing numerical method.

Multi-objective RIME algorithm-based techno economic analysis for security constraints load dispatch and power flow including uncertainties model of hybrid power systems

In recent times, the landscape of power systems has undergone significant evolution, particularly with the integration of diverse renewable energy sources (RESs). This advancement presents an invaluable opportunity to enhance energy efficiency in the modern power grid, primarily by bolstering the role of stochastic RESs. The challenge lies in the optimal power flow (OPF), a multifaceted and non-linear optimization challenge that grows more complex with the inclusion of stochastic RESs that aims to optimize the allocation of power system resources to minimize the operational cost while maintaining the stability and security of the system. Addressing this, the current study introduces an innovative optimization approach, the Multi-Objective RIME (MORIME) algorithm. Drawing inspiration from the physical phenomenon of rime-ice, called the RIME, the MORIME seeks to effectively tackle OPF issues. This algorithm enhances solution accuracy by smartly dividing with non-dominated sorting and crowding distance mechanism. The proposed OPF model incorporates three types of RESs: solar photovoltaic, wind and small-scale hydropower units. While uncertainties in wind speed and solar irradiation are managed through Monte Carlo simulations, the small hydro unit is considered a constant power source. The efficacy of the MORIME algorithm is tested on IEEE 30 bus systems and results indicate that the MORIME method identifies the optimal solution for the multi-objective OPF problem while satisfying the power system constraints, thereby proving its effectiveness and superiority over MOWOA, MOGWO, MOALO, MOMRFO and MOAGDE in terms of Hyper Volume (HV) and reciprocal of Pareto Sets Proximity (1/PSP) metrices. The MORIME source code is available at: https://github.com/kanak02/MORIME

Spectrum-efficient user grouping and resource allocation based on deep reinforcement learning for mmWave massive MIMO-NOMA systems

Millimeter-wave (mmWave) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is proven to be a primary technique for sixth-generation (6G) wireless communication networks. However, the great increase in users and antennas brings challenges for interference suppression and resource allocation for mmWave massive MIMO-NOMA systems. This study proposes a spectrum-efficient and fast convergence deep reinforcement learning (DRL)-based resource allocation framework to optimize user grouping and allocation of subchannel and power. First, an enhanced K-means grouping algorithm is proposed to reduce the multi-user interference and accelerate the convergence. Then, a dueling deep Q-network (DQN) structure is proposed to perform subchannel allocation, which further improves the convergence speed. Moreover, a deep deterministic policy gradient (DDPG)-based power resource allocation algorithm is designed to avoid the performance loss caused by power quantization and improve the system’s achievable sum-rate. The simulation results demonstrate that our proposed scheme outperforms other neural network-based algorithms in terms of convergence performance, and can achieve higher system capacity compared with the greedy algorithm, the random algorithm, the RNN algorithm, and the DoubleDQN algorithm.

Dynamic Neural Network-Based Resource Allocation Framework for Latency-Aware and Energy-Efficient Optimization in Wireless Communication Systems

In wireless communication, efficiency is a crucial factor due to the inherent constraints of the radio frequency spectrum and the escalating demand for connectivity. With a limited spectrum available, efficient communication protocols are vital to maximize its utilization and accommodate the increasing number of devices. This study introduces a dynamic resource allocation system for wireless communication, encompassing essential components such as Base Stations (BS), Mobile Devices (MD), and a Centralized Controller (CC). BSs, serving as access points, establish wireless communication with MD within predetermined coverage areas. The CC acts as the neural centre of the system, collecting real-time data from both BSs and MDs to facilitate an adaptive resource allocation process. The comprehensive mechanism involves a scheduling algorithm for optimized data transmission, considering priority and Quality of Service (QoS) requirements, alongside power control mechanisms that dynamically adjust power levels to optimize energy efficiency while maintaining communication quality. MDs communicate with nearby BSs, relaying data transmission information to the CC, which strategically coordinates resources based on the collected data. This integrated system enhances resource utilization, minimizes latency, and improves energy efficiency, collectively contributing to the robustness and reliability of the wireless communication infrastructure.

