Optimal Resource Allocation Model Research Articles

Economic efficient use of soilless techniques to maximize benefits for farmers

<abstract> <p>The main challenge of the agricultural sector is the increasing pressure on natural resources, mainly water and arable land. Consequently, an urgent imperative arises to explore technological advancements that can boost food production in alignment with the growing demands. The Soilless Production System (SPS) emerges as a proficient approach for managing irrigation water, thereby making a significant contribution to food security. This research focuses on the efficient use of SPS and identifies the best economic use of the soilless techniques for different crops within an area characterized by limited land and water availability. The database for the study was generated through a farm survey to investigate the benefits of adopting SPSs as a sustainable agricultural practice. A linear programming approach was applied to develop an optimization model for resource allocation and crop mix selection, considering the development opportunities through the SPSs. Different scenarios were applied in the model. The results proved that adopting SPSs is a sustainable irrigation practice, since the technique promotes water use efficiency, generates profitability, and conserves the associated natural resources. The SPSs ensure sustainable use of water resources by increasing water use efficiency. The hydroponics cultivation system had 11 ± 1.7 times higher yields but required 82 ± 11 times more energy in comparison to the lettuce crop produced by the conventional production system (CPS). The result of the optimal solution shows that the total revenue of scenarios of the study is 109% of the revenue of the original value of the largest farmer. The water resources and the investment cost as constraints in the model are totally used, but the operational costs in the optimal solutions are 74% of the total operational cost in the original data. The optimal solution showed the importance of using computerized systems in which the control of the fertigation is better.</p> </abstract>

Decision Optimization for Water and Electricity Shared Resources Based on Fusion Swarm Intelligence

As one of the most important water conservancy projects, reservoirs use water resources to achieve essential functions, such as irrigation, flood control, and power generation, by intercepting rivers. As climate extremes and global warming increase, the world’s water reserves are being tested, and reservoir operators are being challenged. This paper investigates the optimal allocation of shared resources for hydropower to achieve rational decisions for reservoir operations. Firstly, a power resource model is constructed based on the real hydroelectric generator theory. Furthermore, based on the established power resource model combined with the influence of weather type and multi-region heterogeneous demand, this paper constructs a multi-objective hydropower shared resource allocation optimization model, with the lowest hydropower resource supply cost and the shortest time hydropower resource supply time as the optimization objectives. Secondly, for the problem that the traditional population intelligence algorithm easily falls into the local optimum when solving complex problems, the improvement of the MOPSO algorithm is completed by introducing the Levy flight strategy and differential evolution. Finally, simulation experiments were carried out, and cutting-edge algorithms, such as the GA algorithm and WOA algorithm, were selected for simulation comparison to verify the effectiveness of the constructed model and algorithm. The simulation results show that the research in this paper can contribute to effective decision-making for reservoir operators and promote intelligent reservoir operation.

Higher education resource optimal allocation and relationship with the biopharma and biotech industry

Current educational resource allocation methods achieve resource allocation through a simple linear constraint approach to a single resource restriction, and the single resource allocation restriction causes the allocation results to easily fall into a local optimal solution, which not only has low allocation efficiency but also cannot allocate all the resources effectively, leading to the waste of educational resources of a certain category or several categories in pharma industries. To address the above problems, the algorithm of optimal allocation of higher education resources based on the relationship with the biopharma and biotech industry is proposed. In view of the above problems, an algorithm for optimizing the allocation of higher education resources biopharma and biotech industry is proposed. By establishing a multidimensional analysis model of educational resource data, the resource data are analyzed and processed to determine the factors affecting resource allocation. Combined with the actual establishment of the resource allocation optimization model, NSGA-II is used to solve the problem, and the optimal allocation scheme is obtained. The application of the proposed configuration algorithm to reduce the standard deviation of the resource discrepancy coefficient to 0.134 is demonstrated through simulation experiments, which can effectively reduce the discrepancy between resources.