Joint mode selection and resource allocation for cellular V2X communication using distributed deep reinforcement learning under 5G and beyond networks

Vehicle-to-everything (V2X) communication via cellular networks is a promising technique for 5G and beyond networks. The cars interact directly with one another, as well as with the infrastructure and various vehicles on the road, in this mode. It enables the interchange of time-sensitive and safety-critical data. Despite these benefits, unstable vehicle-to-vehicle (V2V) communications, insufficient channel status information, high transmission overhead, and the considerable communication cost of centralized resource allocation systems all pose challenges for defense applications. To address these difficulties, this study proposes a combined mode selection and resource allocation system based on distributed deep reinforcement learning (DRL) to optimize the overall network sum rate while maintaining the reliability and latency requirements of V2V pairs and the data rate of V2R connections. Because the optimization issue is non-convex and NP-hard, it cannot be solved directly. To tackle this problem, the defined problem is first translated into machine learning form using the Markov decision process (MDP) to construct the reward function and decide whether agent would conduct the action. Following that, the distributed coordinated duelling deep Q-network (DDQN) method based on prioritized sampling is employed to improve mode selection and resource allocation. This approach learns the action-value distribution by estimating both the state-value and action advantage functions using duelling deep networks. The results of the simulation show that the suggested scheme outperforms state-of-the-art decentralized systems in terms of sum rate and QoS satisfaction probability.

Joint optimization for computation offloading and 3C resource allocations over wireless-powered and NOMA-enabled multi-access MEC

Multi-access mobile edge computing (MEC) enables mobile users (MUs) to offload tasks to proximal multiple MEC servers for fast task processing. Since MUs generally have stringent delay requirements and limited energy and MEC servers have finite communication, computation, and caching (3C) resources, the joint co-design and optimization over computation offloading and 3C resource allocation for wireless-powered multi-access MEC systems have been highly demanded, while having not been well studied yet. We consider a wireless-powered multi-access MEC, where multiple energy harvesting (EH) based MUs and multiple base stations (BSs) each equipped with an MEC server coexist. Each MU first harvests energy from nearby MEC servers, and then offloads its sub-tasks to multiple MEC servers concurrently based on non-orthogonal multiple access (NOMA), so as to reduce data offloading delay. We first formulate a delay minimization problem, by jointly optimizing MUs’ computation offloading, MEC servers’ computation and caching resources allocation, and system communication resources allocation. Then we propose an alternating direction multiplier method (ADMM) based distributed scheme to decompose the formulated optimization problem into several sub-problems, and use the block coordinate descending (BCD) method and the successive convex approximation (SCA) method to transform all sub-problems each corresponding to one MEC server to convex subproblems. Finally, we validate and evaluate our proposed scheme through numerical analyses, which show that our proposed distributed scheme can greatly reduce the min–max delay of all MUs by allowing each MU’s concurrent data offloading to multiple MEC servers using NOMA and by jointly optimizing computation offloading and 3C resource allocations. Also, the delay imposed by our proposed scheme is much smaller than that imposed by the Interior Point method, which shows the effectiveness of our proposed scheme.

Generative Design of the Architecture Platform in Multiprocessor System Design

When designing a system at the Electronic System Level (ESL), designers are confronted with a very large number of design decisions, each affecting the characteristics of the resulting system. Simultaneously, the demands for the system’s performance, reliability, and energy consumption have increased drastically. Design Space Exploration (DSE) aims to facilitate this complex task by automating the system synthesis and traversing the design space autonomously. Previous studies on DSE have mainly considered fixed architectures with a fixed set of hardware components only. In the paper at hand, we overcome this limitation to allow for a higher degree of freedom in the design of a multiprocessor system. Instead of a fixed architecture as input, we are using a resource library containing resource types whose instances can then be arbitrarily placed and connected. More specifically, we enable the exploration of the types, the number, and the positions of required processing-type instances in a grid-based topology template in addition to deciding on the remaining system synthesis tasks, namely, resource allocation, task binding, routing, and scheduling. We provide an extensible framework, based on Answer Set Programming (ASP) modulo Theories (ASPmT), for generating system architectures fulfilling predefined constraints. Our studies show that this higher degree of freedom, originating from fewer restrictions regarding the architecture, leads to an increased complexity of the problem. In extensive experiments, we show scalability trends for a set of parameters, demonstrating the capabilities and limits of our approach.

Adaptive resource allocation in NOMA-enabled backscatter communications systems

The integration of NOMA with Backscatter communication (BackCom) is a promising solution for developing a green future wireless network. However, system performance degrades with the deployment of multiple backscatter devices (BDs) in a network. Hence, energy efficiency (EE) maximization with proper resource allocation is among the primary concerns. In this regard, this paper proposes an adaptive resource allocation method for maximizing EE by simultaneously optimizing the transmission power from the base station (BS), power allocation coefficients, and reflection coefficients under the constraints of maximum allowable transmission power and minimum achievable data rate. Specifically, an iterative method based on a parametric transformation approach is adopted for maximizing EE by jointly optimizing the coefficients, in which the power allocation problem to the BDs is solved by an adaptive method that is based on improved proportionate normalized least mean square (IPNLMS) algorithm. Then, the system performance is evaluated, and the impact of different parameters is also studied it is observed that EE is significantly improved as compared to the existing scheme, and maximum at <span>η</span>=-0.5.