Reliability and Resource Allocation and Recovery of Urban Transportation System Considering the Virus Transmission

The urban transport system is an integral part of a city and is essential for the proper functioning of other urban functional systems. To improve the resilience of urban transport systems under the background of the spreading COVID-19 epidemic, this paper predicts the number of patients of various types at each stage of epidemic development based on an improved infectious disease model for Wuhan and verifies the validity of the model using statistical methods. Then, a system reliability model is developed from the perspective of controlling the spread of the virus and reducing economic losses, and the optimal time points for urban traffic closure and recovery are determined. Finally, a resource allocation optimization model was developed to determine the number and location of resource allocation points which based on 19 hospitals to avoid the further spread of the virus. The results give a valuable reference for enhancing the resilience of urban transport systems and improving their performance in all phases.

Optimization of Operating Hydrogen Storage System for Coal–Wind–Solar Power Generation

To address the severity of the wind and light abandonment problem and the economics of hydrogen energy production and operation, this paper explores the problem of multi-cycle resource allocation optimization of hydrogen storage systems for coal–wind–solar power generation. In view of the seriousness of the problem of abandoning wind and photovoltaic power and the economy of hydrogen production and operation, the node selection and scale setting issues for hydrogen production and storage, as well as decision-making problems such as the capacity of new transmission lines and new pipelines and route planning, are studied. This research takes the satisfaction of energy supply as the basic constraint and constructs a multi-cycle resource allocation optimization model for an integrated energy system, aiming to achieve the maximum benefit of the whole system. Using data from Inner Mongolia, where wind abandonment and power limitation are severe, and Beijing and Shanxi provinces, where hydrogen demand is high, this paper analyzes the benefits of the hydrogen storage system for coal–wind–solar power generation, and explores the impact of national subsidy policies and technological advances on system economics.

HERA: High-Efficiency Resource Allocation Scheme for Newly Joined Mobile Terminal in Heterogeneous Wireless Network

Objectives:To design an efficient resource allocation design for provisioning service of varied traffic classes to Multi-Mode Mobile Terminals (MMTs) in Heterogeneous Wireless Networks (HWNs). In HWNs, users prerequisite certain Quality of Service (QoS); thus, for meeting QoS, theMMTs are handoff to the new network. However, handoff MMTs might induce interference with the ongoing communication of existing MMTs. As resources are shared, resource allocation becomes a challenging task. Methods:This study presents a High-Efficiency Resource Allocation optimization model for HWNs. The HERA scheme first employs Channel State Information (CSI) estimation for mitigating interference and establishing channel availability. Second, using game theory optimal resource is allocated to MMTs and proves the existence of Nash equilibrium (NE) for spectrum resource allocation to MMTs in HWNs. Finally, a hybrid resource allocation algorithm combining contentionless and contention-based is designed to provide an improved resource allocation mechanism. Findings:The experiment outcome shows the HERA scheme allocates resources more efficiently in comparison with Access Fairness Resource Allocation (AFRA)(1), Joint Resource Allocation with Power Optimization (JRA-PO)(2), Resource Allocation and Node Placement (RANP)(3), and Existing Resource Allocation (ERA)(4). The HERA improves throughput by 19.07% and reduces collision by 42.96% in comparison with ERA(4) . Novelty: existing model predominantly focused on either addressing interference or maximizing throughput by leveraging either contention or contention-less resource allocation mechanism. However, in this study both contention-less and contention-based mechanisms are merged to maximize throughput and reduce collision. Keywords: Interference; Quality of service; Resource allocation; Softcomputing; Spectrum utilization; Heterogeneous communication network

Fog-Edge Collaborative Task Offloading Strategy Based on Chaotic Teaching and Learning Particle Swarm Optimization.

To improve the contradiction between the surge of business demand and the limited resources of MEC, firstly, the “cloud, fog, edge, and end” collaborative architecture is constructed with the scenario of smart campus, and the optimization model of joint computation offloading and resource allocation is proposed with the objective of minimizing the weighted sum of delay and energy consumption. Second, to improve the convergence of the algorithm and the ability to jump out of the bureau of excellence, chaos theory and adaptive mechanism are introduced, and the update method of teaching and learning optimization (TLBO) algorithm is integrated, and the chaos teaching particle swarm optimization (CTLPSO) algorithm is proposed, and its advantages are verified by comparing with existing improved algorithms. Finally, the offloading success rate advantage is significant when the number of tasks in the model exceeds 50, the system optimization effect is significant when the number of tasks exceeds 60, the model iterates about 100 times to converge to the optimal solution, the proposed architecture can effectively alleviate the problem of limited MEC resources, the proposed algorithm has obvious advantages in convergence, stability, and complexity, and the optimization strategy can improve the offloading success rate and reduce the total system overhead.

Joint User Scheduling and Resource Allocation in Distributed MIMO Systems with Multi-Carriers

Compared with the traditional collocated multi-input multi-output system (C-MIMO), distributed MIMO (D-MIMO) systems have the advantage of higher throughput and coverage, making them strong candidates for next-generation communication architecture. As a practical implementation of a D-MIMO cooperative network, the multi-TRP (multiple transmission/reception point) system becomes a hotspot in the research of advanced 5G. Different from previous research on a cooperative D-MIMO network with single narrowband transmission, this paper proposes a joint optimization scheme to address the user scheduling problem along with carrier allocation to maximize the total spectral efficiency (SE) in the downlink of coherent multi-TRP systems with multi-carriers. We establish a joint optimization model of user scheduling and resource allocation to maximize the system spectral efficiency under the constraints of power consumption and the backhaul capacity limits at each RAU (remote antenna unit), as well as the QoS (quality of service) requirement at each user. Since the optimization model is both non-covex and non-smooth, a joint optimization algorithm is proposed to solve this non-convex combinatorial optimization problem. We first smooth the mixed-integer problem by employing penalty functions, and after decoupling the coupled variables by introducing auxiliary variables, the original problem is transformed into a series of tractable convex optimization problems by using successive convex approximation (SCA). Numerical results demonstrate that the proposed joint optimization algorithm for user scheduling and resource allocation can reliably converge and achieve a higher system SE than the general multi-TRP system without carrier allocation, and this advantage is more pronounced under a higher backhaul capacity or higher power consumption constraints.

A combinatorial optimization model for post-disaster emergency resource allocation using meta-heuristics

Disasters are large-scale disruptions for the society that cause damage to human lives, infrastructure, and the ecosystem. Multiple emergencies emerge, which need to be handled and resolved efficiently to minimize the impact of the disaster. Therefore, the need for an effective and efficient emergency response system that optimally allocates resources and emergency services is essential. This paper proposes a methodology to model this situation as an optimization problem that can be solved using meta-heuristic algorithms. In the proposed model, four meta-heuristics, namely Particle Swarm Optimization, Cuckoo Search, Grey Wolf Optimizer, and iLSHADE-RSP algorithm, have been used to allocate resources and services. A benchmark dataset consisting of 16 situations is prepared to analyze the proposed model. The conducted empirical analysis demonstrates the applicability of the meta-heuristic algorithms in locating near-optimum solutions for the considered situations. The convergence analysis and statistical tests have been performed to test the validity and significance of the conducted experiments.

Enterprise Human Resource Allocation Optimization Model Based on Improved Particle Swarm Optimization Algorithm

With the development of the market economy, the competition for talents among enterprises has attracted more and more attention. Therefore, in recent years, enterprises have paid more and more attention to the optimal allocation of human resources. Aiming at the problem of human resources allocation in the process of enterprise management, this research establishes a corresponding mathematical model and solves the optimal allocation model of human resources based on particle swarm optimization algorithm. The calculation amount of the model is obviously reduced in the application process, the optimal solution is more ideal, and good results are achieved. However, the genetic algorithm itself has certain limitations, and the selection of initial chromosomes and the design of genetic operators put forward higher requirements. Starting from operability, based on system analysis and quantitative evaluation method, this paper establishes a model of human resources optimal allocation, provides a quantitative management method for optimal allocation of human resources, and designs an improved particle swarm optimization algorithm to solve this problem. The numerical simulation results show the effectiveness of the algorithm.