Joint Resource Allocation and Reflecting Precoding Design for RIS-Assisted ISAC Systems

Joint Resource Allocation and Reflecting Precoding Design for RIS-Assisted ISAC Systems

Navigating sex and sex roles: deciphering sex-biased gene expression in a species with sex-role reversal (Syngnathus typhle).

Sexual dimorphism, the divergence in morphological traits between males and females of the same species, is often accompanied by sex-biased gene expression. However, the majority of research has focused on species with conventional sex roles, where females have the highest energy burden with both egg production and parental care, neglecting the diversity of reproductive roles found in nature. We investigated sex-biased gene expression in Syngnathus typhle, a sex-role reversed species with male pregnancy, allowing us to separate two female traits: egg production and parental care. Using RNA sequencing, we examined gene expression across organs (brain, head kidney and gonads) at various life stages, encompassing differences in age, sex and reproductive status. While some gene groups were more strongly associated with sex roles, such as stress resistance and immune defence, others were driven by biological sex, such as energy and lipid storage regulation in an organ- and age-specific manner. By investigating how genes regulate and are regulated by changing reproductive roles and resource allocation in a model system with an unconventional life-history strategy, we aim to better understand the importance of sex and sex role in regulating gene expression patterns, broadening the scope of this discussion to encompass a wide range of organisms.

Joint User Association, Resource Allocation, and Beamforming in RIS-Assisted Multi-Server MEC Systems

Joint User Association, Resource Allocation, and Beamforming in RIS-Assisted Multi-Server MEC Systems

Mental Health Prediction Using Catboost Algorithm

Abstract: This study investigates the application of the CatBoost algorithm in predicting mental health outcomes using Python programming language. Mental health prediction is a critical area of research due to its significant impact on individuals and society. Traditional predictive modeling techniques often encounter challenges in handling complex and highdimensional data inherent in mental health datasets. CatBoost , a state- of-the-art gradient boosting algorithm, has shown promise in effectively addressing these challenges by handling categorical variables seamlessly and exhibiting robust performance in various domains. Leveraging its powerful capabilities, this study aims to develop predictive models for mental health outcomes utilizing a comprehensive dataset encompassing diverse socio- demographic, behavioural , and clinical factors. The predictive performance of the CatBoost algorithm will be evaluated and compared against other commonly used machine learning algorithms, demonstrating its effectiveness in accurately predicting mental health outcomes. This research contributes to the advancement of predictive modeling in mental health research and holds potential implications for personalized interventions and resource allocation in mental healthcare systems

Resource allocation in healthcare entrepreneurial ecosystems: the strategic role of entrepreneurial support organizations

PurposeInnovation management in the healthcare sector has undergone significant evolutions over the last decades. These evolutions have been investigated from a variety of perspectives: clusters, ecosystems of innovation, digital ecosystems and regional ecosystems, but the dynamics of networks have seldom been analyzed under the lenses of entrepreneurial ecosystems (EEs). As identified by Cao and Shi (2020), the literature is silent about the organization of resource allocation systems for network orchestration in EEs. This article investigates these elements in the healthcare sector. It discusses the strategic role played by entrepreneurial support organizations (ESOs) in resource allocation and elaborates on the distinction between sponsored and nonsponsored ESOs in EEs. ESOs are active in network orchestration. The literature explains that ESOs lift organizational, institutional and cultural barriers, and support entrepreneurs' access to cognitive and technological resources. However, allocation models are not yet discussed. Therefore, our research questions are as follows: What is the resource allocation model in healthcare-related EEs? What is the role played by sponsored and nonsponsored ESOs as regards resource allocation to support the emergence and development of EEs in the healthcare sector?Design/methodology/approachThe article offers an explanatory, exploratory, and theory-building investigation. The research design offers an abductive research protocol and multi-level analysis of seven (sponsored and nonsponsored) ESOs active in French healthcare ecosystems. Field research elaborates on semi-structured interviews collected between 2016 and 2022.FindingsThis article shows explicit complementarities between top-down and bottom-up resource allocation approaches supported by ESOs in the healthcare sector. Despite explicit originalities in each approach, no network orchestration model prevails. Multi-polar coordination is the rule. Entrepreneurs' access to critical technological and cognitive resources is based on resource allocation modalities that differ for sponsored versus nonsponsored ESOs. Emerging from field research, this research also shows that sponsored and nonsponsored ESOs manage their roles in different ways because they confront original issues about organizational legitimacy.Originality/valueBeyond the results listed above, the main originalities of the paper relate to the instantiation of multi-level analysis operated during field research and to the confrontation between sponsored versus nonsponsored ESOs in the domain of healthcare-related innovation management. This research shows that ESOs have practical relevance because they build original routes for resource allocation and network orchestration in EEs. Each ESO category (sponsored versus nonsponsored) provides original support for resource allocation. The ESO's legitimacy is inferred either from the sponsor or the services delivered to end-users. This research leads to propositions for future research and recommendations for practitioners: ESO managers, entrepreneurs, and policymakers.