Optimization Model for Primary and Backup Resource Allocation With Workload-Dependent Failure Probability

Optimization Model for Primary and Backup Resource Allocation With Workload-Dependent Failure Probability

A novel coevolving differential evolution and its application in intelligent device-to-device communication systems

Due to the increasingly demand of wireless broadband applications in modern society, the device-to-device (D2D) communication technique plays an important role for improving communication spectrum efficiency and quality of service (QoS). This study focuses on the optimal allocation of link resource in D2D communication systems using intelligent approaches, in order to obtain optimal energy efficiency of D2D-pair users (DP) and also ensure communication QoS. To be specific, the optimal resource allocation (ORA) model for ensuring the cooperation between DP and cellular users (CU) is established, and a novel coding strategy of ORA model is also proposed. Then, for efficiently optimizing the ORA model, a novel swarm-intelligence-based algorithm called the dynamic topology coevolving differential evolution (DTC-DE) is developed, and the efficiency of DTC-DE is also tested by a comprehensive set of benchmark functions. Finally, the DTC-DE algorithm is employed for optimizing the proposed ORA model, and some state-of-the-art algorithms are also employed for comparison. Result of case study shows that the DTC-DE outperforms its competitors significantly, and the optimal resource allocation can be obtained by DTC-DE with robust performance.

A study on resource reallocation in regional energy networks based on the adjustment of supply and demand nodes

In this paper, we propose and analyse a regional energy network resource allocation scheme and optimization model for regions where the number of energy supply nodes grows seasonally, including the optimal division of the energy supply range of each energy supply node based on distance, the optimal path planning scheme of the energy pipeline network connecting energy supply and consumption nodes, and the optimal allocation/reallocation model of regional energy network resources with the objective of optimal operational economy. The proposed scheme for the optimal redistribution of regional energy network resources is used to analyse and study the case area, and a scheme for the division of energy supply nodes in the case area, a scheme for the construction path of energy pipeline network and a planning strategy for the optimal redistribution of energy network resources are developed to verify the feasibility of the method in this paper.

Growth and feeding ecology of coniform conodonts.

Conodonts were the first vertebrates to develop mineralized dental tools, known as elements. Recent research suggests that conodonts were macrophagous predators and/or scavengers but we do not know how this feeding habit emerged in the earliest coniform conodonts, since most studies focus on the derived, ‘complex’ conodonts. Previous modelling of element position and mechanical properties indicate they were capable of food processing. A direct test would be provided through evidence of in vivo element crown tissue damage or through in vivo incorporated chemical proxies for a shift in their trophic position during ontogeny. Here we focus on coniform elements from two conodont taxa, the phylogenetically primitive Proconodontus muelleri Miller, 1969 from the late Cambrian and the more derived Panderodus equicostatus Rhodes, 1954 from the Silurian. Proposing that this extremely small sample is, however, representative for these taxa, we aim to describe in detail the growth of an element from each of these taxa in order to the test the following hypotheses: (1) Panderodus and Proconodontus processed hard food, which led to damage of their elements consistent with prey capture function; and (2) both genera shifted towards higher trophic levels during ontogeny. We employed backscatter electron (BSE) imaging, energy-dispersive X-ray spectroscopy (EDX) and synchrotron radiation X-ray tomographic microscopy (SRXTM) to identify growth increments, wear and damage surfaces, and the Sr/Ca ratio in bioapatite as a proxy for the trophic position. Using these data, we can identify whether they exhibit determinate or indeterminate growth and whether both species followed linear or allometric growth dynamics. Growth increments (27 in Pa. equicostatus and 58 in Pr. muelleri) were formed in bundles of 4–7 increments in Pa. equicostatus and 7–9 in Pr. muelleri. We interpret the bundles as analogous to Retzius periodicity in vertebrate teeth. Based on applied optimal resource allocation models, internal periodicity might explain indeterminate growth in both species. They also allow us to interpret the almost linear growth of both individuals as an indicator that there was no size-dependent increase in mortality in the ecosystems where they lived e.g., as would be the case in the presence of larger predators. Our findings show that periodic growth was present in early conodonts and preceded tissue repair in response to wear and damage. We found no microwear and the Sr/Ca ratio, and therefore the trophic position, did not change substantially during the lifetimes of either individual. Trophic ecology of coniform conodonts differed from the predatory and/or scavenger lifestyle documented for “complex” conodonts. We propose that conodonts adapted their life histories to top-down controlled ecosystems during the Nekton Revolution.

Metaheuristic based auto-scaling for microservices in cloud environment: a new container-aware application scheduling

PurposeThe cloud is a network of servers to share computing resources to run applications and data storage that offers services in various flavours, namely, infrastructure as a service, platform as a service and software as a service. The containers in the cloud are defined as “standalone and self-contained units that package software and its dependencies together”. Similar to virtual machines, the virtualization method facilitates the resource on a specific server that could be used by numerous appliances.Design/methodology/approachThis study introduces a new Dragon Levy updated squirrel algorithm (DLU-SA) for container aware application scheduling. Furthermore, the solution of optimal resource allocation is attained via defining the objective function that considers certain criteria such as “total network distance (TND), system failure (SF), balanced cluster use (BC) and threshold distance (TD)”. Eventually, the supremacy of the presented model is confirmed over existing models in terms of cost and statistical analysis.FindingsOn observing the outcomes, the total cost of an adopted model for Experimentation 1 has attained a lesser cost value, and it was 0.97%, 10.45% and 10.37% superior to traditional velocity updated grey wolf (VU-GWO), squirrel search algorithm (SSA) and dragonfly algorithm (DA) models, respectively, for mean case scenario. Especially, under best case scenario, the implemented model has revealed a minimal cost value of 761.95, whereas, the compared models such as whale random update assisted lion algorithm, VU-GWO, SSA and DA has revealed higher cost value of 761.98, 779.46, 766.62 and 766.51, respectively. Thus, the enhancement of the developed model has been validated over the existing works.Originality/valueThis paper proposes a new DLU-SA for container aware application scheduling. This is the first work that uses the DLU-SA model for optimal container resource allocation by taking into consideration of certain constraints such as TND, SF, BC and TD.

A multi-objective optimization for resource allocation of emergent demands in cloud computing

Cloud resource demands, especially some unclear and emergent resource demands, are growing rapidly with the development of cloud computing, big data and artificial intelligence. The traditional cloud resource allocation methods do not support the emergent mode in guaranteeing the timeliness and optimization of resource allocation. This paper proposes a resource allocation algorithm for emergent demands in cloud computing. After building the priority of resource allocation and the matching distances of resource performance and resource proportion to respond to emergent resource demands, a multi-objective optimization model of cloud resource allocation is established based on the minimum number of the physical servers used and the minimum matching distances of resource performance and resource proportion. Then, an improved evolutionary algorithm, RAA-PI-NSGAII, is presented to solve the multi-objective optimization model, which not only improves the quality and distribution uniformity of the solution set but also accelerates the solving speed. The experimental results show that our algorithm can not only allocate resources quickly and optimally for emergent demands but also balance the utilization of all kinds of resources.