Dynamic task scheduling in edge cloud systems using deep recurrent neural networks and environment learning approaches

The rapid growth of the cloud computing landscape has created significant challenges in managing the escalating volume of data and diverse resources within the cloud environment, catering to a broad spectrum of users ranging from individuals to large corporations. Ineffectual resource allocation in cloud systems poses a threat to overall performance, necessitating the equitable distribution of resources among stakeholders to ensure profitability and customer satisfaction. This paper addresses the critical issue of resource management in cloud computing through the introduction of a Dynamic Task Scheduling with Virtual Machine allocation (DTS-VM) strategy, incorporating Edge-Cloud computing for the Internet of Things (IoT). The proposed approach begins by employing a Recurrent Neural Network (RNN) algorithm to classify user tasks into Low Priority, Mid Priority, and High Priority categories. Tasks are then assigned to Edge nodes based on their priority, optimizing efficiency through the application of the Spotted Hyena Optimization (SHO) algorithm for selecting the most suitable edge node. To address potential overloads on the edge, a Fuzzy approach evaluates offloading decisions using multiple metrics. Finally, optimal Virtual Machine allocation is achieved through the application of the Stable Matching algorithm. The seamless integration of these components ensures a dynamic and efficient allocation of resources, preventing the prolonged withholding of customer requests due to the absence of essential resources. The proposed system aims to enhance overall cloud system performance and user satisfaction while maintaining organizational profitability. The effectiveness of the DTS-VM strategy is validated through comprehensive testing and evaluation, showcasing its potential to address the challenges posed by the diverse and expanding cloud computing landscape.

Optimizing the resource allocation in cyber physical energy systems based on cloud storage and IoT infrastructure

Given the prohibited operating zones, losses, and valve point effects in power systems, energy optimization analysis in such systems includes numerous non-convex and non-smooth parameters, such as economic dispatch problems. In addition, in this paper, to include all possible scenarios in economic dispatch problems, multi-fuel generators, and transmission losses are considered. However, these features make economic dispatch problems more complex from a non-convexity standpoint. In order to solve economic dispatch problems as an important consideration in power systems, this paper presents a modified robust, and effective optimization algorithm. Here, some modifications are carried out to tackle such a sophisticated problem and find the best solution, considering multiple fuels, valve point effect, large-scale systems, prohibited operating zones, and transmission losses. Moreover, a few complicated power systems including 6, 13, and 40 generators which are fed by one type of fuel, 10 generators with multiple fuels, and two large-scale cases comprised of 80 and 120 generators are analyzed by the proposed optimization algorithm. The effectiveness of the proposed method, in terms of accuracy, robustness, and convergence speed is evaluated, as well. Furthermore, this paper explores the integration of cloud storage and internet of things (IoT) to augment the adaptability of monitoring capabilities of the proposed method in handling non-convex energy resource management and allocation problems across various generator quantities and constraints. The results show the capability of the proposed algorithm for solving non-convex energy resource management and allocation problems irrespective of the number of generators and constraints. Based on the obtained results, the proposed method provides good results for both small and large systems. The proposed method, for example, always yields the best results for the system of 6 power plants with and without losses, which are $15,276.894 and $15,443.7967. Moreover, the improvements made in the proposed method have allowed the economic dispatch problem regarding multi-fuel power plants to be solved not only with optimal results ($623.83) but also in less than 35 iterations. Lastly, the difference between the best-obtained results ($121,412) and the worst-obtained results ($121,316.1992) for the system of 40 power plants is only about $4 which is quite acceptable.