A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

With the development of big data and artificial intelligence, cloud resource requests present more complex features, such as being sudden, arriving in batches and being diverse, which cause the resource allocation to lag far behind the resource requests and an unbalanced resource utilization that wastes resources. To solve this issue, this paper proposes a proactive resource allocation method based on the adaptive prediction of the resource requests in cloud computing. Specifically, this method first proposes an adaptive prediction method based on the runs test that improves the prediction accuracy of resource requests, and then, it builds a multiobjective resource allocation optimization model, which alleviates the latency of the resource allocation and balances the utilizations of the different types of resources of a physical machine. Furthermore, a multiobjective evolutionary algorithm, the Nondominated Sorting Genetic Algorithm with the Elite Strategy (NSGA-II), is improved to further reduce the resource allocation time by accelerating the solution speed of the multiobjective optimization model. The experimental results show that this method realizes the balanced utilization between the CPU and memory resources and reduces the resource allocation time by at least 43% (10 threads) compared with the Improved Strength Pareto Evolutionary algorithm (SPEA2) and NSGA-II methods.

Energy, network, and application-aware virtual machine placement model in SDN-enabled large scale cloud data centers

Cloud computing has been considered a core model of elastic on-demand resource allocation using a pay-as-you go model. One of the big challenges of this environment is to provide high quality service (QoS) through efficient and stringent management of cloud data center resources. With the increasing demand for cloud based services, the traffic volume inside cloud data centers (DC) has been increased exponentially. Accordingly, and to provide high QoS, a proper scheduling mechanism has to be followed by the cloud service provider. Furthermore, accurate scheduling is necessary for advancing the problem of energy consumption and resource utilization. In this paper, we propose an optimal resource allocation and consolidation virtual machine (VM) placement model for multi-tier applications in modern large cloud DCs. The proposed model targets to optimize the DCs’ energy and communication cost that influence the overall cloud performance through Software Defined Networking (SDN) control features. To solve the formulated multi-objective optimization problem, a novel adaptive genetic algorithm is proposed. The experimental results validate the efficacy of the proposed model through extensive simulations using synthetic and real workload traces. These results show that the proposed model jointly optimizes cloud QoS as well as energy consumption.

Robust Optimization Model for Primary and Backup Resource Allocation in Cloud Providers

This article proposes a primary and backup resource allocation model that provides a probabilistic protection guarantee for virtual machines against multiple failures of physical machines in a cloud provider to minimize the required total capacity. A physical machine allocates both primary and backup computing resources for virtual machines. When any failure occurs, the survived physical machines with preplanned backup resources recover the virtual machines on the failed physical machines and take over the workloads. The probability that the protection provided by a physical machine does not succeed is guaranteed within a given number. Providing the probabilistic protection can reduce the required backup capacity by allowing backup resource sharing, but it leads to a nonlinear programing problem in a general-capacity case against multiple failures. We apply robust optimization with extensive mathematical operations to formulate the primary and backup resource allocation problem as a mixed integer linear programming problem, where capacity fragmentation is suppressed. We prove the NP-hardness of considered problem. A heuristic is introduced to solve the optimization problem. The results reveal that the proposed model saves about one-third of the total capacity in our examined cases; it outperforms the conventional models in terms of both blocking probability and resource utilization.

NAAM‐MOEA/D‐Based Multitarget Firepower Resource Allocation Optimization in Edge Computing

In the edge environment, the multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been widely used in the research of multitarget firepower resource allocation. However, as the MOEA/D algorithm uses a fixed neighborhood update mechanism, it is impossible to rationally allocate computing resources based on the difficulty of each subproblem optimization, which results in some problems such as reduced population evolution efficiency and poor evolution quality during the calculation process. In order to solve these problems, a decision mechanism for subproblems and population evolution stages is designed, and on this basis, a MOEA/D algorithm based on the neighborhood adaptive adjustment mechanism is proposed to adapt to the edge environment. The optimization model of multiobjective firepower resource allocation based on the maximization of damage effect and the minimization of strike cost is constructed and solved. Using the ZDT series of test functions for comparative experiments, the simulation results show that the proposed algorithm can balance the distribution and convergence of population evolution and obtain satisfactory optimization results.